Lifetime Prediction of Optocouplers in Digital Input and Output Modules based on Bayesian tracking approaches

Abstract

In recent years, reliability of DIO modules has been drawing much attention from manufacturing companies under the growing complexity of automation systems for smart factory establishment. In manufacturing systems, DIO modules have been widely used to pass sensor measurements and configuration input signals for controlling actuators. Because sensor measurement and control signals pass through DIO modules, the faults of DIO modules would cause malfunctions or failures of the smart manufacturing systems and eventually lead to unexpected downtime in the manufacturing process. For predictive maintenance of DIO modules, this paper proposes a method of predicting the remaining useful life of a critical component in DIO modules based on the Bayesian tracking approaches. Optocouplers are one of the critical components in DIO modules that uses a short optical transmission path including light sources and photo-sensors to transfer an electrical signal. The performance of optocouplers may be degraded overtime with damages in a light source or a photo-sensor and eventually cause the faults of control systems. Extended Kalman Filter and Particle Filter are used in nonlinear degradation modeling to predict the lifetime of optocouplers, evaluating those filters by accuracy-based prognostic metrics and showing the effectiveness of Bayesian tracking approaches for lifetime prediction of optocouplers.