Fault diagnosis based on deep learning

As representation scheme can severely limit the window by which the system observes its world, deep learning for fault diagnosis is put forward in this paper. It is a real time online scheme that can enhance the accuracy of detection, classification and prediction, and efficient for incipient faults that cannot be detected by traditional statistic technology. A stacked sparse auto encoder is used to learn the deep architectures of fault data to minimize the loss of information. Experiment results show that the proposed method not only improves the divisibility between faults and normal process, but also exhibits a better performance on the accuracy of fault classification for the chemical benchmark, Tennessee Eastman Process (TEP) data.

[1]  H. Karimi,et al.  Study on Support Vector Machine-Based Fault Detection in Tennessee Eastman Process , 2014 .

[2]  Manabu Kano,et al.  Monitoring independent components for fault detection , 2003 .

[3]  Catherine Porte,et al.  Automation and optimization of glycine synthesis , 1996 .

[4]  Qixiang Ye,et al.  Fault detection and classification in chemical processes using NMFSC and structural SVMs , 2014 .

[5]  Yingwei Zhang,et al.  Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM , 2009 .

[6]  Chun-Chin Hsu,et al.  A novel process monitoring approach with dynamic independent component analysis , 2010 .

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

[8]  Juhan Nam,et al.  Multimodal Deep Learning , 2011, ICML.

[9]  Claude Delpha,et al.  Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis: Part II , 2015, Signal Process..

[10]  Zhi-Huan Song,et al.  A novel fault diagnosis system using pattern classification on kernel FDA subspace , 2011, Expert Syst. Appl..

[11]  B. Upadhyaya,et al.  Incipient fault detection and isolation in a PWR plant using principal component analysis , 2001, Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148).

[12]  Arthur K. Kordon,et al.  Fault diagnosis based on Fisher discriminant analysis and support vector machines , 2004, Comput. Chem. Eng..

[13]  Lijun Wu,et al.  Fault detection and diagnosis based on sparse representation classification (SRC) , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[14]  Barry M. Wise,et al.  A Theoretical Basis for the use of Principal Component Models for Monitoring Multivariate Processes , 1990 .

[15]  Zhiqiang Ge,et al.  Local ICA for multivariate statistical fault diagnosis in systems with unknown signal and error distributions , 2012 .

[16]  Thomas G. Dietterich.  An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[17]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[18]  Marios M. Polycarpou,et al.  Fault diagnosis of a class of nonlinear uncertain systems with Lipschitz nonlinearities using adaptive estimation , 2010, Autom..

[19]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

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