A Review on Vibration-Based Fault Diagnosis Techniques for Wind Turbine Gearboxes Operating Under Nonstationary Conditions

Abstract

Numerous studies on vibration-based techniques for fault diagnosis of wind turbine gearboxes operating under nonstationary conditions have been reported. In spite, a review on vibration-based condition monitoring and fault diagnosis techniques for gearboxes of wind turbines operating under nonstationary conditions is unavailable. Thus, the objective of this review is to discuss filtering, decomposition, entropy, and cyclostationary analysis techniques, as well as summarizing the remaining issues. This review will discuss various vibration-based diagnostic approaches developed for wind turbine gearboxes under nonstationary conditions, including both simulation and experimental approaches. Studies on dynamic models of gear systems and advanced signal-processing techniques developed for nonstationary conditions are reviewed. Additionally, the importance of multi-sensor and cointegration based approaches is discussed, and intelligent classification methods that have been used to distinguish healthy and faulty gear systems are also reviewed. Finally, the remaining research challenges are outlined.

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

“…As emerges through the analyses and as confirmed in other literature reviews [27], ANNs [208], DNNS, and SVMs are the most investigated methods, and among them DNNs have been dominant in the last years. Cointegration method Sharma_2021 * [25] Support Vector Machine (SVMs) Moosavian_2012 [214] Stetco_2019 * [27] Fei_2014 [215] Sharma_2021 * [25] Linear kernel Vives_2020 [216] Agrawal_2019 [217] Hwang_2015 [218] Hwang_2015 [218]…”

“…Wang_2021 [233] Rauber_2021 [219] Fan_2021 [234] Qian_2020 [235] Zhao_2020a [236] Chen_2020 [237] Li_2020 [238] Xin_2020 [239] Li_2019 [240] Hoang_2019 [241] Qian_2018 [242] Qian_2018 [242] Bai_2021b [243] Sharma_2021 * [25]…”

“…Rauber_2021 [219] Samuel_2005 * [33] Vives_2020 [216] Random Forest Rauber_2021 [219] Li_2016b [249] Fuzzy predictive model Hadroug_2021 [250] Malla_2019 * [3] Str ączkiewicz_2015 [251] Saravanan_2009 [252] Da Silva_2017 [253] Sharma_2021 * [25] Decision Trees (DTs) Lipinski_2020 [254] Joshuva_2017a [112] Tabaszewski_2020 [255] Yang_2005 [256] Yang_2000 [257] Song_2018 [181] Dempster-Shafer (D-S) evidence theory Khazaee_2014 [258] Khazaee_2012 [259] Multi-Sensor Data fusion Safizadeh_2014 [260] Khazaee_2012 [259] Stief_2017 [261] Sharma_2021 * [25] Hybrid classifier based on SVM and ANN Sharma_2021 * [25] Hybrid classifier based on Principal Component Analysis (PCA) and ANN Liu_2008 [262] Devendiran_2015 [104] De Moura_2011 [263] Bendjama_2010 [264] Others Stefanoiu_2019 [265] Yan_2019 [199] Liu_2014 [266] Zhang_2021b [267] Avendaño-Valencia_2017 [268] Jayaswal and Wadhwani, in 2009 [31], reviewed the techniques successfully implemented for the automated fault diagnosis of bearings until that time, and refer to expert systems developed with multilayer perceptron (MLP), radial basis function (RBF) and probabilistic neural network (PNN). More recently, Tao et al, in 2019 [222], adopted a multilayer gated recurrent unit (MGRU) method for gear fault diagnosis; a comparison with long short-term memory (LSTM), multilayer LSTM (MLSTM), and support vector machine (SVM) LSTM, MLSTM, GRU, and SVM models, based on an experimental analysis, revealed improved accuracy with the MGRU network.…”

“…In addition to the models that fall into these three main classes, there are ML models based on the methods K-nearest-neighbor (KNN), decision trees (DTs), fuzzy predictive models, random forest. Hybrid solutions, e.g., classifier based on SVMs and ANNs [25] or neuro-fuzzy models have been investigated. Furthermore, in some studies, multi-sensorbased approaches are used (for example by combining vibration and sound measurements), using data fusion methods.…”

Fault Diagnosis Algorithm Based on Adjustable Nonlinear PI State Observer and Its Application in UAV Fault Diagnosis

“…Because fault estimation is often the key link of fault-tolerant control and fault regulation system, the quality of fault estimation directly affects the control accuracy and the control performance of the whole system. At present, researchers have done a lot of research on fault diagnosis methods, including data-based fault diagnosis algorithm and model-based fault diagnosis algorithm [1].…”