A pairwise graph regularized constraint based on deep belief network for fault diagnosis

Abstract An enhanced intelligent fault diagnosis method is proposed based on pairwise graph regularized deep belief network (PG-DBN) model. In this novel framework, two different graph constraints are imposed on hidden layer of the Restricted Boltzmann Machine (RBM). The first graph constraint defines the representation of preserving the feature manifold structure in same class of the data and the second graph constraint defines the representation of the penalty of the feature manifold structure in different class of the data. The two graph constraints introduce feature manifold structure to RBM and make the extracted features contain more intrinsic information which contributes to a better classification result. Meanwhile, the convergence and stability analysis of the proposed method is presented. Finally, the advantages of the proposed fault diagnosis model are evaluated by Tennessee Eastman process.

[1]  E. F. Vogel,et al.  A plant-wide industrial process control problem , 1993 .

[2]  Ying Zhang,et al.  Reciprocating compressor fault diagnosis using an optimized convolutional deep belief network , 2020 .

[3]  Junjie Wang,et al.  Improved model predictive control for the flow field in a wind tunnel with model uncertainty , 2020, J. Frankl. Inst..

[4]  Chaojie Wang,et al.  A sample entropy inspired affinity propagation method for bearing fault signal classification , 2020, Digit. Signal Process..

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

[6]  Kai-Jie Yang,et al.  Recurrent Fuzzy Neural Cerebellar Model Articulation Network Fault-Tolerant Control of Six-Phase Permanent Magnet Synchronous Motor Position Servo Drive , 2016, IEEE Transactions on Fuzzy Systems.

[7]  Ying Zhang,et al.  Fault diagnosis of reciprocating compressor using a novel ensemble empirical mode decomposition-convolutional deep belief network , 2020 .

[8]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[9]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[10]  Junfei Qiao,et al.  A sparse deep belief network with efficient fuzzy learning framework , 2020, Neural Networks.

[11]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[12]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[13]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

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

[15]  Hyunsoo Lee,et al.  Learning framework of multimodal Gaussian-Bernoulli RBM handling real-value input data , 2018, Neurocomputing.

[16]  Moncef Gabbouj,et al.  Real-Time Motor Fault Detection by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Industrial Electronics.

[17]  Chun-Xia Zhang,et al.  A sparse-response deep belief network based on rate distortion theory , 2014, Pattern Recognit..

[18]  Qinghua Zhang,et al.  An Information Fusion Fault Diagnosis Method Based on Dimensionless Indicators With Static Discounting Factor and KNN , 2016, IEEE Sensors Journal.

[19]  David J. Miller,et al.  Mixture Modeling with Pairwise, Instance-Level Class Constraints , 2005, Neural Computation.

[20]  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.

[21]  Mohieddine Jelali,et al.  Revision of the Tennessee Eastman Process Model , 2015 .

[22]  Jie Huang,et al.  Control of Pendulum-Sloshing Dynamics in Suspended Liquid Containers , 2021, IEEE Transactions on Industrial Electronics.

[23]  Jun Wu,et al.  Machine Health Monitoring Using Adaptive Kernel Spectral Clustering and Deep Long Short-Term Memory Recurrent Neural Networks , 2019, IEEE Transactions on Industrial Informatics.

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

[25]  Dong Yu,et al.  Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP] , 2011, IEEE Signal Processing Magazine.

[26]  Zhanpeng Zhang,et al.  A deep belief network based fault diagnosis model for complex chemical processes , 2017, Comput. Chem. Eng..

[27]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[28]  Haidong Shao,et al.  Rolling bearing fault diagnosis using an optimization deep belief network , 2015 .

[29]  Richa Singh,et al.  Class sparsity signature based Restricted Boltzmann Machine , 2017, Pattern Recognit..

[30]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[31]  Kang Zhang,et al.  A new rotating machinery fault diagnosis method based on local oscillatory-characteristic decomposition , 2018, Digit. Signal Process..

[32]  Diego Cabrera,et al.  A review on data-driven fault severity assessment in rolling bearings , 2018 .

[33]  Honglak Lee,et al.  Sparse deep belief net model for visual area V2 , 2007, NIPS.

[34]  Zhang Yi,et al.  Graph Regularized Restricted Boltzmann Machine , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[35]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[36]  Changzheng Chen,et al.  A method for intelligent fault diagnosis of rotating machinery , 2004, Digit. Signal Process..

[37]  Yaguo Lei,et al.  Intelligent Fault Diagnosis and Remaining Useful Life Prediction of Rotating Machinery , 2016 .