{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T09:52:53Z","timestamp":1768038773836,"version":"3.49.0"},"reference-count":51,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2017,7,4]],"date-time":"2017-07-04T00:00:00Z","timestamp":1499126400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Artificial intelligence (AI) techniques, which can effectively analyze massive amounts of fault data and automatically provide accurate diagnosis results, have been widely applied to fault diagnosis of rotating machinery. Conventional AI methods are applied using features selected by a human operator, which are manually extracted based on diagnostic techniques and field expertise. However, developing robust features for each diagnostic purpose is often labour-intensive and time-consuming, and the features extracted for one specific task may be unsuitable for others. In this paper, a novel AI method based on a deep belief network (DBN) is proposed for the unsupervised fault diagnosis of a gear transmission chain, and the genetic algorithm is used to optimize the structural parameters of the network. Compared to the conventional AI methods, the proposed method can adaptively exploit robust features related to the faults by unsupervised feature learning, thus requires less prior knowledge about signal processing techniques and diagnostic expertise. Besides, it is more powerful at modelling complex structured data. The effectiveness of the proposed method is validated using datasets from rolling bearings and gearbox. To show the superiority of the proposed method, its performance is compared with two well-known classifiers, i.e., back propagation neural network (BPNN) and support vector machine (SVM). The fault classification accuracies are 99.26% for rolling bearings and 100% for gearbox when using the proposed method, which are much higher than that of the other two methods.<\/jats:p>","DOI":"10.3390\/s17071564","type":"journal-article","created":{"date-parts":[[2017,7,4]],"date-time":"2017-07-04T10:31:57Z","timestamp":1499164317000},"page":"1564","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Unsupervised Fault Diagnosis of a Gear Transmission Chain Using a Deep Belief Network"],"prefix":"10.3390","volume":"17","author":[{"given":"Jun","family":"He","sequence":"first","affiliation":[{"name":"The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"The Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shixi","family":"Yang","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"The Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunbiao","family":"Gan","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Fluid Power and Mechatronic Systems, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"The Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province, College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,7,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"10000","DOI":"10.1016\/j.eswa.2011.02.008","article-title":"A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox","volume":"38","author":"Li","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3457","DOI":"10.1177\/0021998313509504","article-title":"Failure modes characterization of impacted carbon fibre reinforced plastics laminates under compression loading using acoustic emission","volume":"48","author":"Arumugam","year":"2013","journal-title":"J. Compos. Mater."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.neucom.2010.02.027","article-title":"Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification","volume":"74","author":"Wong","year":"2010","journal-title":"Neurocomputing"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6207","DOI":"10.3390\/s140406207","article-title":"Enhancement of the Wear Particle Monitoring Capability of Oil Debris Sensors Using a Maximal Overlap Discrete Wavelet Transform with optimized Decomposition Depth","volume":"14","author":"Li","year":"2014","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3757","DOI":"10.1109\/TIE.2015.2417501","article-title":"A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part I: Fault Diagnosis with Model-Based and Signal-Based Approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3768","DOI":"10.1109\/TIE.2015.2417501","article-title":"A Survey of Fault Diagnosis and Fault-Tolerant Techniques-Part II: Fault Diagnosis with Knowledge-Based and Hybrid\/Active Approaches","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1049\/iet-smt.2014.0023","article-title":"Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition","volume":"8","author":"Van","year":"2014","journal-title":"IET Sci. Meas. Tech."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.ymssp.2012.06.015","article-title":"A new procedure for extracting fault feature of multi-frequency signal from rotating machinery","volume":"32","author":"Xiong","year":"2012","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.measurement.2012.07.007","article-title":"Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump","volume":"46","author":"Muralidharan","year":"2013","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/0022-460X(91)90739-7","article-title":"A technique for calculating the time domain averages of the vibration of the individual planet gears and the sun gear in an epicyclic gearbox","volume":"144","author":"McFadden","year":"1991","journal-title":"J. Sound Vib."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1840","DOI":"10.1016\/j.eswa.2007.12.010","article-title":"Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference","volume":"36","author":"Tran","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2005","DOI":"10.3390\/s120202005","article-title":"A method based on multi-sensor data fusion for fault detection of planetary gearboxes","volume":"12","author":"Lei","year":"2012","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.neucom.2015.09.081","article-title":"Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes","volume":"174","author":"Yin","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_14","first-page":"8132528","article-title":"Research on fault diagnosis method based on rule base neural network","volume":"2017","author":"Zheng","year":"2017","journal-title":"J. Control Sci. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1558","DOI":"10.5370\/JEET.2015.10.4.1558","article-title":"Support Vector Machine Based Bearing Fault Diagnosis for Induction Motors Using Vibration Signals","volume":"10","author":"Hwang","year":"2015","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1108\/EC-01-2016-0005","article-title":"Fault diagnosis for rolling bearing based on SIFT-KPCA and SVM","volume":"34","author":"Cheng","year":"2017","journal-title":"Eng. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1016\/j.engappai.2008.07.006","article-title":"Use of particle swarm optimization for machinery fault detection","volume":"22","author":"Samanta","year":"2009","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1007\/s00521-015-1850-y","article-title":"Improved shuffled frog leaping algorithm-based BP neural network and its application in bearing early fault diagnosis","volume":"27","author":"Zhao","year":"2016","journal-title":"Neural Comput. Appl."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.measurement.2016.04.049","article-title":"Wind pressure data reconstruction using neural network techniques: A comparison between BPNN and GRNN","volume":"88","author":"Ni","year":"2016","journal-title":"Measurement"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4602","DOI":"10.3390\/s100504602","article-title":"Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR","volume":"10","author":"Gao","year":"2010","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.measurement.2006.10.010","article-title":"A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM","volume":"40","author":"Yang","year":"2007","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1016\/j.applthermaleng.2016.07.109","article-title":"A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system\u2019s refrigerant charge fault amount","volume":"108","author":"Sun","year":"2016","journal-title":"Appl. Therm. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","article-title":"A review of unsupervised feature learning and deep learning for time-series modeling","volume":"42","author":"Karlsson","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3137","DOI":"10.1109\/TIE.2016.2519325","article-title":"An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data","volume":"63","author":"Lei","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_25","first-page":"1798","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the dimensionality of data with neural networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1109\/MCI.2010.938364","article-title":"Deep machine learning-a new frontier in artificial intelligence research","volume":"5","author":"Arel","year":"2010","journal-title":"IEEE Comput. Intell. Mag."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Jaitly, N., and Hinton, G. (2011, January 22\u201327). Learning a better representation of speech sound waves using restricted Boltzmann machines. Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic.","DOI":"10.1109\/ICASSP.2011.5947700"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","article-title":"Deep Neural Networks for Acoustic Modeling in Speech Recognition","volume":"29","author":"Hinton","year":"2012","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pan, W., and Torresani, L. (2009, January 14\u201318). Unsupervised hierarchical modeling of locomotion styles. Proceedings of the 26th Annual International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553475"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"ImageNet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Memisevic, R., and Hinton, G. (2007, January 17\u201322). Unsupervised learning of image transformations. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383036"},{"key":"ref_35","first-page":"5","article-title":"Sleep stage classification using unsupervised feature learning","volume":"2012","author":"Karlsson","year":"2012","journal-title":"Adv. Artif. Neural Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"0360153","DOI":"10.1088\/1741-2560\/8\/3\/036015","article-title":"Modeling electroencephalography waveforms with semi-supervised deep belief nets: fast classification and anomaly measurement","volume":"8","author":"Wulsin","year":"2011","journal-title":"J. Neural Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.ress.2013.02.022","article-title":"Failure diagnosis using deep belief learning based health state classification","volume":"115","author":"Tamilselvan","year":"2013","journal-title":"Reliab. Eng. Syst. Safe"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.ymssp.2017.03.034","article-title":"A novel deep autoencoder feature learning method for rotating machinery fault diagnosis","volume":"95","author":"Shao","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, C., S\u00e1nchez, R.-V., Zurita, G., Cerrada, M., and Cabrera, D. (2016). Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning. Sensors, 16.","DOI":"10.3390\/s16060895"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4113","DOI":"10.1016\/j.eswa.2013.12.026","article-title":"An approach to fault diagnosis of reciprocating compressor valves using Teager\u2013Kaiser energy operator and deep belief networks","volume":"41","author":"Tran","year":"2014","journal-title":"Expert. Syst. Appl."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.neucom.2017.01.032","article-title":"Deep quantum inspired neural network with application to aircraft fuel system fault diagnosis","volume":"238","author":"Gao","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1016\/j.promfg.2016.08.083","article-title":"Rotating Machinery Diagnostics Using Deep Learning on Orbit Plot Images","volume":"5","author":"Jeong","year":"2016","journal-title":"Procedia Manuf."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","article-title":"Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data","volume":"72","author":"Jia","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. Math. Phys. Eng. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1162\/089976602760128018","article-title":"Training products of experts by minimizing contrastive divergence","volume":"14","author":"Hinton","year":"2002","journal-title":"Neural Comput."},{"key":"ref_47","unstructured":"Hinton, G.E. (2010). A practical guide to training restricted Boltzmann machines. Neural Networks: Tricks of the Trade, Springer."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1016\/j.ymssp.2006.01.007","article-title":"Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble","volume":"21","author":"Hu","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_49","first-page":"16","article-title":"A systematic methodology for gearbox health assessment and fault classification","volume":"2","author":"Siegel","year":"2011","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_50","first-page":"42","article-title":"Information reconstruction method for improved clustering and diagnosis of generic gearbox signals","volume":"2","author":"Wu","year":"2011","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_51","first-page":"32","article-title":"Bearing fault detection with application to PHM Data Challenge. Information reconstruction method for improved clustering and diagnosis of generic gearbox signals","volume":"2","author":"Urevc","year":"2011","journal-title":"Int. J. Progn. Health Manag."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/7\/1564\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:41:26Z","timestamp":1760208086000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/7\/1564"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,4]]},"references-count":51,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2017,7]]}},"alternative-id":["s17071564"],"URL":"https:\/\/doi.org\/10.3390\/s17071564","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,4]]}}}