Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis

Vibration signal analysis is an important means for bearing fault diagnosis. Affected by the vibration of other machine parts, external noise and the vibration transmission path, the impulses induced by a bearing defect in the measured vibrations are very weak. Blind deconvolution (BD) methods can counteract the effect of the transmission path and enhance the fault impulses. Most BD methods highlight fault features of the filtered signals by impulse-featured objective functions (OFs). However, residual noise in the filtered signals has not been well tackled. To overcome this problem, a fuzzy entropy-assisted deconvolution (FEAD) method is proposed. First, FEAD takes advantage of the high noise sensitivity of fuzzy entropy (FuzzyEn) and constructs a weighted FuzzyEn-kurtosis OF to enhance the fault impulses while suppressing noise interference. Then, the PSO algorithm is used to iteratively solve the optimal inverse deconvolution filter. Finally, envelope spectrum analysis is performed on the filtered signal to realize bearing fault diagnosis. The feasibility of FEAD was first verified by the bearing fault simulation signals at constant and variable speeds. The bearing test signals from Case Western Reserve University (CWRU), the railway wheelset and the test bench validated the good performance of FEAD in fault feature enhancement. A comparison with and quantitative results for the other state-of-the-art BD methods indicated the superiority of the proposed method.

Simulating whole-body vibration for neonatal patients on a tire-coupled road simulator

Exposure to excessive whole-body vibration is linked to health issues and may result in increased rates of mortality and morbidity in infants. Newborn infants requiring specialized treatment at neonatal intensive care units often require transportation by road ambulance to specialized care centers, exposing the infants to potentially harmful vibration and noise. A standardized Neonatal Patient Transport System (NPTS) has been deployed in Ontario, Canada, that provides life saving equipment to patients and safe operation for the clinical care staff. However, there is evidence that suggests patients may experience a higher amplitude of vibration at certain frequencies when compared with the vehicle vibration. In a multi-year collaborative project, we seek to create a standardized test procedure to evaluate the levels of vibration and the effectiveness of mitigation strategies. Previous studies have looked at laboratory vibration testing of a transport system or transport incubator and were limited to single degree of freedom excitation, neglecting the combined effects of rotational motion. This study considers laboratory testing of a full vehicle and patient transport system on an MTS Model 320 Tire-Coupled Road Simulator. The simulation of road profiles and discrete events on a tire-coupled road simulator allows for the evaluation of the vibration levels of the transport system and the exploration of mitigation strategies in a controlled setting. The tire-coupled simulator can excite six degrees-of-freedom motion of the transport system for vibration evaluation in three orthogonal directions including the contributions of the three rotational degrees of freedom. The vibration data measured on the transport system during the tire-coupled testing are compared to corresponding road test data to assess the accuracy of the vibration environment replication. Three runs of the same drive file were conducted during the laboratory testing, allowing the identification of anomalies and evaluation of the repeatability. The tire-coupled full vehicle testing revealed a high level of accuracy in re-creating the road sections and synthesized random profiles. The simulation of high amplitude discrete events, such as speed hump traverses, were highly repeatable, yet yielded less accurate results with respect to the peak amplitudes at the patient. The resulting accelerations collected at the input to the manikin (sensor located under the mattress) matched well between the real-world and road simulator. The sensors used during testing included series 3741B uni-axial and series 356A01 tri-axial accelerometers by PCB Piezotronics. These results indicate a tire-coupled road simulator can be used to accurately evaluate vibration levels and assess the benefits of future mitigation strategies in a controlled setting with a high level of repeatability.

A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery. Cepstrum Pre-Whitening (CPW) is a technique that can effectively eliminate discrete frequency components in the signal whilst detecting the impulsive features related to the bearing defect(s). Nevertheless, CPW is ineffective for detecting incipient bearing defects with weak signatures. In this study, a novel hybrid method based on an improved CPW (ICPW) and high-pass filtering (ICPW-HPF) is developed that shows improved detection of BCFs under a wide range of conditions when compared with existing BCF detection methods, such as Fast Kurtogram (FK). Combined with machine learning techniques, this novel hybrid method provides the capability for automated bearing defect detection and diagnosis without the need for manual selection of the resonant frequencies. The results from this novel hybrid method are compared with a number of established BCF detection methods, including Fast Kurtogram (FK), on vibration signals collected from the project I2BS (An EU Clean Sky 2 project 'Integrated Intelligent Bearing Systems' collaboration between Schaeffler Technologies and the University of Southampton. Safran Aero Engines was the topic manager for this project) and those from three databases available in the public domain-Case Western Reserve University (CWRU), Intelligent Maintenance Systems (IMS) datasets, and Safran jet engine data-all of which have been widely used in studies of this kind. By calculating the Signal-to-Noise Ratio (SNR) of each case, the new method is shown to be effective for a much lower SNR (with an average of 30.21) compared with that achieved using the FK method (average of 14.4) and thus is much more effective in detecting incipient bearing faults. The results also show that it is effective in detecting a combination of several bearing faults that occur simultaneously under a wide range of bearing configurations and test conditions and without the requirement of further human intervention such as extra screening or manual selection of filters.

Vibration Analysis and Damping Effect of Blade-Hard Coating Composite Structure Based on Base Excitation

Hard coatings are widely employed on blades to enhance impact resistance and mitigate fatigue failure caused by vibration. While previous studies have focused on the dynamic characteristics of beams and plates, research on real blades remains limited. Specifically, there is a lack of investigation into the dynamic characteristics of hard-coated blades under base excitation. In this paper, the finite element model (FEM) of blade-hard coating (BHC) composite structure is established based on finite element methods in which the hard coating (HC) material and the substrate are considered as the isotropic material. Harmonic response analysis is conducted to calculate the resonance amplitude of the composite under base excitation. Numerical simulations and experimental tests are performed to examine the effects of various HC parameters, including energy storage modulus, loss factors, coating thickness, and coating positions, on the dynamic characteristics and vibration reduction of the hard-coated blade composite structures. The results indicate that the difference in natural frequency and modal loss factor of blades increases with higher storage modulus and HC thickness. Moreover, the vibration response of the BHC decreases with higher storage modulus, loss factor, and coating thickness of the HC material. Blades with a complete coating exhibit superior damping effects compared to other coating distributions. These findings are significant for establishing accurate dynamic models of HC composite structures, assessing the effectiveness of HC vibration suppression, and guiding the selection and preparation of HC materials.

Convolutional Neural Network-Based Transformer Fault Diagnosis Using Vibration Signals

Fast and accurate fault diagnosis is crucial to transformer safety and cost-effectiveness. Recently, vibration analysis for transformer fault diagnosis is attracting increasing attention due to its ease of implementation and low cost, while the complex operating environment and loads of transformers also pose challenges. This study proposed a novel deep-learning-enabled method for fault diagnosis of dry-type transformers using vibration signals. An experimental setup is designed to simulate different faults and collect the corresponding vibration signals. To find out the fault information hidden in the vibration signals, the continuous wavelet transform (CWT) is applied for feature extraction, which can convert vibration signals to red-green-blue (RGB) images with the time-frequency relationship. Then, an improved convolutional neural network (CNN) model is proposed to complete the image recognition task of transformer fault diagnosis. Finally, the proposed CNN model is trained and tested with the collected data, and its optimal structure and hyperparameters are determined. The results show that the proposed intelligent diagnosis method achieves an overall accuracy of 99.95%, which is superior to other compared machine learning methods.

A Comparison of Shell Theories for Vibration Analysis of Single-Walled Carbon Nanotubes Based on an Anisotropic Elastic Shell Model

In the present paper, a comparison is conducted between three classical shell theories as applied to the linear vibrations of single-walled carbon nanotubes (SWCNTs); specifically, the evaluation of the natural frequencies is conducted via Donnell, Sanders, and Flügge shell theories. The actual discrete SWCNT is modelled by means of a continuous homogeneous cylindrical shell considering equivalent thickness and surface density. In order to take into account the intrinsic chirality of carbon nanotubes (CNTs), a molecular based anisotropic elastic shell model is considered. Simply supported boundary conditions are imposed and a complex method is applied to solve the equations of motion and to obtain the natural frequencies. Comparisons with the results of molecular dynamics simulations available in literature are performed to check the accuracy of the three different shell theories, where the Flügge shell theory is found to be the most accurate. Then, a parametric analysis evaluating the effect of diameter, aspect ratio, and number of waves along the longitudinal and circumferential directions on the natural frequencies of SWCNTs is performed in the framework of the three different shell theories. Assuming the results of the Flügge shell theory as reference, it is obtained that the Donnell shell theory is not accurate for relatively low longitudinal and circumferential wavenumbers, for relatively low diameters, and for relatively high aspect ratios. On the other hand, it is found that the Sanders shell theory is very accurate for all the considered geometries and wavenumbers, and therefore, it can be correctly adopted instead of the more complex Flügge shell theory for the vibration modelling of SWCNTs.

Structural Health Monitoring (SHM) Study of Polymer Matrix Composite (PMC) Materials Using Nonlinear Vibration Methods Based on Embedded Piezoelectric Transducers

Nowadays, nonlinear vibration methods are increasingly used for the detection of damage mechanisms in polymer matrix composite (PMC) materials, which are anisotropic and heterogeneous. The originality of this study was the use of two nonlinear vibration methods to detect different types of damage within PMC through an in situ embedded polyvinylidene fluoride (PVDF) piezoelectric sensor. The two used methods are nonlinear resonance (NLR) and single frequency excitation (SFE). They were first tested on damage introduced during the manufacturing of the smart PMC plates, and second, on the damage that occurred after the manufacturing. The results show that both techniques are interesting, and probably a combination of them will be the best choice for SHM purposes. During the experimentation, an accelerometer was used, in order to validate the effectiveness of the integrated PVDF sensor.

Detection of periprosthetic fractures around the femoral stem by resonance frequency analysis: An in vitro study

Periprosthetic femoral bone fractures are frequent complications of Total Hip Arthroplasty (THA) and may occur during the insertion of uncemented Femoral Stems (FS), due to the nature of the press-fit fixation. Such fracture may lead to the surgical failure of the THA and require a revision surgery, which may have dramatic consequences. Therefore, an early detection of intra-operative fractures is important to avoid worsening the fracture and/or to enable a peroperative treatment. The aim of this in vitro study is to determine the sensitivity of a method based on resonance frequency analysis of the bone-stem-ancillary system for periprosthetic fractures detection. A periprosthetic fracture was artificially created close to the lesser-trochanter of 10 femoral bone mimicking phantoms. The bone-stem-ancillary resonance frequencies in the range (2-12) kHz were measured on an ancillary instrumented with piezoelectric sensors, which was fixed to the femoral stem. The measurements were repeated for different fracture lengths from 4 to 55 mm. The results show a decrease of the resonance frequencies due to the fracture occurrence and propagation. The frequency shift reached up to 170 Hz. The minimum fracture length that can be detected varies from 3.1±1.7 mm to 5.9±1.9 mm according to the mode and to the specimen. A significantly higher sensitivity (p = 0.011) was obtained for a resonance frequency around 10.6 kHz, corresponding to a mode vibrating in a plane perpendicular to the fracture. This study opens new paths toward the development of non-invasive vibration-based methods for intra-operative periprosthetic fractures detection.

Multirotor Motor Failure Detection with Piezo Sensor

Failure detection of Unmanned Aerial Vehicle (UAV) motors and propulsion systems is the most important step in the implementation of active fault-tolerant control systems. This will increase the reliability of unmanned systems and increase the level of safety, especially in civil and commercial applications. The following paper presents a method of motor failure detection in the multirotor UAV using piezo bars. The results of a real flight, in which the failure of the propulsion system caused the crash of a hybrid VTOL UAV, were presented and analyzed. The conclusions drawn from this flight led to the development of a lightweight, simple and reliable sensor that can detect a failure of the UAV propulsion system. The article presents the outcomes of laboratory tests concerning measurements made with a piezo sensor. An extensive analysis of the obtained results of vibrations recorded on a flying platform arm with a propulsion system is presented, and a methodology for using this type of data to detect failures is proposed. The article presents the possibility of using a piezoelectric sensor to record vibrations on the basis of which it is possible to detect a failure of the UAV propulsion system.

Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation

Analyzing vibration data using deep neural networks is an effective way to detect damages in rotating machinery at an early stage. However, the black-box approach of these methods often does not provide a satisfactory solution because the cause of classifications is not comprehensible to humans. Therefore, this work investigates the application of the explainable AI (XAI) algorithms to convolutional neural networks for vibration-based condition monitoring. Thus, the three XAI algorithms GradCAM, LRP and LIME with a modified perturbation strategy are applied to classifications based on the Fourier transform as well as the order analysis of the vibration signal. The following visualization as frequency-RPM maps and order-RPM maps allows for an effective assessment of saliency values for variable periodicity of the data, which translates to a varying rotation speed of a real-world machine. To compare the explanatory power of the XAI methods, investigations are first carried out with a synthetic data set with known class-specific characteristics. Both a visual and a quantitative analysis of the resulting saliency maps are presented. Then, a real-world data set for vibration-based imbalance classification on an electric motor, which runs at a broad range of rotation speeds, is used. The results indicate that the investigated algorithms are each partially successful in providing sample-specific saliency maps which highlight class-specific features and omit features which are not relevant for classification.

Effect of Multiple Annular Plates on Vibration Characteristics of Laminated Submarine-like Structures

A numerical model for the prediction of vibration behaviors of a laminated submarine structure consisting of spherical, cylindrical, and cone shells with multiple built-in annular plates is reported in this article. With the aid of the first-order shear deformation theory (FSDT) concerning plates and shells, the energy expressions of each substructure are derived. The displacement functions in the energy functionals are expanded by the employment of Legendre orthogonal polynomials and circumferential Fourier series. Then, the Rayleigh-Ritz procedure is performed to obtain the eigenfrequency and the corresponding eigenmode of the submarine model. The correctness of the structural model is examined by comparing the results with existing papers and the finite element method, and the maximum deviation is not more than 2.07%. Additionally, the influence of the plate's thickness, position, inner diameter, as well as the laying angle on the intrinsic vibration characteristics of laminated submarine-like structure is determined. The results reveal that rational geometry design and assemblage benefit the vibration performance of the combination. Increasing the thickness of all the annular plates, decreasing the inner radius, and regulating the laminated scheme, make remarkable influence on structural free vibration, with the maximum relative changing rate of frequency exceeding 97%, 16%, and 23%, respectively.

Application of Teager-Kaiser Energy Operator in the Early Fault Diagnosis of Rolling Bearings

Rolling bearings are key components that support the rotation of motor shafts, operating with a quite high failure rate among all the motor components. Early bearing fault diagnosis has great significance to the operation security of motors. The main contribution of this paper is to illustrate Gaussian white noise in bearing vibration signals seriously masks the weak fault characteristics in the diagnosis based on the Teager-Kaiser energy operator envelope, and to propose improved TKEO taking both accuracy and calculation speed into account. Improved TKEO can attenuate noise in consideration of computational efficiency while preserving information about the possible fault. The proposed method can be characterized as follows: a series of band-pass filters were set up to extract several component signals from the original vibration signals; then a denoised target signal including fault information was reconstructed by weighted summation of these component signals; finally, the Fourier spectrum of TKEO energy of the resulting target signal was used for bearing fault diagnosis. The improved TKEO was applied to a vibration signal dataset of run-to-failure rolling bearings and compared with two advanced diagnosis methods. The experimental results verify the effectiveness and superiority of the proposed method in early bearing fault detection.

Analysis and Optimization of a Microgripper Driven by Linear Ultrasonic Motors

This paper presents the vibration response analysis and optimal structural design of a microgripper driven by linear ultrasonic motors (LUMs) dedicated to improving end-point positioning accuracy. Based on structural vibration theory, a parametric vibration response model of the microgripper finger was established, and the relative sensitivities of the structural and material parameters that affect the vibration amplitude of the fingertip were calculated within the structural and material constraints. Then, according to the sensitivity calculation results, a multidimensional constrained nonlinear optimization model was constructed to suppress the vibration of the end-effector. The improved internal penalty function method combined with Newton iteration was adopted to obtain the optimal structural parameters. Finally, the vibration experimental results show that the vibration amplitude of the initial microgripper fingertip is 16.31 μm, and the value measured after optimization was 2.49 μm, exhibiting a reduction of 84.7%. Therefore, the proposed optimal design method can effectively restrain the vibration of the microgripper end-effector and improve manipulation stability.

Study on Wear Mechanism of Helical Gear by Three-Body Abrasive Based on Impact Load

This study aimed to explore the wear characteristics and evolution mechanisms of large-scale wind power gears under the impact load of particles of the three-body abrasive Al2O3 (0.2 mg/mL) from four aspects: oil analysis, vibration analysis, amount of gear wear, and tooth-surface-wear profile analysis. A magnetic powder brake was used to simulate the actual working conditions. Combined with the abrasive particle monitoring and the morphology analysis of the tooth-surface-wear scar, by setting quantitative hard particles in the lubricating oil, the gears are mainly operated in the abrasive wear state, and wear monitoring and wear degree analysis are carried out for the whole life cycle of the gears. Oil samples were observed and qualitatively analyzed using a particle counter, a single ferrograph, a metallographic microscope, and a scanning electron microscope. The experiments demonstrate that the initial hard particles have a greater impact in the early wear stage of the gears (<20 h), and abrasive particle concentration increases by 30%. This means that Al2O3 particles accelerate the gear wear during the running-in period. The loading method of the impact load on the oblique gear exacerbates the abrasion particle wear and expands the stress concentration, which reduces the surface of large milling particles on the surface, and reduces the width of the tooth (the part above the pitch line is severely worn), which causes the gear to break into failure. The research provides help for analyzing the mechanism of abrasive wear of gears and predicting wear life.

Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine

Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 ∘C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.

Modal Frequencies Associations with Musculoskeletal Components of Human Legs for Extracorporeal Bone Healing Assessment Based on a Vibration Analysis Approach

Reliable and quantitative assessments of bone quality and fracture healing prompt well-optimised patient healthcare management and earlier surgical intervention prior to complications of nonunion and malunion. This study presents a clinical investigation on modal frequencies associations with musculoskeletal components of human legs by using a prototype device based on a vibration analysis method. The findings indicated that the first out-of-plane and coupled modes in the frequency range from 60 to 110 Hz are associated with the femur length, suggesting these modes are suitable quantitative measures for bone evaluation. Furthermore, higher-order modes are shown to be associated with the muscle and fat mass of the leg. In addition, mathematical models are formulated via a stepwise regression approach to determine the modal frequencies using the measured leg components as variables. The optimal models of the first modes consist of only femur length as the independent variable and explain approximately 43% of the variation of the modal frequencies. The subsequent findings provide insights for further development on utilising vibration-based methods for practical bone and fracture healing monitoring.

Smart Cutting Tools Used in the Processing of Aluminum Alloys

The processing of aluminum alloys in optimal conditions is a problem that has not yet been fully resolved. The research carried out so far has proposed various intelligent tools, but which cannot be used in the presence of cooling-lubricating fluids. The objective of the research carried out in the paper was to design intelligent tools that would allow a control of the vibrations of the tool tip and to determine a better roughness of the processed surfaces. The designed intelligent tools can be used successfully in the processing of aluminum alloys, not being sensitive to coolants-lubricants. In the research, the processing by longitudinal turning of a semi-finished product with a diameter Ø = 55 mm of aluminum alloy A2024-T3510 was considered. Two constructive variants of smart tools were designed, realized, and used, and the obtained results were compared with those registered for the tools in the classic constructive variant. The analysis of vibrations that occur during the cutting process was performed using the following methods: Fast Fourier Transform (FFT); Short-Time Fourier-Transformation (STFT); the analysis of signal of vibrations. A vibration analysis was also performed by modeling using the Finite Element Method (FEM). In the last part of the research, an analysis of the roughness of the processed surfaces, was carried out and a series of diagrams were drawn regarding curved profiles; filtered profiles; Abbott-Firestone curve. Research has shown that the use of smart tools in the proposed construction variants is a solution that can be used in very good conditions for processing aluminum alloys, in the presence of cooling-lubrication fluids.

Investigating the Structural Dynamics and Crack Propagation Behavior under Uniform and Non-Uniform Temperature Conditions

The robustness and stability of the system depend on structural integrity. This stability is, however, compromised by aging, wear and tear, overloads, and environmental factors. A study of vibration and fatigue cracking for structural health monitoring is one of the core research areas in recent times. In this paper, the structural dynamics and fatigue crack propagation behavior when subjected to thermal and mechanical loads were studied. It investigates the modal parameters of uncracked and various cracked specimens under uniform and non-uniform temperature conditions. The analytical model was validated by experimental and numerical approaches. The analysis was evaluated by considering different heating rates to attain the required temperatures. The heating rates were controlled by a proportional-integral-derivative (PID) temperature controller. It showed that a slow heating rate required an ample amount of time but more accurate results than quick heating. This suggested that the heating rate can cause variation in the structural response, especially at elevated temperatures. A small variation in modal parameters was also observed when the applied uniform temperatures were changed to non-uniform temperatures. This study substantiates the fatigue crack propagation behavior of pre-seeded cracks. The results show that propagated cracking depends on applied temperatures and associated mass. The appearance of double crack fronts and multiple cracks were observed. The appearance of multiple cracks seems to be due to the selection of the pre-seeded crack shape. Hence, the real cracks and pre-seeded cracks are distinct and need careful consideration in fatigue crack propagation analysis.

Novel Prediction of Diagnosis Effectiveness for Adaptation of the Spectral Kurtosis Technology to Varying Operating Conditions

In this paper, two novel consistency vectors are proposed, which when combined with appropriate machine learning algorithms, can be used to adapt the Spectral Kurtosis technology for optimum gearbox damage diagnosis in varying operating conditions. Much of the existing research in the field is limited to test apparatus run in constant and carefully controlled operating conditions, and the authors have previously publicised that the Spectral Kurtosis technology requires adaptation to achieve the highest possible probabilities of correct diagnosis when a gearbox is run in non-stationary conditions of speed and load. However, the authors' previous adaptation has been computationally heavy using a brute-force approach unsuited to online use, and therefore, created the requirement to develop these two newly proposed vectors and allow computationally lighter techniques more suited to online condition monitoring. The new vectors are demonstrated and experimentally validated on vibration data collected from a gearbox run in multiple combinations of operating conditions; for the first time, the two consistency vectors are used to predict diagnosis effectiveness, with the comparison and proof of relative gains between the traditional and novel techniques discussed. Consistency calculations are computationally light and thus, many combinations of Spectral Kurtosis technology parameters can be evaluated on a dataset in a very short time. This study shows that machine learning can predict the total probability of correct diagnosis from the consistency values and this can quickly provide pre-adaptation/prediction of optimum Spectral Kurtosis technology parameters for a dataset. The full adaptation and damage evaluation process, which is computationally heavier, can then be undertaken on a much lower number of combinations of Spectral Kurtosis resolution and threshold.

Cost-Effective Vibration Analysis through Data-Backed Pipeline Optimisation

Vibration analysis is an active area of research, aimed, among other targets, at an accurate classification of machinery failure modes. The analysis often leads to complex and convoluted signal processing pipeline designs, which are computationally demanding and often cannot be deployed in IoT devices. In the current work, we address this issue by proposing a data-driven methodology that allows optimising and justifying the complexity of the signal processing pipelines. Additionally, aiming to make IoT vibration analysis systems more cost- and computationally efficient, on the example of MAFAULDA vibration dataset, we assess the changes in the failure classification performance at low sampling rates as well as short observation time windows. We find out that a decrease of the sampling rate from 50 kHz to 1 kHz leads to a statistically significant classification performance drop. A statistically significant decrease is also observed for the 0.1 s time window compared to the 5 s one. However, the effect sizes are small to medium, suggesting that in certain settings lower sampling rates and shorter observation windows might be worth using, consequently making the use of the more cost-efficient sensors feasible. The proposed optimisation approach, as well as the statistically supported findings of the study, allow for an efficient design of IoT vibration analysis systems, both in terms of complexity and costs, bringing us one step closer to the widely accessible IoT/Edge-based vibration analysis.

A Novel Accelerometer Mounting Method for Sensing Performance Improvement in Acoustic Measurements From the Knee

In this study, we propose a new mounting method to improve accelerometer sensing performance in the 50 Hz-10 kHz frequency band for knee sound measurement. The proposed method includes a thin double-sided adhesive tape for mounting and a 3D-printed custom-designed backing prototype. In our mechanical setup with an electrodynamic shaker, the measurements showed a 13 dB increase in the accelerometer's sensing performance in the 1-10 kHz frequency band when it is mounted with the craft tape under 2 N backing force applied through low-friction tape. As a proof-of-concept study, knee sounds of healthy subjects (n = 10) were recorded. When the backing force was applied, we observed statistically significant (p < 0.01) incremental changes in spectral centroid, spectral roll-off frequencies, and high-frequency (1-10 kHz) root-mean-square (RMS) acceleration, while low-frequency (50 Hz-1 kHz) RMS acceleration remained unchanged. The mean spectral centroid and spectral roll-off frequencies increased from 0.8 kHz and 4.15 kHz to 1.35 kHz and 5.9 kHz, respectively. The mean high-frequency acceleration increased from 0.45 mgRMS to 0.9 mgRMS with backing. We showed that the backing force improves the sensing performance of the accelerometer when mounted with the craft tape and the proposed backing prototype. This new method has the potential to be implemented in today's wearable systems to improve the sensing performance of accelerometers in knee sound measurements.

Vibration Analysis for Fault Detection of Wind Turbine Drivetrains-A Comprehensive Investigation

Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of wind turbine drivetrains. It enables the defect location of mechanical subassemblies and health indicator construction for remaining useful life prediction, which is beneficial to reducing the operation and maintenance costs of wind farms. This paper analyzes the structure features of different drivetrains of mainstream wind turbines and introduces a vibration data acquisition system. Almost all the research on the vibration-based diagnosis algorithm for wind turbines in the past decade is reviewed, with its effects being discussed. Several challenging tasks and their solutions in the vibration-based fault detection of wind turbine drivetrains are proposed from the perspective of practicality for wind turbines, including the fault detection of planetary subassemblies in multistage wind turbine gearboxes, fault feature extraction under nonstationary conditions, fault information enhancement techniques and health indicator construction. Numerous naturally damaged cases representing the real operational features of industrial wind turbines are given, with a discussion of the failure mechanism of defective parts in wind turbine drivetrains as well.

Quasi-3D Hyperbolic Shear Deformation Theory for the Free Vibration Study of Honeycomb Microplates with Graphene Nanoplatelets-Reinforced Epoxy Skins

A novel quasi-3D hyperbolic shear deformation theory (QHSDT) with five unknowns is here employed, together with the Hamilton's principle and the modified couple stress theory (MCST) to analyze the vibrational behavior of rectangular micro-scale sandwich plates resting on a visco-Pasternak foundation. The sandwich structure features a Nomex or Glass phenolic honeycomb core, and two composite face sheets reinforced with graphene nanoplatelets (GPLs). The effective properties of both face sheets are evaluated by means of the Halpin-Tsai and extended rule of mixture (ERM) micromechanical schemes. The governing equations of the problem are derived by applying the Hamilton's principle, whose solutions are determined theoretically according to a classical Navier-type procedure. A parametric study checks for the effect of different material properties, length-scale parameters, foundation parameters and geometrical properties of the honeycomb cells, and the reinforcing GPLs, on the vibration response of the layered structure, which can be of great interest for many modern engineering applications and their optimization design.

A New Methodology for a Retrofitted Self-tuned Controller with Open-Source FPGA

Servo systems are feedback control systems characterized by position, speed, and/or acceleration outputs. Nowadays, industrial advances make the electronic stages in these systems obsolete compared to the mechanical elements, which generates a recurring problem in technological, commercial and industrial applications. This article presents a methodology for the development of an open-architecture controller that is based on reconfigurable hardware under the open source concept for servo applications. The most outstanding contribution of this paper is the implementation of a Genetic Algorithm for online self tuning with a focus on both high-quality servo control and reduction of vibrations during the positioning of a linear motion system. The proposed techniques have been validated on a real platform and form a novel, effective approach as compared to the conventional tuning methods that employ empirical or analytical solutions and cannot improve their parameter set. The controller was elaborated from the Graphical User Interface to the logical implementation while using free tools. This approach also allows for modification and updates to be made easily, thereby reducing the susceptibility to obsolescence. A comparison of the logical implementation with the manufacturer software was also conducted in order to test the performance of free tools in FPGAs. The Graphical User Interface developed in Python presents features, such as speed profiling, controller auto-tuning, measurement of main parameters, and monitoring of servo system vibrations.

High-Speed Measurement of Shape and Vibration: Whole-Field Systems for Motion Capture and Vibration Modal Analysis by OPPA Method

In shape measurement systems using a grating projection method, the phase analysis of a projected grating provides accurate results. The most popular phase analysis method is the phase shifting method, which requires several images for one shape analysis. Therefore, the object must not move during the measurement. The authors previously proposed a new accurate and high-speed shape measurement method, i.e., the one-pitch phase analysis (OPPA) method, which can determine the phase at every point of a single image of an object with a grating projected onto it. In the OPPA optical system, regardless of the distance of the object from the camera, the one-pitch length (number of pixels) on the imaging surface of the camera sensor is always constant. Therefore, brightness data for one pitch at any point of the image can be easily analyzed to determine phase distribution, or shape. This technology will apply to the measurement of objects in motion, including automobiles, robot arms, products on a conveyor belt, and vibrating objects. This paper describes the principle of the OPPA method and example applications for real-time human motion capture and modal analysis of free vibration of a flat cantilever plate after hammering. The results show the usefulness of the OPPA method.

Using an Impact Hammer to Estimate Elastic Modulus and Thickness of a Sample During an Osteotomy

Performing an osteotomy with a surgical mallet and an osteotome is a delicate intervention mostly based on the surgeon proprioception. It remains difficult to assess the properties of bone tissue being osteotomized. Mispositioning of the osteotome or too strong impacts may lead to bone fractures which may have dramatic consequences. The objective of this study is to determine whether an instrumented hammer may be used to retrieve information on the material properties around the osteotome tip. A hammer equipped with a piezo-electric force sensor was used to impact 100 samples of different composite materials and thicknesses. A model-based inversion technique was developed based on the analysis of two indicators derived from the analysis of the variation of the force as a function of time in order to (i) classify the samples depending on their material types, (ii) determine the materials stiffness, and (iii) estimate the samples thicknesses. The model resulting from the classification using support vector machines (SVM) learning techniques can efficiently predict the material of a new sample, with an estimated 89% prediction performance. A good agreement between the forward analytical model and the experimental data was obtained, leading to an average error lower than 10% in the samples thickness estimation. Based on these results, navigation and decision-support tools could be developed and allows surgeons to adapt their surgical strategy in a patient-specific manner.

Manufacturing ZrB2-SiC-TaC Composite: Potential Application for Aircraft Wing Assessed by Frequency Analysis through Finite Element Model

This study presents a new ultra-high temperature composite fabricated by using zirconium diboride (ZrB₂), silicon carbide (SiC), and tantalum carbide (TaC) with the volume ratios of 70%, 20%, and 10%, respectively. To attain this novel composite, an advanced processing technique of spark plasma sintering (SPS) was applied to produce ZrB₂-SiC-TaC. The SPS manufacturing process was achieved under pressure of 30 MPa, at 2000 °C for 5 min. The micro/nanostructure and mechanical characteristics of the composite were clarified using X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), and nano-indentation. For further investigations of the product and its characteristics, X-ray fluorescence (XRF) analysis and X-ray photoelectron spectroscopy (XPS) were undertaken, and the main constituting components were provided. The composite was densified to obtain a fully-dense ternary; the oxide pollutions were wiped out. The mean values of 23,356; 403.5 GPa; and 3100 °C were obtained for the rigidity, elastic modulus, and thermal resistance of the ZrB₂-SiC-TaC interface, respectively. To explore the practical application of the composite, the natural frequency of an aircraft wing considering three cases of materials: i) with a leading edge made of ZrB₂-SiC-TaC; ii) the whole wing made of ZrB₂-SiC-TaC; and iii) the whole wing made of aluminum 2024-T3 were investigated employing a numerical finite element model (FEM) tool ABAQUS and compared with that of a wing of traditional materials. The precision of the method was verified by performing static analysis to obtain the responses of the wing including total deformation, equivalent stress, and strain. A comparison study of the results of this study and published literature clarified the validity of the FEM analysis of the current research. The composite produced in this study significantly can improve the vibrational responses and structural behavior of the aircraft's wings.

Compressional-Wave Effects in the Operation of a Quartz Crystal Microbalance in Liquids:Dependence on Overtone Order

The operation of the quartz crystal microbalance (QCM) in liquids is plagued by small flexural admixtures to the thickness-shear deformation. The resonator surface moves not only in the transverse direction, but also along the surface normal, thereby emitting compressional waves into the liquid. Using a simple analytical model and laser Doppler vibrometry, we show that the flexural admixtures are stronger on the fundamental mode than on the overtones. The normal amplitude of motion amounts to about 1% of the transverse motion on the fundamental mode. This ratio drops by a factor of two on the overtones. A similar dependence on overtone order is observed in experiments, where the resonator is immersed in a liquid and faces an opposite planar wall, the distance of which varies. Standing compressional waves occur at certain distances. The amplitudes of these are smaller on the overtones than on the fundamental mode. The findings can be rationalized with the tensor form of the small-load approximation.

A Finite Element Model for the Vibration Analysis of Sandwich Beam with Frequency-Dependent Viscoelastic Material Core

In this work, a finite element model was developed for vibration analysis of sandwich beam with a viscoelastic material core sandwiched between two elastic layers. The frequency-dependent viscoelastic dynamics of the sandwich beam were investigated by using finite element analysis and experimental validation. The stiffness and damping of the viscoelastic material core is frequency-dependent, which results in complex vibration modes of the sandwich beam system. A third order seven parameter Biot model was used to describe the frequency-dependent viscoelastic behavior, which was then incorporated with the finite elements of the sandwich beam. Considering the parameters identification, a strategy to determine the parameters of the Biot model has been outlined, and the curve fitting results closely follow the experiment. With identified model parameters, numerical simulations were carried out to predict the vibration and damping behavior in the first three vibration modes, and the results showed that the finite model presented here had good accuracy and efficiency in the specific frequency range of interest. The experimental testing on the viscoelastic sandwich beam validated the numerical predication. The experimental results also showed that the finite element modeling method of sandwich beams that was proposed was correct, simple and effective.

Non-Destructive Testing of a Sport Tribune under Synchronized Crowd-Induced Excitation Using Vibration Analysis

This paper presents the concept of repairing the stand of a motorbike speedway stadium. The synchronized dancing of fans cheering during a meeting brought the stand into excessive resonance. The main goal of this research was to propose a method for the structural tuning of stadium stands. Non-destructive testing by vibration methods was conducted on a selected stand segment, the structure of which recurred on the remaining stadium segments. Through experiments, we determined the vibration forms throughout the stand, taking into account the dynamic impact of fans. Numerical analyses were performed on the 3-D finite element method (FEM) stadium model to identify the dynamic jump load function. The results obtained on the basis of sensitivity tests using the finite element method allowed the tuning of the stadium structure to successfully meet the requirements of the serviceability limit state.

Effects of mass loading on the viability of assessing the state of healing of a fixated fractured long bone

Introduction

This paper aims to evaluate the effects of mass loading on the healing assessment of an internally fixated femur by vibrational means. The presence of soft tissue surrounding a femur increases damping and mass of a system, and hence affects the vibrational response of a mechanical structure by obscuring the coherent modes. This may compromise vibration-based monitoring strategies in identifying modes associated with fracture healing.

Methods

This paper presents a series of experimental works to address this issue. Two osteotomised composite femurs were internally fixated using a plate-screw system and an intramedullary nail. Soft tissue is approximated by surrounding an artificial Sawbone femur with modelling clay. The femur is excited by an instrumented impact hammer and instrumented with two accelerometers to record bending and torsion modes between 0 and 600 Hz. A 30-min epoxy was applied to simulate the healing of the fractured femur in the osteotomised region. The resonant frequencies and its modes are monitored while union is being formed and a healing index is calculated at various times to quantify the degree of healing.

Results

The results demonstrate that the effect of modelling clay compressed the natural modes along the frequency axis. It is observed that frequency bandwidth in the vicinity of 150 Hz and 500 Hz is sensitive to the state of healing of the fixated femurs, which is due to the increase in stiffness of the osteotomised region. These findings were used to formulate the healing index which assists in identifying the initial, later and complete healing stages in conjunction with the index derivative.

Conclusion

In this study, a two-sensor measurement strategy to quantify fixated femur healing is investigated. It is shown that the mass loading effect did not affect this vibrational analysis method ability to assess the state of healing, and both coherent bending and twisting modes associated with healing were easily identified. The proposed healing index, its derivative, and the cross-spectra are a viable tool for quantitative healing assessment.

Collision Detection and Identification on Robot Manipulators Based on Vibration Analysis

Robot manipulators should be able to quickly detect collisions to limit damage due to physical contact. Traditional model-based detection methods in robotics are mainly concentrated on the difference between the estimated and actual applied torque. In this paper, a model independent collision detection method is presented, based on the vibration features generated by collisions. Firstly, the natural frequencies and vibration modal features of the manipulator under collisions are extracted with illustrative examples. Then, a peak frequency based method is developed for the estimation of the vibration modal along the manipulator structure. The vibration modal features are utilized for the construction and training of the artificial neural network for the collision detection task. Furthermore, the proposed networks also generate the location and direction information about contact. The experimental results show the validity of the collision detection and identification scheme, and that it can achieve considerable accuracy.

Experimental Setup for Dynamic Analysis of Micro- and Nano-Mechanical Systems in Vacuum, Gas, and Liquid

An experimental setup to perform dynamic analysis of a micro- and nano-mechanical system in vacuum, gas, and liquid is presented. The setup mainly consists of a piezoelectric excitation part and the chamber that can be either evacuated for vacuum, or filled with gas or water. The design of the piezoelectric actuator was based on a Langevin transducer. The chamber is made out of materials that can sustain: vacuum, variety of gases and different types of liquids (mild acids, alkalies, common alcohols and oils). All the experiments were performed on commercial cantilevers used for contact and tapping mode Atomic Force Microscopy (AFM) with stiffness 0.2 N/m and 48 N/m, respectively, in vacuum, air and water. The performance of the setup was evaluated by comparing the measured actuator response to a finite element model. The frequency responses of the two AFM cantilevers measured were compared to analytical equations. A vacuum level of 0.6 mbar was obtained. The setup has a bandwidth of 10⁻550 kHz in vacuum and air, and a bandwidth of 50⁻550 kHz in liquid. The dynamic responses of the cantilevers show good agreement with theory in all media.

A Self-Adaptive Umbrella Model for Vibration Analysis of Graphene

The beam finite element and molecular dynamics models are two popular methods to represent the reaction of carbon-carbon bonds in graphene. However, the wrinkles and ripples in geometrical characteristics are difficult take into consideration. The out-planar mechanical properties are neglected in classical models of graphene. This paper proposes a self-adaptive umbrella model for vibration analysis of graphene. The parameters in the umbrella model are flexible to adapting the geometrical and material characteristics of graphene. The umbrella model consists of shell and beam elements. The honeycomb beam and planar shell model of graphene are included in the self-adaptive umbrella model as particular cases. The sensitivity analysis and results confirmed the rationality and feasibility of the self-adaptive umbrella model.

Occupational risks related to vibrations using a brush cutter for green area management

INTRODUCTION

Weed control is one of the most important issues in the maintenance sectors of both agriculture and green areas. Small tools are employed for controlling grass and other growths on steep verges and river banks. This leads the operators being exposed to many risks among which vibration is one. The purpose of this study is to measure and evaluate hand-arm vibration and to verify the daily exposure to which workers are often subjected while weeding.

MATERIAL AND METHODS

Two cutting heads, a brush knife and a mowing head were compared. Both were mounted on the same cow-horn brush cutter. The vibration total value was expressed as the root-mean-square (rms) of three component values according to the axes X, Y and Z. The signal was frequency weighted using the weighting curve Wh, as described in the ISO 5349-1 (2001) standard. In addition, the daily vibration exposure was calculated and compared with the thresholds set by EU Directive 2002/44/EC (2005).

RESULTS

The obtained results showed that the exposure action value (EAV) of 2.5 ms^-2 was exceeded while using both cutting heads. The exposure limit value (ELV) using the brush knife also exceeded 5 ms^-2.

CONCLUSIONS

The results highlighted important aspects in terms of exposure values that should be considered with the view of preventing the risk of Hand-Arm Vibration Syndrome (HAVS) to which the operators who frequently use these tools are exposed. Specific measures should therefore be taken to protect the exposed workers.

Effective Crack Detection in Railway Axles Using Vibration Signals and WPT Energy

Crack detection for railway axles is key to avoiding catastrophic accidents. Currently, non-destructive testing is used for that purpose. The present work applies vibration signal analysis to diagnose cracks in real railway axles installed on a real Y21 bogie working on a rig. Vibration signals were obtained from two wheelsets with cracks at the middle section of the axle with depths from 5.7 to 15 mm, at several conditions of load and speed. Vibration signals were processed by means of wavelet packet transform energy. Energies obtained were used to train an artificial neural network, with reliable diagnosis results. The success rate of 5.7 mm defects was 96.27%, and the reliability in detecting larger defects reached almost 100%, with a false alarm ratio lower than 5.5%.

A High-Speed Imaging Method Based on Compressive Sensing for Sound Extraction Using a Low-Speed Camera

This paper reports an efficient method for sound extraction from high-speed light spot videos reconstructed from the coded light spot images captured with a low-speed camera based on compressive sensing, but at the expense of consuming time. The proposed method first gets the high-speed video of the light spot that is illuminated on the vibrating target caused by sound. Then the centroid of the light spot is used to recover the sound. Simulations of the proposed method are carried out and experimental results are demonstrated. The results show that high-speed videos with a frame rate of 2000 Hz can be reconstructed with a low-speed (100 Hz) charge-coupled device (CCD) camera, which is randomly modulated by a digital micro-mirror device (DMD) 20 times during each exposure time. This means a speed improvement of 20 times is achieved. The effects of synchronization between CCD image recording and DMD modulation, the optimal sampling patterns of DMD, and sound vibration amplitudes on the performance of the proposed method are evaluated. Using this compressive camera, speech (counting from one to four in Chinese) was recovered well. This has been confirmed by directly listening to the recovered sound, and the intelligibility value (0–1) that evaluated the similarity between them was 0.8185. Although we use this compressive camera for sound detection, we expect it to be useful in applications related to vibration and motion.

In vivo impact testing on a lengthened femur with external fixation: a future option for the non-invasive monitoring of fracture healing?

Non-invasive methods for assessing fracture healing are crucial for biomedical engineers. An approach based on mechanical vibrations was tried out in the 1990s, but was soon abandoned due to insufficiently advanced technologies. The same approach is re-proposed in the present study in order to monitor the healing process of a lengthened femur with an external fixator. The pins screwed into the bone were exploited for the impact testing (IT) to excite the bone and capture its response. Transmission through the soft tissues was thus prevented, and the quality of the signals was improved. Impact tests were performed every three to four weeks for five months. Unfortunately, after seven weeks, some pins were removed due to infection, and thus, the system was modified. Two different configurations were considered: before and after pin removal. An additional configuration was examined in the last two sessions, when the fixator body was removed, while four pins were left in the femur. The evolution of the frequency response function and of the resonant frequencies of the system were analysed for the duration of the monitoring period. The IT results were compared to the indications provided by X-ray images. During the evolution of the callus from the soft phase to the woven bone, the resonant frequencies of the system were found to increase by approximately 2-3% per week. The largest increase (approx. 22%) was observed for the first resonant frequency. After formation of the woven bone, the vibratory response remained almost the same, suggesting that the healing assessment could be related to the relative variation in the resonant frequencies. The results presented support the application of the IT approach for fracture healing assessment.

A Low-Cost Modular Platform for Heterogeneous Data Acquisition with Accurate Interchannel Synchronization

Most experimental fields of science and engineering require the use of data acquisition systems (DAQ), devices in charge of sampling and converting electrical signals into digital data and, typically, performing all of the required signal preconditioning. Since commercial DAQ systems are normally focused on specific types of sensors and actuators, systems engineers may need to employ mutually-incompatible hardware from different manufacturers in applications demanding heterogeneous inputs and outputs, such as small-signal analog inputs, differential quadrature rotatory encoders or variable current outputs. A common undesirable side effect of heterogeneous DAQ hardware is the lack of an accurate synchronization between samples captured by each device. To solve such a problem with low-cost hardware, we present a novel modular DAQ architecture comprising a base board and a set of interchangeable modules. Our main design goal is the ability to sample all sources at predictable, fixed sampling frequencies, with a reduced synchronization mismatch (<1 µs) between heterogeneous signal sources. We present experiments in the field of mechanical engineering, illustrating vibration spectrum analyses from piezoelectric accelerometers and, as a novelty in these kinds of experiments, the spectrum of quadrature encoder signals. Part of the design and software will be publicly released online.

A Long Distance Phase-Sensitive Optical Time Domain Reflectometer with Simple Structure and High Locating Accuracy

A phase-sensitive optical time domain reflectometer (Φ-OTDR) can be used for pipeline security. However, the sensing distance (less than 20 km) of traditional Φ-OTDR is too short for the needs of typical oil and gas pipeline monitoring applications (30–50 km). A simple structure Φ-OTDR system utilizing long pulse, balanced amplified detector and heterodyne detection is proposed in this paper and the sensing range is thereby increased to 60 km. Through analyzing the sensing principle of Φ-OTDR, a novel locating strategy is proposed to maintain the locating accuracy at a few meters when a long pulse (5 µs) is used. The increased pulse width deteriorates the time series of each sensing point seriously. In order to eliminate the deterioration, a data processing technique combining wavelet and empirical mode decomposition is applied in this system. The experiment results show that the sensing distance can be increased to 60 km and the locating accuracy is maintained at 6.8 m.

Monitoring the press-fit insertion of an acetabular cup by impact measurements: influence of bone abrasion

Press-fit procedures used for the insertion of cementless hip prostheses aim at obtaining optimal implant primary stability. We have previously used the measurement of impact duration to follow the insertion of the acetabular cup implant within bone tissue. The aim of this study was to investigate the variation of the value of the impact momentum due to successive insertions of the acetabular cup into bone tissue. The results obtained with impact momentum and contact duration measurements were compared. A total of 10 bovine bone samples were subjected to three successive procedures consisting of 10 reproducible impacts (3.5 kg falling 40 mm). Each procedure aimed at inserting the acetabular cup implant into the same bone cavity. The time variation of force during each impact was recorded by a force sensor, allowing the measurement of the impact duration (I 1) and momentum (I 2). The value of I 2 increased as a function of the impact number and reached a constant value after N 2 = 5.07 ± 1.31 impacts. Moreover, statistical analyses show that N 2 decreased significantly as a function of the number of experiments, which may be due to abrasion phenomena at the bone-implant interface. Abrasion phenomena led to a faster insertion of the acetabular cup when the implant had been previously inserted into the same bone cavity. An empirical analytical model considering a flat punch configuration to model the bone-implant contact conditions was used to understand the trend of the variation of I 2 during the insertion of the acetabular cup. The measurement of the force during impacts is useful to assess the bone-implant interface properties, but needs to be validated in the clinic to be useful for orthopaedic surgeons intra-operatively.

In vivo monitoring of implant osseointegration in a rabbit model using acoustic sound analysis

Implant osseointegration can currently only be assessed reliably post mortem. A novel method that relies on the principle of acoustic sound analysis was developed to enable examination of the longitudinal progress of osseointegration. The method is based on a magnetic sphere inside a hollow cylinder of the implant. By excitation using an external magnetic field, collision of the sphere inside the implant produces a sound signal. Custom-made titanium implants equipped thusly were inserted in each lateral femoral epicondyle of 20 New Zealand White Rabbits. Two groups were investigated: Uncoated, machined surface versus antiadhesive surface; and calcium phosphate-coated surface versus antiadhesive surface. The sound analysis was performed postoperatively and weekly. After 4 weeks, the animals were euthanized, and the axial pull-out strengths of the implants were determined. A significant increase in the central frequency was observed for the loose implants (mean pull-out strength 21.1 ± 16.9 N), up to 6.4 kHz over 4 weeks. In comparison, the central frequency of the osseointegrated implants (105.2 ± 25.3 N) dropped to its initial value. The presented method shows potential for monitoring the osseointegration of different implant surfaces and could considerably reduce the number of animals needed for experiments.