# Micro Fault Diagnosis of Driving Motor Bearings Based on Multi-Residual Neural Networks and Evidence Reasoning Rule

## TL;DR

This paper introduces a new model for detecting small faults in vehicle motor bearings using neural networks and reasoning rules to improve accuracy and reliability.

## Contribution

The novel contribution is the integration of multi-residual neural networks with an evidence reasoning rule for improved micro-fault diagnosis.

## Key findings

- The proposed model achieves higher diagnostic accuracy for micro-faults under variable conditions.
- The model integrates a credibility assessment mechanism to enhance result reliability.

## Abstract

Micro-fault diagnosis of vehicle driving motor bearings can significantly bring safety and economic benefits in preventing major accidents and extending equipment lifespan. However, under variable operating conditions, effectively capturing and diagnosing fault-related weak current fluctuation or high-frequency noise features, presents substantial technical challenges. Regarding these issues, this paper proposes multi-residual neural networks (multi-ResNets) and an evidential reasoning rule (ER Rule)-based fault diagnosis model. The model employs a benchmark condition generalization mechanism, which selects multiple typical load conditions as diagnostic anchor points based on a multi-residual neural network structure. Furthermore, by integrating a sub-model credibility assessment mechanism to perform diagnostic condition assessment and category assessment based on ER rule. The experimental results indicate that compared to the traditional machine learning algorithms, the proposed multi-ResNets-ER Rule-based model achieves higher diagnostic accuracy and result reliability for micro-faults under variable operating conditions.