Deep Decoupling Convolutional Neural Network for Intelligent Compound Fault Diagnosis

Intelligent compound fault diagnosis of rotating machinery plays a crucial role for the security, high-efficiency, and reliability of modern manufacture machines, but identifying and decoupling the compound fault are still a great challenge. The traditional compound fault diagnosis methods focus on either bearing or gear fault diagnosis, where the compound fault is always regarded as an independent fault pattern in the process of fault diagnosis, and the relationship between the single fault and compound fault is not considered completely. To solve such a problem, a novel method called deep decoupling convolutional neural network is proposed for intelligent compound fault diagnosis. First, one-dimensional deep convolutional neural network is employed as the feature learning model, which can effectively learn the discriminative features from raw vibration signals. Second, multi-stack capsules are designed as the decoupling classifier to accurately identify and decouple the compound fault. Finally, the routing by agreement algorithm and the margin loss cost function are utilized to train and optimize the proposed model. The proposed method is validated by gearbox fault tests, and the experimental results demonstrate that the proposed method can effectively identify and decouple the compound fault.

[1]  Sven Behnke,et al.  Two-layer contractive encodings for learning stable nonlinear features , 2015, Neural Networks.

[2]  Chuang Sun,et al.  Discriminative Deep Belief Networks with Ant Colony Optimization for Health Status Assessment of Machine , 2017, IEEE Transactions on Instrumentation and Measurement.

[3]  Lingli Cui,et al.  Quantitative and Localization Diagnosis of a Defective Ball Bearing Based on Vertical–Horizontal Synchronization Signal Analysis , 2017, IEEE Transactions on Industrial Electronics.

[4]  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.

[5]  Bo-Suk Yang,et al.  Support vector machine in machine condition monitoring and fault diagnosis , 2007 .

[6]  Fei Dong,et al.  Rolling Bearing Fault Diagnosis Using Modified LFDA and EMD With Sensitive Feature Selection , 2018, IEEE Access.

[7]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[8]  Wei Qiao,et al.  A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems , 2015, IEEE Transactions on Industrial Electronics.

[9]  Kwok-Leung Tsui,et al.  Statistical Modeling of Bearing Degradation Signals , 2017, IEEE Transactions on Reliability.

[10]  Geoffrey E. Hinton,et al.  Dynamic Routing Between Capsules , 2017, NIPS.

[11]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[12]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[13]  Sahibsingh A. Dudani.  The Distance-Weighted k-Nearest-Neighbor Rule , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Haidong Shao,et al.  Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine , 2018, Knowl. Based Syst..

[15]  Shaohui Liu,et al.  Bearing Fault Diagnosis Using Fully-Connected Winner-Take-All Autoencoder , 2018, IEEE Access.

[16]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[17]  Yu Zhang,et al.  Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures , 2017, IEEE Access.

[18]  Chuan Li,et al.  Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition , 2018, Energy.

[19]  Yongbo Li,et al.  Early Fault Diagnosis of Rotating Machinery by Combining Differential Rational Spline-Based LMD and K–L Divergence , 2017, IEEE Transactions on Instrumentation and Measurement.

[20]  Sun Guoxi,et al.  A New Incipient Fault Diagnosis Method Combining Improved RLS and LMD Algorithm for Rolling Bearings With Strong Background Noise , 2018, IEEE Access.

[21]  Guolin He,et al.  Semisupervised Distance-Preserving Self-Organizing Map for Machine-Defect Detection and Classification , 2013, IEEE Transactions on Instrumentation and Measurement.

[22]  Fanrang Kong,et al.  Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier , 2013 .

[23]  Weihua Li,et al.  Feature Denoising and Nearest–Farthest Distance Preserving Projection for Machine Fault Diagnosis , 2016, IEEE Transactions on Industrial Informatics.

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

[25]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[26]  René Vinicio Sánchez,et al.  A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis , 2019, IEEE Transactions on Fuzzy Systems.

[27]  Mangesh B. Chaudhari,et al.  Detection of Combined Gear-Bearing Fault in Single Stage Spur Gear Box Using Artificial Neural Network , 2016 .

[28]  Dong Wang,et al.  Brownian motion with adaptive drift for remaining useful life prediction: Revisited , 2018 .

[29]  Peng Chen,et al.  Step-by-Step Fuzzy Diagnosis Method for Equipment Based on Symptom Extraction and Trivalent Logic Fuzzy Diagnosis Theory , 2018, IEEE Transactions on Fuzzy Systems.

[30]  Robert X. Gao,et al.  Machine health monitoring based on locally linear embedding with kernel sparse representation for neighborhood optimization , 2019, Mechanical Systems and Signal Processing.

[31]  Shibin Wang,et al.  Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis , 2016 .

[32]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[33]  Zhibin Zhao,et al.  Sparse Deep Stacking Network for Fault Diagnosis of Motor , 2018, IEEE Transactions on Industrial Informatics.

[34]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

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

[36]  Qingmin Liao,et al.  Margin Loss: Making Faces More Separable , 2018, IEEE Signal Processing Letters.

[37]  Jiangtao Wen,et al.  Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning , 2018, IEEE Transactions on Instrumentation and Measurement.

[38]  Jiafu Wan,et al.  Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications , 2017, IEEE Access.

[39]  Xiaoran Zhu,et al.  Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features , 2012 .

[40]  Lei Zhang,et al.  Regrouping particle swarm optimization based variable neural network for gearbox fault diagnosis , 2018, Journal of Intelligent & Fuzzy Systems.

[41]  Weihua Li,et al.  Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[42]  Yaguo Lei,et al.  Condition monitoring and fault diagnosis of planetary gearboxes: A review , 2014 .

[43]  Peng Chen,et al.  Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery , 2018, IEEE Transactions on Instrumentation and Measurement.

[44]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[45]  Chuang Sun,et al.  Deep Coupling Autoencoder for Fault Diagnosis With Multimodal Sensory Data , 2018, IEEE Transactions on Industrial Informatics.

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

[47]  D. N. Vishwakarma,et al.  Intelligent techniques for fault diagnosis in transmission lines — An overview , 2015, 2015 International Conference on Recent Developments in Control, Automation and Power Engineering (RDCAPE).