论文标题
具有时间逻辑神经网络的滚动元件轴承的可解释故障诊断
Interpretable Fault Diagnosis of Rolling Element Bearings with Temporal Logic Neural Network
论文作者
论文摘要
基于机器学习的方法已在机械故障诊断中成功应用。但是，这些方法的主要限制是它们是黑匣子，通常不可解释。本文提出了一种新型的神经网络结构，称为时间逻辑神经网络（TLNN），其中网络的神经元是逻辑命题。更重要的是，可以将网络描述为加权信号时间逻辑。 TLNN不仅保留了传统神经元网络的良好特性，而且还用正式语言提供了对自己的正式解释。使用真实数据集的实验表明，提出的神经网络可以以良好的计算效率获得高度准确的故障诊断结果。此外，神经元网络的嵌入式形式语言可以提供有关决策过程的解释，从而实现可解释的故障诊断。
Machine learning-based methods have achieved successful applications in machinery fault diagnosis. However, the main limitation that exists for these methods is that they operate as a black box and are generally not interpretable. This paper proposes a novel neural network structure, called temporal logic neural network (TLNN), in which the neurons of the network are logic propositions. More importantly, the network can be described and interpreted as a weighted signal temporal logic. TLNN not only keeps the nice properties of traditional neuron networks but also provides a formal interpretation of itself with formal language. Experiments with real datasets show the proposed neural network can obtain highly accurate fault diagnosis results with good computation efficiency. Additionally, the embedded formal language of the neuron network can provide explanations about the decision process, thus achieve interpretable fault diagnosis.
