A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions

Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time performance, effectiveness, and generalization performance. In this paper, a deep learning method based on stacked residual dilated convolutional neural network (SRDCNN) is proposed for real-time bearing fault diagnosis, which is subtly combined by the dilated convolution, the input gate structure of long short-term memory network (LSTM) and the residual network. In the SRDCNN model, the dilated convolution is used to exponentially increase the receptive field of convolution kernel and extract features from the sample with more points, alleviating the influence of randomness. The input gate structure of LSTM could effectively remove noise and control the entry of information contained in the input sample. Meanwhile, the residual network is introduced to overcome the problem of vanishing gradients caused by the deeper structure of the neural network, hence improving the overall classification accuracy. The experimental results indicate that compared with three excellent models, the proposed SRDCNN model has higher denoising ability and better workload adaptability.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[3]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[4]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[5]  Robert B. Randall,et al.  Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram , 2009 .

[6]  Jay Lee,et al.  A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis , 2011, Expert Syst. Appl..

[7]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[8]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[9]  Jin Chen,et al.  Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings , 2012 .

[10]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[11]  Vijanth S. Asirvadam,et al.  An on-line condition monitoring system for induction motors via instantaneous power analysis , 2015 .

[12]  Vladlen Koltun,et al.  Multi-Scale Context Aggregation by Dilated Convolutions , 2015, ICLR.

[13]  Alex Graves,et al.  Neural Machine Translation in Linear Time , 2016, ArXiv.

[14]  Heiga Zen,et al.  WaveNet: A Generative Model for Raw Audio , 2016, SSW.

[15]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[16]  Miao He,et al.  Deep Learning Based Approach for Bearing Fault Diagnosis , 2017, IEEE Transactions on Industry Applications.

[17]  Sander Bohte,et al.  Conditional Time Series Forecasting with Convolutional Neural Networks , 2017, 1703.04691.

[18]  Samy Bengio,et al.  Understanding deep learning requires rethinking generalization , 2016, ICLR.

[19]  Ming Zhao,et al.  A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .

[20]  Chen Lu,et al.  Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification , 2017, Adv. Eng. Informatics.

[21]  Wei Zhang,et al.  A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals , 2017, Sensors.

[22]  Qiang Miao,et al.  Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis , 2018 .

[23]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[24]  Tielin Shi,et al.  Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings , 2018, Sensors.

[25]  Faramarz Khoshnoudian,et al.  Seismic Fragility Assessment of Asymmetric Structures Supported on TCFP Bearings Subjected to Near-field Earthquakes , 2018 .

[26]  Aryan Rezaei Rad,et al.  Probabilistic Risk-Based Performance Evaluation of Seismically Base-Isolated Steel Structures Subjected to Far-Field Earthquakes , 2018, Buildings.

[27]  Fanming Meng,et al.  An Improved Bearing Fault Diagnosis Method using One-Dimensional CNN and LSTM , 2018, Strojniški vestnik - Journal of Mechanical Engineering.

[28]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

[29]  Yan Han,et al.  An enhanced convolutional neural network with enlarged receptive fields for fault diagnosis of planetary gearboxes , 2019, Comput. Ind..

[30]  Huijun Gao,et al.  A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis , 2019, Neurocomputing.