End-to-end CNN + LSTM deep learning approach for bearing fault diagnosis

Fault diagnostics and prognostics are important topics both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering potential faults in its early stages. Intelligent fault diagnosis is a promising tool due to its ability to rapidly and efficiently processing collected signals and providing accurate diagnosis results. Although many studies have developed machine leaning (M.L) and deep learning (D.L) algorithms for detecting the bearing fault, the results have generally been limited to relatively small train and test datasets and the input data has been manipulated (selective features used) to reach high accuracy. In this work, the raw data, collected from accelerometers (time-domain features) are taken as the input of a novel temporal sequence prediction algorithm to present an end-to-end method for fault detection. We use equivalent temporal sequences as the input of a novel Convolutional Long-Short-Term-Memory Recurrent Neural Network (CRNN) to detect the bearing fault with the highest accuracy in the shortest possible time. The method can reach the highest accuracy in the literature, to the best knowledge of the authors of the present paper, voiding any sort of pre-processing or manipulation of the input data. Effectiveness and feasibility of the fault diagnosis method are validated by applying it to two commonly used benchmark real vibration datasets and comparing the result with the other intelligent fault diagnosis methods.

[1]  Jieping Ye,et al.  Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction , 2018, AAAI.

[2]  Iqbal Gondal,et al.  Inchoate Fault Detection Framework: Adaptive Selection of Wavelet Nodes and Cumulant Orders , 2012, IEEE Transactions on Instrumentation and Measurement.

[3]  Yoshimatsu Osamu,et al.  Rolling Bearing Diagnosis Based on Deep Learning Enhanced by Various Dataset Training , 2018 .

[4]  Serkan Kiranyaz,et al.  A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier , 2018, Journal of Signal Processing Systems.

[5]  Hermann Ney,et al.  LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.

[6]  A.H. Bonnett,et al.  Increased Efficiency Versus Increased Reliability , 2008, IEEE Industry Applications Magazine.

[7]  Onur Avci,et al.  1-D Convolutional Neural Networks for Signal Processing Applications , 2019, ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  H. Ishida,et al.  Application of Convolutional Long Short-Term Memory Neural Networks to Signals Collected from a Sensor Network for Autonomous Gas Source Localization in Outdoor Environments , 2018, Sensors.

[9]  Tara N. Sainath,et al.  Convolutional, Long Short-Term Memory, fully connected Deep Neural Networks , 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Wentao Mao,et al.  A novel deep output kernel learning method for bearing fault structural diagnosis , 2019, Mechanical Systems and Signal Processing.

[11]  Aihua Zhu,et al.  Bearing Fault Diagnosis Based on a Hybrid Classifier Ensemble Approach and the Improved Dempster-Shafer Theory , 2019, Sensors.

[12]  Andrew W. Senior,et al.  Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.

[13]  Peter Vrancx,et al.  Convolutional Neural Networks for Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control , 2016, IEEE Transactions on Smart Grid.

[14]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[15]  Alberto Bellini,et al.  Diagnosis of Bearing Faults in Induction Machines by Vibration or Current Signals: A Critical Comparison , 2008, IEEE Transactions on Industry Applications.

[16]  Chandrasekhar Nataraj,et al.  Use of particle swarm optimization for machinery fault detection , 2009, Eng. Appl. Artif. Intell..

[17]  Yong Liu,et al.  Simplifying Long Short-Term Memory for Fast Training and Time Series Prediction , 2019, Journal of Physics: Conference Series.

[18]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[19]  J. Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM networks , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

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

[21]  Gang Wang,et al.  Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks , 2015, IEEE Transactions on Image Processing.

[22]  Michael J. Devaney,et al.  Bearing damage detection via wavelet packet decomposition of the stator current , 2004, IEEE Transactions on Instrumentation and Measurement.

[23]  Ping-Huan Kuo,et al.  A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities , 2018, Sensors.

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

[25]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[26]  Xiuping Jia,et al.  Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Onur Avci,et al.  1D Convolutional Neural Networks and Applications: A Survey , 2019, Mechanical Systems and Signal Processing.

[28]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[29]  Ran Zhang,et al.  Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence , 2017, Sensors.

[30]  T.G. Habetler,et al.  Motor bearing damage detection using stator current monitoring , 1994, Proceedings of 1994 IEEE Industry Applications Society Annual Meeting.

[31]  ImageNet Classification with Deep Convolutional Neural , 2013 .

[32]  Wei Zhou,et al.  Bearing Fault Detection Via Stator Current Noise Cancellation and Statistical Control , 2008, IEEE Transactions on Industrial Electronics.

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[35]  Xiaobin Zhang,et al.  A Combination of RNN and CNN for Attention-based Relation Classification , 2018 .

[36]  Walter Sextro,et al.  Condition Monitoring of Bearing Damage in Electromechanical Drive Systems by Using Motor Current Signals of Electric Motors: A Benchmark Data Set for Data-Driven Classification , 2016, PHM Society European Conference.

[37]  Chenglin Wen,et al.  Deep learning fault diagnosis method based on global optimization GAN for unbalanced data , 2020, Knowl. Based Syst..

[38]  Gang Wang,et al.  Learning Contextual Dependencies with Convolutional Hierarchical Recurrent Neural Networks , 2015, ArXiv.

[39]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .