Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization

Abstract Deep learning has attracted attentions in intelligent fault diagnosis of machinery because it allows a deep network to accomplish the tasks of feature learning and fault classification automatically. Among deep learning models, convolutional neural networks (CNNs) are able to learn features from mechanical vibration signals and thus several studies have applied CNNs in intelligent fault diagnosis of machinery. However, these studies suffer from the following weaknesses. (1) The imbalanced distribution of machinery health conditions is not considered. (2) What CNNs have learned is not clear. Therefore, in this paper, a framework called deep normalized convolutional neural network (DNCNN) is proposed for imbalanced fault classification of machinery to overcome the first weakness. Meanwhile, neuron activation maximization (NAM) algorithm is developed to handle the second weakness. To verify the proposed methods, three bearing datasets containing single faults and compound faults are constructed with different imbalanced degrees. The classification accuracies of the three datasets demonstrate that DNCNN is able to deal with the imbalanced classification problem more effectively than the commonly used CNNs. By analyzing the kernels of the convolutional layers of DNCNN via NAM algorithm, we find that these kernels act as filters and they become complex when the layers go deeper. This result may help us understand what DNCNN has learned in intelligent fault diagnosis of machinery.

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

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

[3]  Hongkai Jiang,et al.  An adaptive deep convolutional neural network for rolling bearing fault diagnosis , 2017 .

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

[5]  Qingbo He,et al.  Energy-Fluctuated Multiscale Feature Learning With Deep ConvNet for Intelligent Spindle Bearing Fault Diagnosis , 2017, IEEE Transactions on Instrumentation and Measurement.

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

[7]  Yanqing Zhang,et al.  SVMs Modeling for Highly Imbalanced Classification , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

[9]  Theodoros Loutas,et al.  Rolling element bearings diagnostics using the Symbolic Aggregate approXimation , 2015 .

[10]  Thomas L. Griffiths,et al.  Supplementary Information for Natural Speech Reveals the Semantic Maps That Tile Human Cerebral Cortex , 2022 .

[11]  Minqiang Xu,et al.  A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mRMR feature selection , 2017 .

[12]  Adam Glowacz,et al.  Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals , 2018 .

[13]  Chang Ouk Kim,et al.  A Convolutional Neural Network for Fault Classification and Diagnosis in Semiconductor Manufacturing Processes , 2017, IEEE Transactions on Semiconductor Manufacturing.

[14]  Tara N. Sainath,et al.  Deep Convolutional Neural Networks for Large-scale Speech Tasks , 2015, Neural Networks.

[15]  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.

[16]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[17]  Mir Mohammad Ettefagh,et al.  Diagnosis of combined faults in Rotary Machinery by Non-Naive Bayesian approach , 2017 .

[18]  Hasmat Malik,et al.  Proximal Support Vector Machine (PSVM) Based Imbalance Fault Diagnosis of Wind Turbine Using Generator Current Signals , 2016 .

[19]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[20]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[21]  Guiming Mei,et al.  A Fault Diagnosis Method for Rolling Bearings Based on Feature Fusion of Multifractal Detrended Fluctuation Analysis and Alpha Stable Distribution , 2016 .

[22]  Liang Chen,et al.  Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis , 2016 .

[23]  Yaguo Lei,et al.  Application of a Novel Hybrid Intelligent Method to Compound Fault Diagnosis of Locomotive Roller Bearings , 2008 .

[24]  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.

[25]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[26]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[27]  Daniel Morinigo-Sotelo,et al.  Early Fault Detection in Induction Motors Using AdaBoost With Imbalanced Small Data and Optimized Sampling , 2017, IEEE Transactions on Industry Applications.

[28]  Wentao Mao,et al.  Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine , 2017 .

[29]  James Hensman,et al.  Natural computing for mechanical systems research: A tutorial overview , 2011 .

[30]  Yaguo Lei,et al.  Health condition identification of multi-stage planetary gearboxes using a mRVM-based method , 2015 .

[31]  Ronald Davis,et al.  Neural networks and deep learning , 2017 .

[32]  Jorge Nocedal,et al.  On the limited memory BFGS method for large scale optimization , 1989, Math. Program..