Self-Supervised Joint Learning Fault Diagnosis Method Based on Three-Channel Vibration Images

Abstract

The accuracy of bearing fault diagnosis is of great significance for the reliable operation of rotating machinery. In recent years, increasing attention has been paid to intelligent fault diagnosis techniques based on deep learning. However, most of these methods are based on supervised learning with a large amount of labeled data, which is a challenge for industrial applications. To reduce the dependence on labeled data, a self-supervised joint learning (SSJL) fault diagnosis method based on three-channel vibration images is proposed. The method combines self-supervised learning with supervised learning, makes full use of unlabeled data to learn fault features, and further improves the feature recognition rate by transforming the data into three-channel vibration images. The validity of the method was verified using two typical data sets from a motor bearing. Experimental results show that this method has higher diagnostic accuracy for small quantities of labeled data and is superior to the existing methods.

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

“…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 the class "Others", in Table 4, some particular methods, which do not belong to the other classes, are collected. With reference to the problem of labels, Zhang et al in [267] propose a method that combines self-supervised learning with supervised learning, making full use of unlabeled data to learn fault features, and transforms the data into three-channel vibration images. Stefanoiu et al,in [265], present a method based on the matching pursuit algorithm (MPA), a new finding in signal processing, which proves the possibility of performing fault diagnosis of bearings, even in case of multiple defects.…”

“…In the table, the nomenclature for categories and sub-categories of phases, components, and topics reflect the previously introduced legend. Bajaj_2022 [276] x p3 x Xu_2022 [277] x p4 x x Ye_2021a [244] x p3 x Ahmed_2021 [172] x x p2 x x Mufazzal_2021 [278] x p4 x Yang_2021 [110] x p2 x x x x x Moghadam_2021 [193] x p2 x x Saucedo-Dorantes_2021 [173] x p2 x x x x Espinoza-Sepulveda_2021 [279] x p4 x x Kalista_2021 [73] x p1 x x x Zhang_2021a [280] x p4 x x Zhang_2021b [267] x p3 x x Meng_2021 [59] x p1 x x Tiwari_2021 [95] x p1 x x Tatsis_2021 [281] x p4 x x Leaman_2021 [282] x p5 x x Ou_2021 [283] x p5 x x Espinoza_2021 [226] x x p3 x x Wang_2021 [233] x p3 x x x Goyal_2021 [60] x p1 x x Bai_2021a [203] x p2 x x x Yu_2021 [75] x p1 x x Papathanasopoulos_2021 [66] x p1 x x Sharma_2021 [25] x p1 x x x Rauber_2021 [219] x p3 x x Laval_2021 [76] x p1 x x x x Shao_2021 [158] x x p2 x x Rafiq_2021 [166] x p2 x x x x Zhao_2021 [77] x p1 x x Gómez_2021 [284] x p1 x x Jablon_2021 [197] x p2 x x Barusu_2021 [62] x p1 x x x x Hadroug_2021 [250] x p3 x Hou_2021 [35] x x p1 x x Yuan_2021 [285] x p5 x Ye_2021b [245] x p3 x x x Tingarikar_2021 [286] x p4 x Ribeiro_2021 [287] x p2 x Peng_2021 [288] x x p2 x x x Gu_2021 …”

Generative Adversarial Network-Based Scheme for Diagnosing Faults in Cyber-Physical Power Systems

“…In [ 16 ], the authors have used the infrared thermography and infrared image processing in order to generate a useful set of features to evaluate the working condition of angle grinders through a method called BCAoMID-F. The implementation of GANs for self-supervised [ 17 ], semi-supervised [ 18 ], and unsupervised [ 12 ] fault diagnosis schemes have also been extensively studied. In [ 19 ], the authors adapt a framework to generate knockoffs, which are random variables that mimic the correlation structure within the existing set of variables in a way that provides a mechanism for the accurate control of the false discovery rate.…”