System failure early warning method based on baseline model and Bayesian factor

Abstract

The invention discloses a system failure early warning method based on a baseline model and the Bayesian factor. The system failure early warning method based on the baseline model and the Bayesian factor can be applied to a dynamic complex system and particularly applied to early detection and warning of aerospace vehicle equipment failure. The method includes: using the multivariate state estimation technique to excavate the system 'individuation' baseline model on the basis of system state monitoring data; and using an actual measurement value of a system state parameter to subtract a baseline value so as to obtain a deviation value of a state parameter, analyzing a deviation value sequence by the aid of a Bayesian factor method, monitoring abnormalities of a sequence structure, and timely giving out failure early warning. The system failure early warning method based on the baseline model and the Bayesian factor solves the problems that dynamic complex system performance parameter deviation value dispersion is large, failure early characteristics are easily buried in noise, warning can be given out at the early stage of system failure, and sensitivity and robustness of the monitoring system are improved. Besides, the baseline model is easy to implement from the perspective of engineering, and the method is high in university and capable of meeting the requirements of the dynamic system on real-time monitoring.

Description

technical field [0001] The invention belongs to the technical field of equipment state monitoring, and relates to an early detection method for dynamic system failures, in particular to early detection and early warning of aerospace equipment failures. Background technique [0002] Modern engineering systems, such as aerospace vehicles, nuclear power plants, etc., are increasingly complex and integrated, and at the same time face the characteristics of diverse operating environments, resulting in increasingly prominent system reliability and safety issues. Timely and accurate detection of system faults to take further measures is of great significance to ensure the safe operation of the system. Therefore, paying attention to the processing of weak signals and finding the timing of fault symptoms in advance is an important development direction of complex system status monitoring technology. [0003] Using the baseline model of equipment state parameters to judge the system ...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to overcome the above-mentioned "generalized" baseline model for system status monitoring that cannot accurately reflect the characteristics of individual systems under actual operating conditions, and the failure warning of the monitoring system caused by model errors and noise interference after the system fails. The shortcomings and shortcomings of lag provide a new data-driven "personalized" complex system baseline model mining technology, combined with Bayesian factor method for abnormal state monitoring, widely applicable, low-cost early warning of complex system failures method

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