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Improved Hidden Markov Model and Its Application for Fault Prediction

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DOI: 10.23977/iccsc.2017.1021


Feifei Dai, Zhiqiang Wang

Corresponding Author

Feifei Dai


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.


Improved Hidden Markov Model (IHMM), Fault Prediction, Machine Learning.

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