A HMM-based fault detection method for piecewise stationary industrial processes

In this paper, fault detection in piecewise stationary industrial processes is investigated. Such processes can be modeled as sequences of distinct system modes in which the respective expectation values and variances of process variables do not change. In particular, piecewise stationary processes with autonomous transitions between system modes are considered in this work, i.e. processes without observable trigger events such as on/off signals. A Hidden Markov Model (HMM) is employed as underlying system model for such processes. System modes are modeled as hidden state variables with given transition probabilities. Continuous process variables are assumed to be Gaussian distributed with constant second order statistics in each system mode. A novel HMM-based fault detection method is proposed which incorporates the Viterbi algorithm into a fault detection method for hybrid industrial processes. Experimental results for the proposed fault detection method are presented for a module of the Lemgo Smart Factory.

[1]  Feng Zhao,et al.  Monitoring and fault diagnosis of hybrid systems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Oliver Niggemann,et al.  A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems , 2013, 2013 11th IEEE International Conference on Industrial Informatics (INDIN).

[3]  Alexander Maier,et al.  Online passive learning of timed automata for cyber-physical production systems , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).

[4]  Benno Stein,et al.  Learning Behavior Models for Hybrid Timed Systems , 2012, AAAI.

[5]  Janis Dingel,et al.  Parameter Estimation of a Convolutional Encoder from Noisy Observations , 2007, 2007 IEEE International Symposium on Information Theory.

[6]  C. W. Frey.  Monitoring of complex industrial processes based on self-organizing maps and watershed transformations , 2012, 2012 IEEE International Conference on Industrial Technology.

[7]  J. Baker,et al.  The DRAGON system--An overview , 1975 .

[8]  Oliver Niggemann,et al.  Automatic model separation and application for diagnosis in industrial automation systems , 2015, 2015 IEEE International Conference on Industrial Technology (ICIT).

[9]  Geoffrey E. Hinton,et al.  Parameter estimation for linear dynamical systems , 1996 .

[10]  Hans Kleine Büning,et al.  Identifying behavior models for process plants , 2011, ETFA2011.

[11]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[12]  Sauro Longhi,et al.  Multi-scale PCA based fault diagnosis on a paper mill plant , 2011, ETFA2011.

[13]  Gautam Biswas,et al.  Model-Based Diagnosis of Hybrid Systems , 2003, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[14]  Igor Santos,et al.  Anomaly detection for high precision foundries , 2011, 2011 9th IEEE International Conference on Industrial Informatics.

[15]  Weimin Chen,et al.  Review of fault diagnosis in control systems , 2009, 2009 Chinese Control and Decision Conference.

[16]  S.D.J. McArthur,et al.  Learning Models of Plant Behavior for Anomaly Detection and Condition Monitoring , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[17]  Jörg Neidig,et al.  Improved diagnosis by combining structural and process knowledge , 2011, ETFA2011.

[18]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[19]  Steven Salzberg,et al.  Identifying bacterial genes and endosymbiont DNA with Glimmer , 2007, Bioinform..

[20]  Oliver Niggemann,et al.  Detecting anomalous energy consumptions in distributed manufacturing systems , 2012, IEEE 10th International Conference on Industrial Informatics.

[21]  James R. Glass,et al.  Developments and directions in speech recognition and understanding, Part 1 [DSP Education] , 2009, IEEE Signal Processing Magazine.

[22]  Melvin Michael Henry,et al.  Model-based Estimation of Probabilistic Hybrid Automata , 2002 .

[23]  Hans Kleine Büning,et al.  Using behavior models for anomaly detection in hybrid systems , 2011, 2011 XXIII International Symposium on Information, Communication and Automation Technologies.

[24]  R. Dearden,et al.  Detecting and Learning Unknown Fault States in Hybrid Diagnosis , 2009 .

[25]  Inseok Hwang,et al.  Robust estimation and fault detection and isolation algorithms for stochastic linear hybrid systems with unknown fault input , 2011 .