Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries

There has been enormous growth in the energy sector in the new millennium, and it has enhanced energy demand, creating an exponential rise in the capital investment in the energy industry in the last few years. Regular monitoring of the health of industrial equipment is necessary, and thus, the concept of structural health monitoring (SHM) comes into play. In this paper, the purpose is to highlight the importance of SHM systems and various techniques primarily used in pipelining industries. There have been several advancements in SHM systems over the years such as Point OFS (optical fiber sensor) for Corrosion, Distributed OFS for physical and chemical sensing, etc. However, these advanced SHM technologies are at their nascent stages of development, and thus, there are several challenges that exist in the industries. The techniques based on acoustic, UAVs (Unmanned Aerial Vehicles), etc. bring in various challenges, as it becomes daunting to monitor the deformations from both sides by employing only one technique. In order to determine the damages well in advance, it is necessary that the sensor is positioned inside the pipes and gives the operators enough time to carry out the troubleshooting. However, the mentioned technologies have been unable to indicate the errors, and thus, there is the requirement for a newer technology to be developed. The purpose of this review manuscript is to enlighten the readers about the importance of structural health monitoring in pipeline and wind turbine industries.

A CUSUM-Based Approach for Condition Monitoring and Fault Diagnosis of Wind Turbines

This paper presents a cumulative sum (CUSUM)-based approach for condition monitoring and fault diagnosis of wind turbines (WTs) using SCADA data. The main ideas are to first form a multiple linear regression model using data collected in normal operation state, then monitor the stability of regression coefficients of the model on new observations, and detect a structural change in the form of coefficient instability using CUSUM tests. The method is applied for on-line condition monitoring of a WT using temperature-related SCADA data. A sequence of CUSUM test statistics is used as a damage-sensitive feature in a control chart scheme. If the sequence crosses either upper or lower critical line after some recursive regression iterations, then it indicates the occurrence of a fault in the WT. The method is validated using two case studies with known faults. The results show that the method can effectively monitor the WT and reliably detect abnormal problems.

Vibration-Based Monitoring of Wind Turbines: Influence of Layout and Noise of Sensors

The reduction in operating and maintenance costs of wind farms is a fundamental element to guarantee the competitiveness and growth of the wind market. Wind turbines are highly dynamic structures prone to wear during their lifetime. Therefore, dynamic monitoring systems represent an excellent option to continuously evaluate their structural conditions. These systems allow early detection of damages, permit a proactive response, minimising downtime, and maximising productivity. In this context, the present paper describes the main results obtained with alternative instrumentation strategies tested in a 2.0 MW onshore wind turbine to reduce the costs of the monitoring equipment and at the same time ensure an adequate accuracy in structural condition evaluation. The data processing strategy encompasses the use of operational modal analysis combined with algorithms that deal with the particularities of operation of the wind turbines to continuously track the main vibration modes. After this automated online identification, the influence of the environmental and operating conditions on the tracked natural frequencies is mitigated, making the detection of abnormal variations of the natural frequencies possible, which might flag the appearance of damage. A database of continuously collected acceleration time series during one year is adopted to test the efficiency of alternative monitoring system layouts in detecting simulated damage scenarios. The tested alternative monitoring layouts present a varying number of sensors, alternative distributions in the wind turbine tower, and different sensor noise levels.

Damage Mechanism Based Approach to the Structural Health Monitoring of Wind Turbine Blades

A damage mechanism based approach to the structural health monitoring of wind turbine blades is formulated. Typical physical mechanisms of wind turbine blade degradation, including surface erosion, adhesive fatigue, laminate cracking and in some cases compressive kinking and failure are reviewed. Examples of a local, damage mechanism based approach to the structural health monitoring of wind turbine blades are demonstrated, including the monitoring of leading edge erosion of wind turbine blades, adhesive bond failure, plydrop delamination, static and dynamic plydrop tests, and bolt and laminate fatigue. The examples demonstrate the possibilities of monitoring specific damage mechanisms, and specific localizations of wind turbine blades.

Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

Monitoring Wind Turbine Gearbox with Echo State Network Modeling and Dynamic Threshold Using SCADA Vibration Data

Health monitoring of wind turbine gearboxes has gained considerable attention as wind turbines become larger in size and move to more inaccessible locations. To improve the reliability, extend the lifetime of the turbines, and reduce the operation and maintenance cost caused by the gearbox faults, data-driven condition motoring techniques have been widely investigated, where various sensor monitoring data (such as power, temperature, and pressure, etc.) have been modeled and analyzed. However, wind turbines often work in complex and dynamic operating conditions, such as variable speeds and loads, thus the traditional static monitoring method relying on a certain fixed threshold will lead to unsatisfactory monitoring performance, typically high false alarms and missed detections. To address this issue, this paper proposes a reliable monitoring model for wind turbine gearboxes based on echo state network (ESN) modeling and the dynamic threshold scheme, with a focus on supervisory control and data acquisition (SCADA) vibration data. The aim of the proposed approach is to build the turbine normal behavior model only using normal SCADA vibration data, and then to analyze the unseen SCADA vibration data to detect potential faults based on the model residual evaluation and the dynamic threshold setting. To better capture temporal information inherent in monitored sensor data, the echo state network (ESN) is used to model the complex vibration data due to its simple and fast training ability and powerful learning capability. Additionally, a dynamic threshold monitoring scheme with a sliding window technique is designed to determine dynamic control limits to address the issue of the low detection accuracy and poor adaptability caused by the traditional static monitoring methods. The effectiveness of the proposed monitoring method is verified using the collected SCADA vibration data from a wind farm located at Inner Mongolia in China. The results demonstrated that the proposed method can achieve improved detection accuracy and reliability compared with the traditional static threshold monitoring method.

Health Monitoring and Safety Evaluation of the Offshore Wind Turbine Structure: A Review and Discussion of Future Development

With the depletion of fossil energy, offshore wind power has become an irreplaceable energy source for most countries in the world. In recent years, offshore wind power generation has presented the gradual development trend of larger capacity, taller towers, and longer blades. The more flexible towers and blades have led to the structural operational safety of the offshore wind turbine (OWT) receiving increasing worldwide attention. From this perspective, health monitoring systems and operational safety evaluation techniques of the offshore wind turbine structure, including the monitoring system category, data acquisition and transmission, feature information extraction and identification, safety evaluation and reliability analysis, and the intelligent operation and maintenance, were systematically investigated and summarized in this paper. Furthermore, a review of the current status, advantages, disadvantages, and the future development trend of existing systems and techniques was also carried out. Particularly, the offshore wind power industry will continue to develop into deep ocean areas in the next 30 years in China. Practical and reliable health monitoring systems and safety evaluation techniques are increasingly critical for offshore wind farms. Simultaneously, they have great significance for strengthening operation management, making efficient decisions, and reducing failure risks, and are also the key link in ensuring safe energy compositions and achieving energy development targets in China. The aims of this article are to inform more scholars and experts about the status of the health monitoring and safety evaluation of the offshore wind turbine structure, and to contribute toward improving the efficiency of the corresponding systems and techniques.

Crack Monitoring of Operational Wind Turbine Foundations

The degradation of onshore, reinforced-concrete wind turbine foundations is usually assessed via above-ground inspections, or through lengthy excavation campaigns that suspend wind power generation. Foundation cracks can and do occur below ground level, and while sustained measurements of crack behaviour could be used to quantify the risk of water ingress and reinforcement corrosion, these cracks have not yet been monitored during turbine operation. Here, we outline the design, fabrication and field installation of subterranean fibre-optic sensors for monitoring the opening and lateral displacements of foundation cracks during wind turbine operation. We detail methods for in situ sensor characterisation, verify sensor responses against theoretical tower strains derived from wind speed data, and then show that measured crack displacements correlate with monitored tower strains. Our results show that foundation crack opening displacements respond linearly to tower strain and do not change by more than ±5 μm. Lateral crack displacements were found to be negligible. We anticipate that the work outlined here will provide a starting point for real-time, long-term and dynamic analyses of crack displacements in future. Our findings could furthermore inform the development of cost-effective monitoring systems for ageing wind turbine foundations.

Quantitative damage detection and sparse sensor array optimization of carbon fiber reinforced resin composite laminates for wind turbine blade structural health monitoring

The active structural health monitoring (SHM) approach for the complex composite laminate structures of wind turbine blades (WTBs), addresses the important and complicated problem of signal noise. After illustrating the wind energy industry's development perspectives and its crucial requirement for SHM, an improved redundant second generation wavelet transform (IRSGWT) pre-processing algorithm based on neighboring coefficients is introduced for feeble signal denoising. The method can avoid the drawbacks of conventional wavelet methods that lose information in transforms and the shortcomings of redundant second generation wavelet (RSGWT) denoising that can lead to error propagation. For large scale WTB composites, how to minimize the number of sensors while ensuring accuracy is also a key issue. A sparse sensor array optimization of composites for WTB applications is proposed that can reduce the number of transducers that must be used. Compared to a full sixteen transducer array, the optimized eight transducer configuration displays better accuracy in identifying the correct position of simulated damage (mass of load) on composite laminates with anisotropic characteristics than a non-optimized array. It can help to guarantee more flexible and qualified monitoring of the areas that more frequently suffer damage. The proposed methods are verified experimentally on specimens of carbon fiber reinforced resin composite laminates.