A Study of Adaptive Threshold Based on the Reconstruction Model for Marine Systems and Their Equipment Failure Warning

Abstract

To achieve the failure warning of marine systems and their equipment (MSAE), the threshold is one of the most prominent issues that should be solved first. In this study, a fusion model based on sparse Bayes and probabilistic statistical methods is applied to determine a new and more accurate adaptive alarm threshold. A multistep relevance vector machine (RVM) model is established to realize the parameter reconstruction in which the internal uncertainties caused by the degradation process and the external uncertainty caused by the loading, environment, and disturbances were considered. Then, a varying moving window (VMW) method is employed to determine the window size and achieve continuous data reconstruction. Further, the model based on Johnson distribution systems is utilized to complete the transformation of the residual parameters and calculate the adaptive threshold. Finally, the proposed adaptive decision threshold is successfully involved in the actual examples of the peak pressure and exhaust temperature of marine diesel engines. The results show that the proposed method can realize the continuous health condition monitoring of MSAE, successfully detect abnormal conditions in advance, achieve an early warning of failure, and reserve sufficient time for decision-making to prevent the occurrence of catastrophic disasters.