Using extended Kalman filter for failure detection and prognostic of degradation process in feedback control system

Abstract

In this article, we study a new approach to predict failures in feedback control system and particularly in actuators. However, we use two-tank control system with a proportional–integral–derivative controller for controlling a process variable. In practice, the actuator is a dynamic operating component in a random environment. Moreover, its capacity decreases over time and becomes valuable information for reliability analysis. The loss of capacity which is related to degradation, either normally or in an accelerated manner, depends on different operational conditions of the feedback control system and environmental factors. For this reason, to improve its working condition, a service life time analysis is necessary. Obviously, one has to predict the trend of future system characteristics, such as the reliability, which is measured by the estimate value of remaining useful life. In this situation, we use the stochastic gamma process model to describe the degradation behavior of the actuator. Generally, the algorithm of extended Kalman filter is a widely used method to overcome the difficulties of estimating the state vector in a nonlinear model of two-tank control system. This algorithm gives an innovation vector or prediction residual which contains fault information, when the system is failed. The prediction residuals can be recursively computed for diagnosis by the generalized likelihood ratio test. However, we use the generalized likelihood ratio test algorithm to estimate the moment at which the prognostic started. Finally, a practical case study is given to show the effectiveness of the proposed approaches for failure detection. Obviously, the simulation results show that the degradation path of the actuator capacity is estimated and the reliability based on remaining useful life predicted is analyzed.

Remaining useful life prediction for motor systems by iteratively updated wiener process based on closed-loop performance degradation monitoring

“…It is necessary to monitor the operational status of motor systems to prevent serious degradation of the performance. 6 However, the system error will fluctuate in a small allowable range owing to the feedback controller during the early even the middle stage of the motor performance degradation. 7 Once the obvious change is detected at the later stage of degradation, only extreme ways such as shutdown and replacement can be adopted.…”

Extended Kalman filter-based prognostic of actuator degradation in two tank system

“…The faults are classified into many categories based upon its nature. It is classified as additive fault, multiplicative fault and process fault (Moulahi, & Ben Hmida, 2022).…”

“…The third type of fault contains fault in the process itself. It may occur due to plant leakage or improper working of coolant (Moulahi, & Ben Hmida, 2022). The subdivisions of fault includes single fault or multiple faults.…”

“…This in turn increases the maintenance cost of the equipment. According to the varying needs of industries, the production rate changes frequently (Moulahi & Ben Hmida, 2022). This needs the component to adapt to varying industrial demands.…”

Trajectory Similarity Matching and Remaining Useful Life Prediction Based on Dynamic Time Warping

“…Yu et al [8] developed a nonlinear-drift-driven Wiener process model considering three sources of variability for the RUL prediction of degrading systems. Moulahi and Ben Hmida [9] proposed a model using the thick-tailed distributions called scalemixture normal and developed an expectation-maximum algorithm to estimate the model parameters.…”