ABSTRACT
Mechanical equipment is becoming much larger, more precise and more autonomous in current industrial society. The mechanical equipment fault detection is entering the age of 'big data' for much more monitoring points and sampling rate. Traditional diagnosis methods based on "signal processing feature extraction + machine learning classification" require a large amount of signal processing technology and diagnostic experience and can no longer meet the requirements of mechanical 'big data'. To solve this problem, an important part bearing in mechanical equipment is taken as the research object, and a diagnosis method based on convolutional neural network is proposed. This method uses the vibration signal as the monitoring signal and uses the Fourier transform to generate the vibration signal spectrum picture as the input of the whole system. Using the powerful feature extraction capability of convolutional neural network can automatically complete fault feature extraction and fault identification. The results show that the proposed method is able to not only adaptively mine available fault characteristics from the data, but also obtain higher identification accuracy than the existing methods.
- LEI Yaguo, HE Zhengjia. Advances in applications of Hybrid intelligent fault diagnosis and prognosis technique[J]. Journal of Vibration and Shock, 2011, 30(9):129--135.Google Scholar
- LEI Yaguo, JIA Feng, ZHOU Xin, et al. A Deep Learning-based Method for Machinery Health Monitoring with Big Data[J]. Journal of Mechanical Engineering, 2015, 51(21): 49--56Google ScholarCross Ref
- Graham-rowe D, Goldston D, Doctorow C, et al. Big data: Science in the petabyte era[J]. Nature, 2008, 455(7209): 8--9.Google Scholar
- LI Guojie, CHEN Xueqi. Research status and scientific thinking of big data[J]. Bulletin of the Chinese Academy of Sciences, 2012, 27(6): 647--657.Google Scholar
- Hinton G E, Salakhutdinov R R. Reducing the Dimensionality of Data with Neural Networks[J]. Science, 2006, 313(5786):504--507Google ScholarCross Ref
- LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436--444.Google ScholarCross Ref
- Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097--1105.Google Scholar
- Du W, Tao J, Li Y, et al. Wavelet leaders multifractal features based fault diagnosis of rotating mechanism[J] Mechanical Systems & Signal Processing, 2014, 43(1-2):57--75.Google ScholarCross Ref
- Li W, Zhang S, He G. Semisupervised Distance-Preserving Self-Organizing Map for Machine-Defect Detection and Classification[J]. IEEE Transactions on Instrumentation & Measurement, 2013, 62(5):869--879.Google ScholarCross Ref
- Van M, Kang H J. Bearing Defect Classification Based on Individual Wavelet Local Fisher Discriminant Analysis with Particle Swarm Optimization[J]. IEEE Transactions on Industrial Informatics, 2017, 12(1):124--135.Google Scholar
- Worden K, Staszewski W J, Hensman J J. Natural computing for mechanical systems research: A tutorial overview[J]. Mechanical Systems & Signal Processing, 2011, 25(1):4--111.Google ScholarCross Ref
- LEI Yaguo, ZUO M J. Gear crack level identification based on weighted K nearest neighbor classification algorithm[J]. Mechanical Systems and Signal Processing, 2009, 23(5):1535--1547.Google ScholarCross Ref
- G. Hinton, L. Deng, D. Yu, G.E. Dahl, A.R. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T.N. Sainath, B. Kingsbury, Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups, IEEE Signal Process. Magaz. 29 (6) (2012) 82--97.Google ScholarCross Ref
- C. Lu, Z.Y. Wang, W.L. Qin, J. Ma, Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification, Signal Process. 130 (2017) 377--388.Google ScholarDigital Library
- F. Jia, Y. Lei, J. Lin, X. Zhou, N. Lu, Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Process. 72 (2016) 303--315.Google ScholarCross Ref
- L. Guo, H. Gao, H. Huang, X. He, S. Li, Multifeatures fusion and nonlinear dimension reduction for intelligent bearing condition monitoring, Shock Vib.(2016).Google Scholar
- Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE 86 (11) (1998) 2278--2324.Google ScholarCross Ref
Index Terms
- Fault Diagnosis Method of Mechanical Equipment Based on Convolutional Neural Network
Recommendations
Convolutional Neural Network and 2-D Image Based Fault Diagnosis of Bearing without Retraining
ICCDA '19: Proceedings of the 2019 3rd International Conference on Compute and Data AnalysisBearings are vital part of rotary machines. A failure of bearing has a negative impact on schedules, production operation and even human casualties. Therefore, in prior achieving fault diagnosis of bearings is very important. How well features are ...
Rolling Bearing Fault Diagnosis Based on GWVD and Convolutional Neural Network
Advanced Intelligent Computing Technology and ApplicationsAbstractIn order to solve the problems that the traditional signal processing method cannot deal with non-Euclidian data, a graph Wigner-Ville distribution (GWVD) and convolutional neural network (CNN) based rolling bearing fault diagnosis method is ...
Mechanical equipment fault diagnosis based on redundant second generation wavelet packet transform
Wavelet transform has been widely used for the vibration signal based mechanical equipment fault diagnosis. However, the decomposition results of the discrete wavelet transform do not possess time invariant property, which may result in the loss of ...
Comments