Fault Detection and Diagnosis based on Probabilistic Production Rule

This paper presents a new fault detection strategy based on the expression with a probabilistic production rule (PPR). Production rule (PR) is widely used in the field of computer science as a tool of formal verification. In this work, first of all, PR is used to represent the mapping between highly quantized input and output signals of the dynamical system. By using PR expression, the fault detection algorithm can be implemented with less computational effort. In addition, we introduce the new system description with probabilistic PR (PPR) in which occurrence probability of PRs is assigned to them to improve the robustness. The probability is derived statistic characteristics of measured input and output signals. Then, the fault detection and diagnosis algorithm is developed based on the calculation of the log-likelihood of the measured data for the designed PPR. Finally, some experiments on the motor control are demonstrated to confirm the usefulness of the proposed method