Machine Learning-Based Failure Rate Identification for Predictive Maintenance in Industry 4.0

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The problem of regression model of failure rate building using datasets containing a number of failures of recoverable systems at certain time intervals and measurements of influencing technological and operational factors is considered. To solve the problem, kernel method of machine learning along with ridge regression is used, which providing possibility of efficient approximation of failure rate dependence from a large number of influencing technological and operational factors, without the need to pre-select model structure, complexity of which is determined by amount of available training data. The technique is generalized to the problem of non-stationary failure rate prediction, the model of which is trained on a sliding data window. The proposed approach is illustrated by solving the problem of failure rate model building for semiconductor production equipment using real data.