用数学近似方法提升电机故障诊断准确率
Padé Approximant Neural Networks for Enhanced Electric Motor Fault Diagnosis Using Vibration and Acoustic Data
- 引入帕德神经元增强网络非线性表达能力
- 在四类传感器数据上达到最高99.96%诊断准确率
- 适合需要高精度故障检测的工业场景
目的：本研究旨在通过帕德近似神经元（PAON）模型提升感应电机的故障诊断性能。尽管加速度计和麦克风是电机状态监测的标准传感器，但采用非线性神经元结构的深度学习模型在诊断性能方面展现出显著潜力。本文探究帕德近似神经网络（PadéNets）是否能优于传统卷积神经网络（CNN）和自组织操作神经网络（Self-ONNs），在振动与声学数据中实现电气与机械故障诊断。方法：对比三种深度学习架构——一维CNN、Self-ONNs和PadéNets在渥太华大学公开的恒速感应电机数据集上的诊断能力，该数据集包含振动与声学传感器数据。PadéNet设计用于引入更强非线性，并兼容无界激活函数如LeakyReLU。结果与结论：PadéNets持续优于基线模型，在加速度计1、2、3及声学传感器上的诊断准确率分别为99.96%、98.26%、97.61%和98.33%。其增强的非线性结合对无界激活函数的兼容性，显著提升了感应电机状态监测中的故障诊断性能。
原文摘要 · Abstract (English)
Purpose: The primary aim of this study is to enhance fault diagnosis in induction machines by leveraging the Padé Approximant Neuron (PAON) model. While accelerometers and microphones are standard in motor condition monitoring, deep learning models with nonlinear neuron architectures offer promising improvements in diagnostic performance. This research investigates whether Padé Approximant Neural Networks (PadéNets) can outperform conventional Convolutional Neural Networks (CNNs) and Self-Organized Operational Neural Networks (Self-ONNs) in the diagnosis of electrical and mechanical faults from vibration and acoustic data. Methods: We evaluate and compare the diagnostic capabilities of three deep learning architectures: one-dimensional CNNs, Self-ONNs, and PadéNets. These models are tested on the University of Ottawa's publicly available constant-speed induction motor datasets, which include both vibration and acoustic sensor data. The PadéNet model is designed to introduce enhanced nonlinearity and is compatible with unbounded activation functions such as LeakyReLU. Results and Conclusion: PadéNets consistently outperformed the baseline models, achieving diagnostic accuracies of 99.96%, 98.26%, 97.61%, and 98.33% for accelerometers 1, 2, 3, and the acoustic sensor, respectively. The enhanced nonlinearity of PadéNets, together with their compatibility with unbounded activation functions, significantly improves fault diagnosis performance in induction motor condition monitoring.
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