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Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning
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School of Mechanical Engineering and Automation Northeastern University 3-11 Wenhua Road, Heping District Shenyang, 110819, China
Department of Mechanical and Industrial Engineering University of Illinois at Chicago 842 West Taylor Street, Chicago, IL 60607, USA
School of Mechanical and Electronic Engineering Wuhan University of Technology 122 Luoshi Road,Wuhan, 430070, China
Publication date: 2019-09-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2019;21(3):403–410
Gear pitting fault is one of the most common faults in mechanical transmission. Acoustic emission (AE) signals have been effective for gear fault detection because they are less affected by ambient noise than traditional vibration signals. To overcome the problem of low gear pitting fault recognition rate using AE signals and convolutional neural networks, this paper proposes a new method named augmented convolution sparse autoencoder (ACSAE) for gear pitting fault diagnosis using raw AE signals. First, the proposed method combines sparse autoencoder and one-dimensional convolutional neural networks for unsupervised learning and then uses the reinforcement theory to enhance the adaptability and robustness of the network. The ACSAE method can automatically extract fault features directly from the original AE signals without time and frequency domain conversion of the AE signals. AE signals collected from gear test experiments are used to validate the ACSAE method. The analysis result of the gear pitting fault test shows that the proposed method can effectively performing recognition of the gear pitting faults, and the recognition rate reaches above 98%. The comparative analysis shows that in comparison with fully-connected neural networks, convolutional neural networks, and recurrent neural networks, the ACSAE method has achieved a better diagnostic accuracy for gear fitting faults.
Ali Y H, Abd Rahman R, Hamzah R I R. Artificial Neural Network Model for Monitoring Oil Film Regime in Spur Gear Based on Acoustic Emission Data. Shock and Vibration, 2015,
Aouabdi S, Taibi M, Bouras S, Boutasseta N. Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis. Mechanical Systems and Signal Processing 2017; 90: 298-316,
Bafroui H H, Ohadi A. Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions. Neurocomputing 2014; 133: 437-445,
Bangalore P, Tjernberg L B. An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings. Ieee Transactions on Smart Grid 2015; 6: 980-987,
Cao P, Zhang S L, Tang J. Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning. Ieee Access 2018; 6: 26241-26253,
Chen X H, Cheng G, Li H Y, Li Y. Fault identification method for planetary gear based on DT-CWT threshold denoising and LE. Journal of Mechanical Science and Technology 2017; 31: 1035-1047,
Crivelli D, McCrory J, Miccoli S, Pullin R, Clarke A. Gear tooth root fatigue test monitoring with continuous acoustic emission: Advanced signal processing techniques for detection of incipient failure. Structural Health Monitoring-an International Journal 2018; 17: 423-433,
Elforjani M, Shanbr S. Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning. Ieee Transactions on Industrial Electronics 2018; 65: 5864-5871,
Feng Z P, Liang P M. Fault diagnosis of wind turbine planetary gearbox under nonstationary conditions via adaptive optimal kernel time frequency analysis. Renewable Energy 2014; 66: 468-477,
Feng Z P, Liang P M, Zhang Y, Hou S M. Fault diagnosis for wind turbine planetary gearboxes via demodulation analysis based on ensemble empirical mode decomposition and energy separation. Renewable Energy 2012; 47: 112-126,
Guo S, Yang T, Gao W, Zhang C. A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network. Sensors 2018; 18(5): 1429-1444,
He K M, Zhang X, Ren S, Sun J. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference 2015; 1026-1034,
He M, He D. Deep Learning Based Approach for Bearing Fault Diagnosis. Ieee Transactions on Industry Applications 2017; 53: 3057-3065,
Krishnakumari A, Elayaperumal A, Saravanan M, Arvindan C. Fault diagnostics of spur gear using decision tree and fuzzy classifier. International Journal of Advanced Manufacturing Technology 2017; 89: 3487-3494,
Liu X, Jia Y X, He Z W, Zhou J. Application of EMD-WVD and particle filter for gearbox fault feature extraction and remaining useful life prediction. Journal of Vibroengineering 2017; 19: 1793-1808,
Li Z, Ma Z Y, Liu Y B, Teng W, Jiang R. Crack Fault Detection for a Gearbox Using Discrete Wavelet Transform and an Adaptive Resonance Theory Neural Network. Strojniski Vestnik-Journal of Mechanical Engineering 2015; 61: 63-73,
Lu C, Wang Z Y, Qin W L, Ma J. Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process 2017; 130: 377-388,
Praveenkumar T, Saimurugan M, Krishnakumar P, Ramachandran K I. Fault diagnosis of automobile gearbox based on machine learning techniques. 12th Global Congress on Manufacturing and Management 2014; 97: 2092-2098,
Qu Y Z, He M, Deutsch J, He D. Detection of Pitting in Gears Using a Deep Sparse Autoencoder. Applied Sciences-Basel 2017; 7(5): 515-529,
Qu Y Z, Zhu J D, He D, Qiu B, Bechhoefer E. Development of a New Acoustic Emission Based Fault Diagnosis Tool for Gearbox. 2013 Ieee International Conference on Prognostics and Health Management, 2013; 1-9,
Ratni A, Rahmoune C, Benazzouz D. A new method to enhance of fault detection and diagnosis in gearbox systems. Journal of Vibroengineering 2017; 19: 176-188,
Shao R P, Hu W T, Wang Y Y, Qi X K. The fault feature extraction and classification of gear using principal component analysis and kernel principal component analysis based on the wavelet packet transform. Measurement 2014; 54: 118-132,
Sharma R B, Parey A. Modelling of acoustic emission generated due to pitting on spur gear. Engineering Failure Analysis 2018; 86: 1-20,
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
Song M M, Xiao S G. A Fault Diagnosis Method of Gear Based on SVD and Improved EEMD. Intelligent Computing, Networked Control, and Their Engineering Applications 2017; 762: 65-74,
Sreepradha C, Kumari A K, Perumal A E, Panda R C, Harshabardhan K, Aribalagan M. Neural network model for condition monitoring of wear and film thickness in a gearbox. Neural Computing & Applications 2014; 24: 1943-1952,
Widodo A, Satrijo D, Prahasto T, Haryanto I. Fault Detection of Gearbox Using Time-Frequency Method. 7th International Conference on Mechanical and Manufacturing Engineering 2017; 1831,
Yang Z, Jing H. A deep learning method based on hybrid auto-encoder model. Technology, Networking, Electronic and Automation Control Conference 2017; 1100-1104,
Yao Z T, Hu Y C. Gearbox fault diagnosis based on LMD and Cyclostationary Demodulation. 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence 2016; 984-989.
Yuan J, Ji F, Gao Y, Zhu J, Wei C J, Zhou Y. Integrated ensemble noise-reconstructed empirical mode decomposition for mechanical fault detection. Mechanical Systems and Signal Processing 2018; 104: 323-346,
Zhang Q, Zhao W, Xiao S G. Fault Diagnosis of Gear Based on Singular Value Decomposition and RBF Neural Network. 2017 2nd International Conference on Frontiers of Sensors Technologies 2017; 470-474.
Zhang Q, Zhao W, Xiao S G, Song M M. Method of Gear Fault Diagnosis Based on EEMD and Improved Elman Neural Network. Materials Science, Energy Technology, and Power Engineering I 2017; 1839(1): 020111,
Zhao R, Wang D Z, Yan R Q, Mao K Z, Shen F, Wang J J. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks. Ieee Transactions on Industrial Electronics 2018; 65: 1539-1548,
Zhou Y, Lin L, Wang D, He M, He D. A new method to classify railway vehicle axle fatigue crack AE signal. Applied Acoustics 2018; 131:174-185,
Zuber N, Bajric R, Sostakov R. Gearbox Faults Identification Using Vibration Signal Analysis and Artificial Intelligence Methods. Eksploatacja I Niezawodnosc-Maintenance and Reliability 2014; 16: 61-65.
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