Abstract
In this article, a fast krill herd algorithm is developed for prognosis of hybrid mechatronic system using the improved Wiener degradation process. First, the diagnostic hybrid bond graph is used to model the hybrid mechatronic system and derive global analytical redundancy relations. Based on the global analytical redundancy relations, the fault signature matrix and mode change signature matrix for fault and mode change isolation can be obtained. Second, in order to determine the true faults from the suspected fault candidates after fault isolation, a fault estimation method based on adaptive square root cubature Kalman filter is proposed when the noise distributions are unknown. Then, the improved Wiener process incorporating nonlinear term is developed to build the degradation model of incipient fault based on the fault estimation results. For prognosis, the fast krill herd algorithm is proposed to estimate unknown degradation model coefficients. After that, the probability density function of remaining useful life is derived using the identified degradation model. Finally, the proposed methods are validated by simulations.
Highlights
Hybrid mechatronic systems, which include interacting continuous and discrete dynamics, are widely used in modern industrial systems, such as automobile, chemical plant, and aerospace engineering [1,2]
Actuators 2021, 10, 213 (3) A prognosis method using fast krill herd (FKH) algorithm is proposed where the FKH is developed to estimate the degradation model coefficients based on the identified fault data from fault estimation module
The possible fault mode is [1 0 0], and the GARRs will return within the threshold after a is set as [1 0 0]
Summary
Hybrid mechatronic systems, which include interacting continuous and discrete dynamics, are widely used in modern industrial systems, such as automobile, chemical plant, and aerospace engineering [1,2]. Many valuable works have been reported in the hybrid system fault diagnosis and prognosis fields [6,7,8,9,10] Among these works, discrete event system model-based methods (e.g., Petri net and automaton) are widely investigated. The Kalman filter (KF) is a popular method for fault parameter estimation of linear systems with Gaussian noise where the fault parameter is treated as a special state It generates recursive estimations of state vectors by optimally weighting information from the system dynamic model and current measurements [11]. (3) A prognosis method using fast krill herd (FKH) algorithm is proposed where the FKH is developed to estimate the degradation model coefficients based on the identified fault data from fault estimation module.
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