One sensor acoustic emission localization in plates

Acoustic source localization using L-shaped sensor clusters: A review

Acoustic source localization (ASL) plays an important role in structural health monitoring (SHM). The L-shaped sensor cluster (LSSC) is very convenient for ASL, and hence SHM. Various techniques based on LSSC have been developed rapidly in the past decade. LSSC can be conveniently used for damage detection and localization, a necessary step for monitoring structures through non-destructive testing (NDT). After ten years of development, LSSC still has a wide development space. In this paper, the fundamental roles of LSSC in developing different techniques within last ten years and its future potentials are discussed. The LSSC-based time difference of arrival localization techniques and the wave front shape-based localization techniques are reviewed in detail in this paper. This paper aims to give readers a more comprehensive and clear understanding of these techniques. The discussion on the advantages and disadvantages of these techniques and various sources of the errors will give the readers the current limitations and future development prospects of ASL and damage detection techniques using LSSC.

An Integration of Neural Network and Shuffled Frog-Leaping Algorithm for CNC Machining Monitoring

This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN).

Active source localization in wave guides based on machine learning

In the present work, an active source localization strategy is proposed. The presence of active sources in a waveguide can have several reasons, such as crack initiation or internal friction. In this study, the active source is represented by an impact event. A steel ball is dropped on an aluminum plate at different positions. Elastic waves are excited and will propagate through the plate. The wave response is acquired by a piezoelectric sensor network, which is attached to the plate. After performing numerical and physical experiments, enough data are collected in order to train an artificial neural network and a support vector machine. Those machine learning algorithms will predict the impact position based on the wave response of each sensor, while only numerical data from the finite element simulations are used to train both methods. After the training process is completed, the algorithms are applied to experimental data. A good agreement between reference and predicted results proves that the wave responses at the piezoelectric transducers contain sufficient information in order to localize the impact position precisely.

Performance analysis of non-reflective boundary conditions on sound localization problem in an isotropic plate

Acoustic source localization, considering the effect of reflected waves from geometrical features (such as holes, lugs and structural discontinuities) is still one of the most challenging areas in this field. In this paper, the effects of reflected waves from edges on source localization results are discussed. Most of the previous studies have ignored the reflected waves by selection of the test zone far from the test plate edges. The current approaches for considering reflected waves are based on using high Sampling Rate Data (SRD) which is unsuitable for practical applications. This paper discusses how silicon dampers on edges affect the acoustic source localization on an isotropic plate using low SRD. In this approach, four silicon dampers are installed on a Plexiglas plate. The effect of each damper is experimentally tested on final prediction error. The experimental results reveal that the reduction of prediction error according to each damper highly depends on the impact and sensor location related to the damper's position.

Multiphysics Simulation of Low-Amplitude Acoustic Wave Detection by Piezoelectric Wafer Active Sensors Validated by In-Situ AE-Fatigue Experiment

Piezoelectric wafer active sensors (PWAS) are commonly used for detecting Lamb waves for structural health monitoring application. However, in most applications of active sensing, the signals are of high-amplitude and easy to detect. In this article, we have shown a new avenue of using the PWAS transducer for detecting the low-amplitude fatigue-crack related acoustic emission (AE) signals. Multiphysics finite element (FE) simulations were performed with two PWAS transducers bonded to the structure. Various configurations of the sensors were studied by using the simulations. One PWAS was placed near to the fatigue-crack and the other one was placed at a certain distance from the crack. The simulated AE event was generated at the crack tip. The simulation results showed that both PWAS transducers were capable of sensing the AE signals. To validate the multiphysics simulation results, an in-situ AE-fatigue experiment was performed. Two PWAS transducers were bonded to the thin aerospace test coupon. The fatigue crack was generated in the test coupon which had produced low-amplitude acoustic waves. The low-amplitude fatigue-crack related AE signals were successfully captured by the PWAS transducers. The distance effect on the captured AE signals was also studied. It has been shown that some high-frequency contents of the AE signal have developed as they travel away from the crack.

Acoustic emission source localization in thin metallic plates: A single-sensor approach based on multimodal edge reflections

This paper presents a new acoustic emission (AE) source localization for isotropic plates with reflecting boundaries. This approach that has no blind spot leverages multimodal edge reflections to identify AE sources with only a single sensor. The implementation of the proposed approach involves three main steps. First, the continuous wavelet transform (CWT) and the dispersion curves of the fundamental Lamb wave modes are utilized to estimate the distance between an AE source and a sensor. This step uses a modal acoustic emission approach. Then, an analytical model is proposed that uses the estimated distances to simulate the edge-reflected waves. Finally, the correlation between the experimental and the simulated waveforms is used to estimate the location of AE sources. Hsu-Nielsen pencil lead break (PLB) tests were performed on an aluminum plate to validate this algorithm and promising results were achieved. Based on these results, the paper reports the statistics of the localization errors.

Solder joint failure localization of welded joint based on acoustic emission beamforming

A localization approach of welded joint damage is proposed based on acoustic emission (AE) beamforming. In this method, a uniform line array is introduced to detect the AE signal of welded joints in specified area. In order to investigate the influence of fillet and crimping commonly existing in a welded plate structure during the AE wave propagation process, the finite element method (FEM) is applied to simulate the behavior of AE wave in the specimen. The simulation localization results indicate that the proposed localization approach can effectively localize AE sources although there exist the fillet and crimping, and it is also validated by the pencil-lead-broken test on rectangular steel tube with welded joints. Finally, the proposed method is adopted to localize the failure of solder joint in operation vibration condition. The proposed method is successful to localize the compact AE source caused by the cracked joint based on wavelet packet transform.

Sound localization in an anisotropic plate using electret microphones

Acoustic source localization without knowing the velocity profile in anisotropic plates is still one of the most challenging areas in this field. The current time-of-flight based approaches for localization in anisotropic media, are based on using six high sampling sensors. The number of sensors and the corresponding large amount of data, would make those methods inefficient in practical applications. Although there are many different non-time-of-flight based approaches such as machine learning, or soft computing based methods that can be used for localization with a less number of sensors, they are not as accurate as time-of-flight based techniques. In this article, a new approach which requires only four low sampling rate sensors to localize acoustic source in an anisotropic plate is proposed. In this technique, four electret low sampling rate sensors in two clusters are installed on the plate surface. The presented method uses attenuation analysis in a suitable frequency band to decrease the number of sensors. The approach is experimentally tested and verified on an airplane composite nose by applying artificially generated acoustic emissions (Hsu-Nielsen source). The results reveal that the accuracy of proposed technique depends on distinction of dominant frequency band. A stethoscope as a physical filter is employed to reduce the sensitivity of the technique and delineation of frequency band. The suggested technique improves the accuracy of localization prediction.