A cost-optimal fault prediction model using machine learning and statistical process control

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Abstract
Traditional methods of monitoring industrial products and processes tend to focus on single-model approaches and ignore the cost of monitoring systems in fault detection. To fill the gap, this paper presents a novel cost-optimal fault monitoring and prediction model using machine learning and statistical process control for industrial systems. First, a least squares support vector machine model is presented to estimate the degradation process and obtain degradation residuals of the industrial systems. Secondly, a multidimensional hidden Markov model is presented to describe the residual process of the industrial systems. The multidimensional hidden Markov model parameters are derived and estimated via an expectation-maximization algorithm with explicit expressions. Afterward, based on the built hidden Markov model, a fault prediction model utilizing a Bayesian statistical control is developed to dynamically update the probability estimate of equipment failure throughout the entire operation process of industrial systems. The decision variable of the control process for fault prediction is optimized by a semi-Markov decision process (SMDP) under minimization of the average operation costs. Finally, a case study with NASA's PHM08 dataset is performed to verify the proposed approach, which demonstrates the effectiveness and superiority of the presented model.
Funding Information
  • National Natural Science Foundation of China (52375106)
  • National Natural Science Foundation of China (62033001)
  • Ministry of Education (HZKY20220088)

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