Improved Hidden Markov Model and Its Application for Fault Prediction
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DOI: 10.23977/iccsc.2017.1021
Author(s)
Feifei Dai, Zhiqiang Wang
Corresponding Author
Feifei Dai
ABSTRACT
The fault prediction is an important problem which can improve the production
efficiency during the process of automatic production. With the continuous development of
technology, the way of using threshold to detect faults has been applied to production.
Threshold detection, however, can’t predict the occurrence of fault. It can only judge
whether there is any fault after getting the data. In this paper, we proposed an improved
Hidden Markov Model for fault prediction. This algorithm obtains a model by training the
previous data, and then it uses the model to deal with the new data so that it could forecast the
fault successfully. Experiment shows that this algorithm can better adapt to different
production occasions with high accuracy. It also has strong anti-interference ability, and
satisfactory effect.
KEYWORDS
Improved Hidden Markov Model (IHMM), Fault Prediction, Machine Learning.