In this article, deep neural network and stacked sparse auto-encoder deep neural network performances in fault diagnosis are compared. Methods are employed experimentally for the detection and isolation of an induction motor’s condition (healthy, bearing outer race fault, stator winding short circuit, and rotor broken bar) in the presence of unbalanced power supply and pump dry running disturbances. Pre-processing and de-noising is performed on three-phase electrical current signals using fast Fourier transform and independence component analysis algorithm, respectively. Experimental results show that sparse auto-encoder deep neural network method has outperformed and diagnosed the aforementioned faults in the presence of disturbances with a highly reliable accuracy rate of 90.65%.
ShenCWangDKongF, et al. Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement2013; 46(4): 1551–1564.
2.
WangDTsePWGuoW, et al. Support vector data description for fusion of multiple health indicators for enhancing gearbox fault diagnosis and prognosis. Meas Sci Technol2010; 22(2): 025102.
3.
WangDTsePW. Prognostics of slurry pumps based on a moving-average wear degradation index and a general sequential Monte Carlo method. Mech Syst Signal Pr2015; 56–57: 213–229.
4.
TranVTYangB-SGuF, et al. Thermal image enhancement using bi-dimensional empirical mode decomposition in combination with relevance vector machine for rotating machinery fault diagnosis. Mech Syst Signal Pr2013; 38(2): 601–614.
5.
WidodoAKimEYSonJ-D, et al. Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine. Expert Syst Appl2009; 36(3): 7252–7261.
6.
Tarkesh EsfahaniE. Multi-sensor wireless system for fault detection in induction motors. IEEE/ASME Trans Mechatron2012; 19(3): 818–826.
7.
JafariHPoshtanJSadeghiH. Application of fuzzy data fusion theory in fault diagnosis of rotating machinery. Proc IMechE, Part I: J Systems and Control Engineering2018; 232(8): 1015–1024.
8.
LiuRYangBZioE, et al. Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Pr2018; 108: 33–47.
9.
AbedWSharmaSSuttonR. Neural network fault diagnosis of a trolling motor based on feature reduction techniques for an unmanned surface vehicle. Proc IMechE, Part I: J Systems and Control Engineering2015; 13: 558–571.
10.
RafieeJArvaniFHarifiA, et al. Intelligent condition monitoring of a gearbox using artificial neural network. Mech Syst Signal Pr2007; 21: 1746–1754.
11.
PatelTHDarpeAK. Vibration response of a cracked rotor in presence of rotor–stator rub. J Sound Vib2008; 317: 841–865.
12.
ChandraNHSekharA. Fault detection in rotor bearing systems using time frequency techniques. Mech Syst Signal Pr2016; 72: 105–133.
13.
SadeghianAYeZWuB. Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Trans Instrum Meas2009; 58: 2253–2263.
14.
CunhaRHNunesIGoedtelA, et al. A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electr Pow Syst Res2015; 127: 249–258.
15.
TranVTThobianiFAAndrewBall. An approach to fault diagnosis of reciprocating compressor valves using Teager–Kaiser energy operator and deep belief networks. Expert Syst Appl2014; 41(9): 4113–4122.
16.
RaiPLondheNDRajR. Fault classification in power system distribution network integrated with distributed generators using CNN. Electr Pow Syst Res2020; 13: 106914.
17.
ThirukovalluruRDixitSSevakulaR, et al. Generating feature sets for fault diagnosis using denoising stacked auto-encoder. In: Proceedings of 2016 IEEE conference on prognostics and health management, Ottawa, ON, Canada, 20–22 June 2016, pp.1–7. New York: IEEE.
18.
JiaFLeiYZhouJLX, et al. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Pr2016; 72–73: 303–315.
19.
JiangNHuXLiN. Graphical temporal semi-supervised deep learning-based principal fault localization in wind turbine systems. Proc IMechE, Part I: J Systems and Control Engineering2020; 15(11): 990–1005.
20.
ChenZLiW. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Trans Instrum Meas2017; 66(7): 1693–1702.
21.
MaoWHeJLi Yand YanY. Bearing fault diagnosis with auto-encoder extreme learning machine: a comparative study. Proc IMechE, Part C: J Mechanical Engineering Science2017; 231: 1560–1578.
22.
QiYShenCLiuJ, et al. An automatic feature learning and fault diagnosis method based on stacked sparse autoencoder. Lect Notes Electr Eng2018; 451: 367–375.
23.
SunWShaoSZhaoR, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement2016; 89: 171–178.
24.
ZhaoXJiaMLinM. Deep Laplacian auto-encoder and its application into imbalanced. Measurement2019; 76: 107320.
25.
BinGFGaoJJLiXJ, et al. Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mech Syst Signal Pr2012; 27: 696–711.
26.
BengioY. Learning deep architectures for AI. Found Trends Mach Learn2009; 2: 1–127.
27.
DengL. A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process2014; 3: e2.
28.
WangZLiuXZengN, et al. Intelligent fault diagnosis of rotating machinery using a new ensemble deep auto-encoder method. Measurement2020; 151: 7232–7248.
29.
ZhuHChengJZhangC, et al. Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings. Appl Soft Comput2020; 88: 1060–1071.
30.
JorkeshSPoshtanJ. Fault diagnosis of an induction motor using data fusion based on neural networks. IET Sci Meas Technol2021; 15(8): 681–689
