As the demand for wind energy continues to grow at exponential rates, reducing operation and maintenance (OM) costs and improving reliability have become top priorities in wind turbine (WT) maintenance strategies. In addition to the development of more highly evolved WT designs intended to improve availability, the application of reliable and cost-effective condition-monitoring (CM) techniques offers an efficient approach to achieve this goal. This paper provides a general review and classification of wind turbine condition monitoring (WTCM) methods and techniques with a focus on trends and future challenges. After highlighting the relevant CM, diagnosis, and maintenance analysis, this work outlines the relationship between these concepts and related theories, and examines new trends and future challenges in the WTCM industry. Interesting insights from this research are used to point out strengths and weaknesses in today’s WTCM industry and define research priorities needed for the industry to meet the challenges in wind industry technological evolution and market growth.

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Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges

Gearbox fault diagnosis based on deep random forest fusion of acoustic and vibratory signals

Wind turbines have been developed fast in the recent years and at the same time have brought some problems. It is important to maintain the healthy condition of the running turbine because the consequences after faults are miserable for both the company and owner. There is a constant need to reduce the costs of operating and maintaining the turbines. Therefore, it is very important to detect the faults/failures early so as to minimize downtime and maximize productivity. This paper reviewed the structure of wind turbines and analyzed the different components of wind turbines in order to detect the faults that may happen. Meanwhile, this paper mainly reviewed fault diagnosis methods of wind turbines in the last three years. The main purpose of this paper is to supply some information on structure healthy condition monitoring (SHCM) and fault diagnosis in wind turbines for related researchers.

The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review

Damage detection techniques for wind turbine blades: A review

Damage characterization of laminated composites has been thoroughly studied the last decades where researchers developed several damage models, and in combination with experimental evidence, contributed to better understanding of the structural behavior of these structures. Experimental techniques played an essential role on this progress and among the techniques that were utilized, acoustic emission (AE) was extensively used due to its advantages for in-situ damage monitoring with high sensitivity and its capability to inspect continuously a relatively large area. This paper presents a comprehensive review on the use of AE for damage characterization in laminated composites. The review is divided into two sections; the first section discusses the literature for damage diagnostics and it is presented in three subsections: damage initiation detection, damage type identification and damage localization, while the second section is devoted to damage prognostics and it focuses on the remaining useful life (RUL) and residual strength prediction of composite structures using AE data. In every section, efforts have been made to analyze the most relevant literature, discuss in a critical manner the results and conclusions, and identify possibilities for future work.

Damage characterization of laminated composites using acoustic emission: A review

https://bura.brunel.ac.uk/bitstream/2438/8394/2/Fulltext.pdf

Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring

https://bura.brunel.ac.uk/bitstream/2438/13153/3/Fulltext.pdf

An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades

The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency−frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency−MARSE, and average frequency−peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope). The results show that these parameters are representative for the classification of the failure modes.

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A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades

Condition monitoring of a wind turbine drive train based on its power dependant vibrations

Offshore installations are subject to perpetual fatigue loading and are usually very hard to inspect. Close visual inspection from the turret is usually too hazardous for divers and is not possible with remotely operated vehicles (ROVs) because of the limited access. Conventional nondestructive techniques (NDTs) have been used in the past to carry out inspections of mooring chains, floating production storage and offloading systems (FPSOs), and other platforms. Although these have been successful at detecting and assessing fatigue cracks, the hazardous nature of the operations calls for remote techniques that could be applied continuously to identify damage initiation and progress. The aim of the present work is to study the capabilities of acoustic emission (AE) as a monitoring tool to detect fatigue crack initiation and propagation in mooring chains. A 72-day large-scale experiment was designed for this purpose. A detailed analysis of the different AE signal time domain features was not conclusive, possibly due to the high level of noise. However, the frequency content of the AE signals offers a promising indication of fatigue crack growth.

/pdf/acoustic-emission-monitoring-of-fatigue-crack-growth-in-168ee7gsd1.pdf

Slim Soua

Papers

Determination of the combined vibrational and acoustic emission signature of a wind turbine gearbox and generator shaft in service as a pre-requisite for effective condition monitoring

An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades

A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades

Condition monitoring of a wind turbine drive train based on its power dependant vibrations

Acoustic emission monitoring of fatigue crack growth in mooring chains