Multiway continuous hidden Markov model-based approach for fault detection and diagnosis
A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM) which is capable of capturing complex system dynamics and uncertainty is proposed. A set of observations from normal and faulty runs of the system was collected and...
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| Published in | AIChE journal Vol. 60; no. 6; pp. 2035 - 2047 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Blackwell Publishing Ltd
01.06.2014
American Institute of Chemical Engineers |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0001-1541 1547-5905 |
| DOI | 10.1002/aic.14386 |
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| Abstract | A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM) which is capable of capturing complex system dynamics and uncertainty is proposed. A set of observations from normal and faulty runs of the system was collected and used to generate the training dataset. The training data is assumed to follow a finite Gaussian mixture model. The number of mixture components and associated parameters for the optimal Gaussian mixture fit of the observed data was computed subsequently by clustering using the Figueiredo–Jain algorithm for unsupervised learning. The segmental k‐means algorithm was used to compute the HMM parameters. The applicability of the proposed scheme is investigated for the case of an inverted pendulum system and a fluidized catalytic cracker. The monitoring results for the above cases with the proposed scheme was found to be superior to the multiway discrete hidden Markov model (MDHMM) based scheme in terms of the accuracy of fault detection, especially in case of noisy observations. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2035–2047, 2014 |
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| AbstractList | A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM) which is capable of capturing complex system dynamics and uncertainty is proposed. A set of observations from normal and faulty runs of the system was collected and used to generate the training dataset. The training data is assumed to follow a finite Gaussian mixture model. The number of mixture components and associated parameters for the optimal Gaussian mixture fit of the observed data was computed subsequently by clustering using the Figueiredo-Jain algorithm for unsupervised learning. The segmental k-means algorithm was used to compute the HMM parameters. The applicability of the proposed scheme is investigated for the case of an inverted pendulum system and a fluidized catalytic cracker. The monitoring results for the above cases with the proposed scheme was found to be superior to the multiway discrete hidden Markov model (MDHMM) based scheme in terms of the accuracy of fault detection, especially in case of noisy observations. copyright 2014 American Institute of Chemical Engineers AIChE J, 60: 2035-2047, 2014 A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM) which is capable of capturing complex system dynamics and uncertainty is proposed. A set of observations from normal and faulty runs of the system was collected and used to generate the training dataset. The training data is assumed to follow a finite Gaussian mixture model. The number of mixture components and associated parameters for the optimal Gaussian mixture fit of the observed data was computed subsequently by clustering using the Figueiredo–Jain algorithm for unsupervised learning. The segmental k‐means algorithm was used to compute the HMM parameters. The applicability of the proposed scheme is investigated for the case of an inverted pendulum system and a fluidized catalytic cracker. The monitoring results for the above cases with the proposed scheme was found to be superior to the multiway discrete hidden Markov model (MDHMM) based scheme in terms of the accuracy of fault detection, especially in case of noisy observations. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2035–2047, 2014 A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM) which is capable of capturing complex system dynamics and uncertainty is proposed. A set of observations from normal and faulty runs of the system was collected and used to generate the training dataset. The training data is assumed to follow a finite Gaussian mixture model. The number of mixture components and associated parameters for the optimal Gaussian mixture fit of the observed data was computed subsequently by clustering using the Figueiredo-Jain algorithm for unsupervised learning. The segmental k-means algorithm was used to compute the HMM parameters. The applicability of the proposed scheme is investigated for the case of an inverted pendulum system and a fluidized catalytic cracker. The monitoring results for the above cases with the proposed scheme was found to be superior to the multiway discrete hidden Markov model (MDHMM) based scheme in terms of the accuracy of fault detection, especially in case of noisy observations. [PUBLICATION ABSTRACT] |
| Author | Raihan, Dilshad Sen, Deepthi Chidambaram, M. |
| Author_xml | – sequence: 1 givenname: Deepthi surname: Sen fullname: Sen, Deepthi organization: Dept. of Chemical Engineering, Indian Institute of Technology Madras, Tamil Nadu, 600036, Chennai, India – sequence: 2 givenname: Dilshad surname: Raihan fullname: Raihan, Dilshad organization: Dept. of Mechanical Engineering, Indian Institute of Technology Madras, Tamil Nadu, 600036, Chennai, India – sequence: 3 givenname: M. surname: Chidambaram fullname: Chidambaram, M. email: chidam@iitm.ac.in organization: Dept. of Chemical Engineering, Indian Institute of Technology Madras, Tamil Nadu, 600036, Chennai, India |
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Chem Eng Sci. 2002;57:63-75. – reference: Hwang I, Kim S, Kim Y, Seah CE. A survey of fault detection, isolation, reconfiguration methods. IEEE Trans Control Syst Technol. 2010;18(3):636-653. – reference: Hwang DH, Han C. Real-time monitoring for a process with multiple operating modes. Control Eng Pract. 1999;7:891-902. – reference: Chiang L, Kotanchek M, Kordon A. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Comput Chem Eng. 2004;28:1389-1401. – reference: Rabiner L. A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE. 1989;77:257-286. – reference: Nomikos P, MacGregor JF. Monitoring of batch processes using multi-way principal component analysis. AIChE J. 1994;40:1361-1375. – reference: Figueiredo MAF, Jain AK. Unsupervised learning of finite mixture models. AIChE J. 2002;24:381-96. – reference: Venkatasubramanian V, Rengaswamy R, Kavuri SN. A review of process fault detection and diagnosis. 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Comput Chem Eng. 2003;27:327-346. – reference: Chen J, Liu K. Mixture principal component analysis models for process monitoring. Ind Eng Chem Res. 1999;38:1478-1488. – reference: MacGregor JF, Jaeckle C, Kiparissides C, Koutoudi M. Process monitoring and diagnosis by multiblock PLS methods. AIChE J. 1994;40:826-838. – reference: Yu J. Multiway discrete hidden Markov model-based approach for dynamic batch process monitoring and fault classification. AIChE J. 2012;58:2714-2725. – reference: Bhowmik T, van Oosten J, Schomaker L. Segmental k-means learning with mixture distribution for HMM based handwriting recognition. Pattern Recognit Mach Intell. 2011;6744:432-439. – reference: Forney GD Jr. The Viterbi algorithm. Proc IEEE 1978;61:268-278. – reference: Choi SW, Park JH, Lee IB. Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis. Comput Chem Eng. 2004;28:1377-1387. – reference: Chiang LH, Russell EL, Braatz RD. 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| SubjectTerms | Algorithms Chemical engineering Dynamical systems Dynamics Fault detection fault detection and classification Fault diagnosis Figueiredo-Jain algorithm finite Gaussian mixture models fluidized catalytic cracker Gaussian hidden Markov models inverted pendulum Markov analysis Markov chains Mathematical models Probabilistic inference Process engineering segmental k-means algorithm Training |
| Title | Multiway continuous hidden Markov model-based approach for fault detection and diagnosis |
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