School of Mechanical Engineering, Sichuan University, Chengdu 610065, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Sensors (Basel). 2020 Feb 24;20(4):1233. doi: 10.3390/s20041233.
The gear fault signal under different working conditions is non-linear and non-stationary, which makes it difficult to distinguish faulty signals from normal signals. Currently, gear fault diagnosis under different working conditions is mainly based on vibration signals. However, vibration signal acquisition is limited by its requirement for contact measurement, while vibration signal analysis methods relies heavily on diagnostic expertise and prior knowledge of signal processing technology. To solve this problem, a novel acoustic-based diagnosis (ABD) method for gear fault diagnosis under different working conditions based on a multi-scale convolutional learning structure and attention mechanism is proposed in this paper. The multi-scale convolutional learning structure was designed to automatically mine multiple scale features using different filter banks from raw acoustic signals. Subsequently, the novel attention mechanism, which was based on a multi-scale convolutional learning structure, was established to adaptively allow the multi-scale network to focus on relevant fault pattern information under different working conditions. Finally, a stacked convolutional neural network (CNN) model was proposed to detect the fault mode of gears. The experimental results show that our method achieved much better performance in acoustic based gear fault diagnosis under different working conditions compared with a standard CNN model (without an attention mechanism), an end-to-end CNN model based on time and frequency domain signals, and other traditional fault diagnosis methods involving feature engineering.
在不同工况下，齿轮故障信号是非线性和非平稳的，这使得从正常信号中区分故障信号变得困难。目前，不同工况下的齿轮故障诊断主要基于振动信号。然而，振动信号采集受到接触测量的限制，而振动信号分析方法则严重依赖于诊断专业知识和信号处理技术的先验知识。为了解决这个问题，本文提出了一种基于多尺度卷积学习结构和注意力机制的基于声的诊断（ABD）方法，用于不同工况下的齿轮故障诊断。多尺度卷积学习结构旨在使用原始声信号中的不同滤波器组自动挖掘多个尺度的特征。随后，建立了基于多尺度卷积学习结构的新型注意力机制，以使多尺度网络能够自适应地关注不同工况下相关的故障模式信息。最后，提出了一种堆叠卷积神经网络（CNN）模型来检测齿轮的故障模式。实验结果表明，与标准 CNN 模型（无注意力机制）、基于时频域信号的端到端 CNN 模型以及其他涉及特征工程的传统故障诊断方法相比，我们的方法在不同工况下的基于声的齿轮故障诊断中表现出更好的性能。
