In most of previous machine fault diagnosis, the performance of traditional methods was over-dependent on high-quality feature extraction from original signals. Recently, deep learning–based fault recognition methods can successful automatically learn high-level hidden features from measured signals, but a deep neural network has too much hyperparameter tuning and a complicated architecture, so the training process is time-consuming. To address these issues, a novel machine fault diagnosis approach using a recurring broad learning model is reported in this article. This method uses a recurring broad learning model first proposed in this article, which learns high-level hidden features from sensor signals with different working status of a machine effectively and efficiently. Experimental results conducted on two public machine fault datasets verify the accuracy and efficiency of the proposed approach for machine fault diagnosis separately. Compared with many existing mainstream methods, the state-of-the-art framework presented reveals its superiority on both datasets. The accuracy achieves nearly 100% on both bearing dataset (Dataset 1) and gearbox-bearing dataset (Dataset 2). In addition, it can obviously realize faster training than most of the methods reported in this article.
GaoHLiangLChenX, et al. Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization. Chin J Mech Eng2015; 28(1): 96–105.
2.
ChenGQieLZhangA, et al. Improved CICA algorithm used for single channel compound fault diagnosis of rolling bearings. Chin J Mech Eng2016; 29(1): 204–211.
3.
ZhangMTangJZhangX, et al. Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples. Chin J Mech Eng2016; 29(2): 396–405.
4.
YanRGaoRXChenX. Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process2014; 96: 1–15.
5.
GuoRGuoKDongJ. Fault diagnosis for the landing phase of the aircraft based on an adaptive kernel principal component analysis algorithm. Proc IMechE, Part I: J Systems and Control Engineering2015; 229(10): 917–926.
6.
ZhangLXiongGLiuH, et al. Fault diagnosis based on optimized node entropy using lifting wavelet packet transform and genetic algorithms. Proc IMechE, Part I: J Systems and Control Engineering2010; 224(5): 557–573.
7.
BatistaLBadriBSabourinR, et al. A classifier fusion system for bearing fault diagnosis. Expert Syst Appl2013; 40(7): 6788–6797.
8.
LiuRYangBZioE, et al. Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Pr2018; 108: 33–47.
9.
TianJMorilloCAzarianM, et al. Motor bearing fault detection using spectral Kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE T Ind Electron2016; 63(3): 1793–1803.
10.
PandarakoneSMizunoYNakamuraH. Distinct fault analysis of induction motor bearing using frequency spectrum determination and support vector machine. IEEE T Ind Appl2017; 53(3): 3049–3056.
11.
ElforjaniMShanbrS. Prognosis of bearing acoustic emission signals using supervised machine learning. IEEE T Ind Electron2018; 65(7): 5864–5871.
12.
WuJWuCCaoS, et al. Degradation data-driven time-to-failure prognostics approach for rolling element bearings in electrical machines. IEEE T Ind Electron2019; 66(1): 529–539.
13.
HuQQinAZhangQ, et al. Fault diagnosis based on weighted extreme learning machine with wavelet packet decomposition and KPCA. IEEE Sens J2018; 18(20): 8472–8483.
14.
ZhaoXJiaM. Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis. Neurocomputing2018; 315: 447–464.
15.
RazaviRFarajzadehMSaifM. An integrated class-imbalanced learning scheme for diagnosing bearing defects in induction motors. IEEE T Ind Inform2017; 13(6): 2758–2769.
16.
YuXDongFDingE, et al. Rolling bearing fault diagnosis using modified LFDA and EMD with sensitive feature selection. IEEE Access2018; 6: 3715–3730.
17.
SunJYanCWenJ. Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning. IEEE T Instrum Meas2017; 67(1): 185–195.
18.
LeCBengioYHintonG. Deep learning. Nature2015; 521(7553): 436–444.
19.
LuweiKYunusaASha’abanY. Integrated fault detection framework for classifying rotating machine faults using frequency domain data fusion and artificial neural networks. Machines2018; 6(4): 59.
20.
ShaoSSunWYanR, et al. A deep learning approach for fault diagnosis of induction motors in manufacturing. Chin J Mech Eng2017; 30(6): 1347–1356.
21.
SegoviaPBlesaJHorváthK, et al. Modeling and fault diagnosis of flat inland navigation canals. Proc IMechE, Part I: J Systems and Control Engineering2018; 232(6): 761–777.
22.
OhHJungJJeonB, et al. Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis. IEEE T Ind Electron2018; 65(4): 3539–3549.
23.
KulkarniSGuhaA. Equation-based domain knowledge utilization into neural network structure and learning. Proc IMechE, Part I: J Systems and Control Engineering2018; 232(10): 1275–1291.
24.
JiaFLeiYLuN, et al. Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization. Mech Syst Signal Pr2018; 110: 349–367.
25.
DjelloulISariZSidibeI. Fault diagnosis based on the quality effect of learning algorithm for manufacturing systems. Proc IMechE, Part I: J Systems and Control Engineering2019; 233: 801–814.
26.
WangYYangXYanH. Reliable fuzzy tracking control of near-space hypersonic vehicle using aperiodic measurement information. IEEE T Ind Electron2019; 66: 9439–9447.
27.
YaoZYuYYaoJ. Artificial neural network–based internal leakage fault detection for hydraulic actuators: an experimental investigation. Proc IMechE, Part I: J Systems and Control Engineering2018; 232(4): 369–382.
28.
ChenHJiangBChenW, et al. Data-driven detection and diagnosis of incipient faults in electrical drives of high-speed trains. IEEE T Ind Electron2018; 66(6): 4716–4725.
29.
WangYZhouWLuoJ, et al. Reliable intelligent path following control for a robotic airship against sensor faults. IEEE/ASME T Mech2019; 24(6): 2572–2582.
30.
PanJZiYChenJ, et al. LiftingNet: a novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification. IEEE T Ind Electron2018; 65(6): 4973–4982.
31.
DeutschJHeD. Using deep learning-based approach to predict remaining useful life of rotation components. IEEE T Syst Man Cyb2018; 48(1): 11–20.
32.
LiuHZhouJXuY, et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomputing2018; 315: 412–424.
33.
ZhangSZhangSWangB, et al. Deep learning algorithms for bearing fault diagnostics —a comprehensive review. IEEE Access2020; 8: 29857–29881.
34.
ChenCLPLiuZ. Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE T Neur Net Lear2017; 29(1): 10–24.
35.
ChenCLPWanJZ. A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction. IEEE T Syst Man Cy B1999; 29(1): 62–72.
36.
PaoYTakefujiY. Functional-link net computing: theory, system architecture, and functionalities. Computer1992; 25(5): 76–79.
ShaoSYMcAleerSYanR, et al. Highly accurate machine fault diagnosis using deep transfer learning. IEEE T Ind Inform2019; 15(4): 2446–2455.
39.
DuWTaoJLiY, et al. Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mech Syst Signal Pr2014; 43(1-2): 57–75.
40.
JinXZhaoMChowT, et al. Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE T Ind Electron2013; 61(5): 2441–2451.
41.
LiWZhangSHeG. Semisupervised distance-preserving self-organizing map for machine-defect detection and classification. IEEE T Instrum Meas2013; 62(5): 869–879.
42.
ZhangXLiangYZhouJ. A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM. Measurement2015; 69: 164–179.
43.
LeiYJiaFLinJ, et al. An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE T Ind Electron2016; 63(5): 3137–3147.
44.
ZhaoRWangDYanR, et al. Machine health monitoring using local feature-based gated recurrent unit networks. IEEE T Ind Electron2017; 65(2): 1539–1548.
45.
DingXHeQ. Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE T Instrum Meas2017; 66(8): 1926–1935.
46.
WenLGaoLLiX. A new deep transfer learning based on sparse auto-encoder for fault diagnosis. IEEE T Syst Man Cy: S2017; 49(1): 136–144.
47.
LiuJHuYWangY, et al. An integrated multi-sensor fusion-based deep feature learning approach for rotating machinery diagnosis. Meas Sci Technol2018; 29(5): 055103.
48.
ZhaoMDongCDongQ, et al. Question classification of tomato pests and diseases question answering system based on BIGRU. Trans Chin Soc Agricul Mach2018; 5: 32.
