A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings

Intelligent fault diagnosis of bogie is fundamental for the reliability, stability, and security of a high-speed train (HST). However, thorny issues, including complicated structure of bogie and complex motion states of HST, aggravate the difficulty of research, where traditional approaches for fault diagnosis might fail. In this article, CapsNet, a contemporary novel neural network architecture, in which a single unit called capsule known for its uniqueness of multidimension and abundant spatial information, is adopted to accomplish the recognition and classification of seven working conditions of a HST bogie, comprising both single and compound faults. The experimental accuracy achieved is 96.65%, which proves the efficiency and potency of CapsNet in this regard. The ability of CapsNet to diagnose faults of a bogie in this article exceeds that of a convolutional neural network (CNN), especially for compound ones. Moreover, the method employed is capable of extracting features from raw data automatically and is independent of expert experience or knowledge about signal processing.

Fault Diagnosis of High-Speed Train Bogie Based on Capsule Network

Wind power, as a renewable energy for coping with global climate change challenge, has achieved rapid development in recent years. The breakdown of wind turbines (WTs) not only leads to high repair expenses but also may threaten the stability of the whole power grid. How to reduce the operation and the maintenance (O&M) cost of wind farms is an obstacle to its further promotion and application. To provide reliable condition monitoring and fault diagnosis (CMFD) for WTs, this paper presents a comprehensive survey of the existing CMFD methods in the following three aspects: energy flow, information flow, and integrated O&M system. Energy flow mainly analyzes the characteristics of each component from the angle of energy conversion of WTs. Information flow is the carrier of fault and control information of WT. At the end of this paper, an integrated WT O&M system based on electrical signals is proposed.

https://www.mdpi.com/1996-1073/12/14/2801/pdf

A Survey of Condition Monitoring and Fault Diagnosis toward Integrated O&M for Wind Turbines

Robust and reliable drivetrain is important for preventing electromechanical (e.g., wind turbine) downtime. In recent years, advanced machine learning (ML) techniques including deep learning have been introduced to improve fault diagnosis performance for electromechanical systems. However, electromechanical systems (e.g., wind turbine) operate in varying working conditions, meaning that the distribution of the test data (in the target domain) is different from the training data used for model training, and the diagnosis performance of an ML method may become downgraded for practical applications. This paper proposes a joint distribution optimal deep domain adaptation approach (called JDDA) based auto-encoder deep classifier for fault diagnosis of electromechanical drivetrains under the varying working conditions. First, the representative features are extracted by the deep auto-encoder. Then, the joint distribution adaptation is used to implement the domain adaptation, so the classifier trained with the source domain features can be used to classify the target domain data. Lastly, the classification performance of the proposed JDDA is tested using two test-rig datasets, compared with three traditional machine learning methods and two domain adaptation approaches. Experimental results show that the JDDA can achieve better performance compared with the reference machine learning, deep learning and domain adaptation approaches.

/pdf/fault-diagnosis-for-electromechanical-drivetrains-using-a-3p1i4sw3ia.pdf

Fault Diagnosis for Electromechanical Drivetrains Using a Joint Distribution Optimal Deep Domain Adaptation Approach

Fault detection of wind turbines using SCADA data and genetic algorithm-based ensemble learning

The total installed capacity of wind turbines is continuously increasing with focus on Renewable Energy Sources (RES). The reliability and efficiency of wind turbines has become a major issue. The occurrence of fault in various components of wind turbine needs to be addressed for improved performance. Among these issues, wind turbine bearing fault is the most significant. Different techniques such as online monitoring using Artificial Intelligence (AI), have been proposed and still research is being carried out in this domain. This paper presents a fault diagnosis analysis of main shaft bearings of wind turbines. The goal is to monitor the condition of wind turbines for early fault prediction so that the turbine can immediately be adjusted for improved performance and extended service life. Different techniques such as Fast Fourier transform (FFT), Hilbert Huang Transformation (HHT), Feature extraction, Logistic Regression (LR) are applied on a real data set of 18 wind turbines to accurately evaluate the health of the turbine. The results highlight the superior and reliable performance of these techniques for bearing fault detection for cost effective operation and maintenance (O&M).

Logistic regression and feature extraction based fault diagnosis of main bearing of wind turbines

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Logistic regression and feature extraction based fault diagnosis of main bearing of wind turbines