Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives

TL;DR

This paper presents a Bayesian neural network approach for gear fault diagnosis in motor drives, enabling uncertainty quantification, improved robustness, and interpretability over traditional deterministic models.

Contribution

It introduces a Bayesian neural network framework for fault diagnosis that captures uncertainty and enhances robustness and interpretability compared to existing methods.

Findings

BNNs effectively quantify uncertainty in fault diagnosis.

The approach improves robustness to noisy data.

It demonstrates adaptability to new fault types and datasets.

Abstract

This paper introduces a novel approach to quantify the uncertainties in fault diagnosis of motor drives using Bayesian neural networks (BNN). Conventional data-driven approaches used for fault diagnosis often rely on point-estimate neural networks, which merely provide deterministic outputs and fail to capture the uncertainty associated with the inference process. In contrast, BNNs offer a principled framework to model uncertainty by treating network weights as probability distributions rather than fixed values. It offers several advantages: (a) improved robustness to noisy data, (b) enhanced interpretability of model predictions, and (c) the ability to quantify uncertainty in the decision-making processes. To test the robustness of the proposed BNN, it has been tested under a conservative dataset of gear fault data from an experimental prototype of three fault types at first, and is then incrementally trained on new fault classes and datasets to explore its uncertainty quantification features and model interpretability under noisy data and unseen fault scenarios.