Online Fault Diagnosis and Tolerance Based on Multiexpert Joint Belief Rule Base for Sensor Failures of Vehicles

Abstract

As sensors readings are used for vehicle flight control, their reliability directly affects the flight performance. This paper develops a new fault diagnosis and tolerance method for sensor failures of vehicles by addressing three problems:unavailable faulty data, difficulty in establishing analytical system, and inconsistence of expert cognitive ability. For the purpose, a new belief rule base model with multi-expert joint (BRB-ME) is proposed. The first two problems are handled by combining the small size of observation data and the uncertain knowledge from multiple experts in BRB-ME. For the third problem, a new multi-expert joint strategy is proposed in the BRB-ME model. The experts first construct their own models, and then the models are fused with different weights according to the experts’ ability, such as research fields, the working time and etc. Then, a new fault diagnosis and tolerance framework is developed based on BRB-ME for detecting vehicles’ sensor failures, where the sensor failures are tolerated by the reconstruction strategy for faulty sensor output. Moreover, in order to address the influence of the uncertain expert knowledge, an optimization model is constructed for obtaining the optimal solutions for the framework. An experimental illustration is conducted for accelerometer failure. The diagnosis accuracy is 97.50%, and the developed framework can ensure the navigation accuracy of the vehicle under accelerometer failure.