Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.engappai.2013.06.005
Title: Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach
Authors: Geramifard, O.
Xu, J.-X. 
Kumar Panda, S. 
Keywords: Fault detection
Fault diagnosis
Hidden
Markov model
Nonparametric approach
Synchronous motors
Issue Date: Sep-2013
Source: Geramifard, O., Xu, J.-X., Kumar Panda, S. (2013-09). Fault detection and diagnosis in synchronous motors using hidden Markov model-based semi-nonparametric approach. Engineering Applications of Artificial Intelligence 26 (8) : 1919-1929. ScholarBank@NUS Repository. https://doi.org/10.1016/j.engappai.2013.06.005
Abstract: Early detection and diagnosis of faults in industrial machines would reduce the maintenance cost and also increase the overall equipment effectiveness by increasing the availability of the machinery systems. In this paper, a semi-nonparametric approach based on hidden Markov model is introduced for fault detection and diagnosis in synchronous motors. In this approach, after training the hidden Markov model classifiers (parametric stage), two matrices named probabilistic transition frequency profile and average probabilistic emission are computed based on the hidden Markov models for each signature (nonparametric stage) using probabilistic inference. These matrices are later used in forming a similarity scoring function, which is the basis of the classification in this approach. Moreover, a preprocessing method, named squeezing and stretching is proposed which rectifies the difficulty of dealing with various operating speeds in the classification process. Finally, the experimental results are provided and compared. Further investigations are carried out, providing sensitivity analysis on the length of signatures, the number of hidden state values, as well as statistical performance evaluation and comparison with conventional hidden Markov model-based fault diagnosis approach. Results indicate that implementation of the proposed preprocessing, which unifies the signatures from various operating speeds, increases the classification accuracy by nearly 21% and moreover utilization of the proposed semi-nonparametric approach improves the accuracy further by nearly 6%. © 2013 Elsevier Ltd. All rights reserved.
Source Title: Engineering Applications of Artificial Intelligence
URI: http://scholarbank.nus.edu.sg/handle/10635/56023
ISSN: 09521976
DOI: 10.1016/j.engappai.2013.06.005
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