用卷积网络实时识别轴承故障，兼顾准确率与处理速度。
Deep learning-based fault identification in condition monitoring
- 将振动信号转为二维图像，输入CNN分类故障类型和大小
- 在CWRU数据集上实现高精度故障识别，推理速度快于传统方法
- 适合对响应时间敏感的工业现场与远程监控场景
基于振动的设备状态监测常用于滚动轴承故障识别。故障检测的准确性与速度是关键性能指标，尤其在远程监测和时间敏感的工业应用中延迟问题尤为突出。现有方法多关注准确性，较少考虑故障识别过程中的推理时间。本文提出一种基于卷积神经网络（CNN）的实时轴承故障识别方法，采用多种编码方式将原始振动信号转换为二维图像，并利用CNN对多种故障类型和尺寸进行分类。通过分析故障识别准确率与处理时间的权衡关系，在轴承故障CWRU数据集上完成训练与评估。
原文摘要 · Abstract (English)
Vibration-based condition monitoring techniques are commonly used to identify faults in rolling element bearings. Accuracy and speed of fault detection procedures are critical performance measures in condition monitoring. Delay is especially important in remote condition monitoring and time-sensitive industrial applications. While most existing methods focus on accuracy, little attention has been given to the inference time in the fault identification process. In this paper, we address this gap by presenting a Convolutional Neural Network (CNN) based approach for real-time fault identification in rolling element bearings. We encode raw vibration signals into two-dimensional images using various encoding methods and use these with a CNN to classify several categories of bearing fault types and sizes. We analyse the interplay between fault identification accuracy and processing time. For training and evaluation we use a bearing failure CWRU dataset.
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