Abstract
Condition monitoring and classification of machinery health state is of great practical significance in manufacturing industry, because it provides updated information regarding machine status on-line, thus avoiding the production loss and minimizing the chances of catastrophic machine failures. This is a pattern recognition problem and a condition monitoring system based on a hybrid of neural network and hidden Markov model (HMM) is proposed in this paper. Neural network realizes dimensionality reduction for Lipschitz exponent functions obtained from vibration data as input features and hidden Markov model is used for condition classification. The machinery condition can be identified by selecting the corresponding HMM which maximizes the probability of a given observation sequence. In the end, the proposed method is validated using gearbox vibration data.
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Miao, Q., Huang, HZ., Fan, X. (2006). A Novel Hybrid System with Neural Networks and Hidden Markov Models in Fault Diagnosis. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_48
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DOI: https://doi.org/10.1007/11925231_48
Publisher Name: Springer, Berlin, Heidelberg
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