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.

翻译：本文提出了一种利用贝叶斯神经网络量化电机驱动故障诊断中不确定性的新方法。传统用于故障诊断的数据驱动方法通常依赖于点估计神经网络，这类网络仅提供确定性输出，无法捕捉推理过程相关的不确定性。相比之下，贝叶斯神经网络通过将网络权重视为概率分布而非固定值，为不确定性建模提供了理论框架。该方法具有多项优势：(a) 提升对噪声数据的鲁棒性，(b) 增强模型预测的可解释性，(c) 具备量化决策过程中不确定性的能力。为验证所提贝叶斯神经网络的鲁棒性，研究首先采用包含三种故障类型的齿轮故障实验原型保守数据集进行测试，随后通过增量训练引入新故障类别和数据集，以探究其在噪声数据和未见故障场景下的不确定性量化特性与模型可解释性。