Trustworthy Fault Diagnosis Method Based on Belief Rule Base With Multisource Uncertain Information for Vehicle

This article develops a new trustworthy fault diagnosis (TFD) method based on belief rule base expert system with multisource uncertainty information (BRB-MU) for a vehicle, such as a flying machine. To improve the performance of fault diagnosis for a vehicle, a new BRB-MU is developed, where the influence of the random environment disturbance is addressed by introducing uncertain factors of input characteristics. In BRB-MU, the uncertain factors of input characteristics are calculated by the signal to noise ratio based method. The new developed BRB-MU model aims to handle the following three problems: 1) unbalanced observation data of system states; 2) uncertain expert knowledge and 3) random environment disturbance. The first two problems are solved by the combination of expert knowledge and observation data. In parallel, the third problem is handled by the new introduced uncertain factors based on singular value decomposition in BRB-MU. To quantitatively analyze the influence of the uncertain information to the TFD output, the traceability analysis of the BRB-MU is conducted that can provide support for decision making of vehicle optimization design. It is analyzed based on the modeling traceability and parameters interpretability. To demonstrate the effectiveness of the TFD method, an experiment illustration is conducted.

I. Introduction

Vehicles, represented by a flying machine, are important transport carriers for satellites in aerospace. With the development of electronic technology, systems, such as a rocket and a drone, are developing in the direction of complexity and integration. In engineering practice, performance maintenance of the vehicle during its operation is an important way to improve its reliability and avoid serious accidents [1], [2].