基于声音的工业设备无监督故障诊断,考虑到环境噪音
Jeong-Geun Lee1,2, Kwang Sik Kim3, Jang Hyun Lee4
1Department of Smart Digital Engineering, Inha University, Incheon 22212, Republic of Korea.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究引入了一种使用变异自编码器 (VAE) 和域适应神经网络 (DANN) 的新型机器故障诊断方法,以有效地诊断噪音环境中的故障,提高准确性和可靠性.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 环境噪音往往会影响使用声信号的机器故障诊断.
- 现有的方法在多样化或噪音条件下扎,性能下降.
研究的目的:
- 开发一种基于无监督学习的故障诊断方法,该方法对环境噪声具有稳定性.
- 为了提高机器故障诊断的准确性和可靠性,在具有挑战性的声学环境中.
主要方法:
- 使用变化自编码器 (VAE) 来学习正常的声学数据并通过重建错误识别故障.
- 应用域适应神经网络 (DANN) 弥合域差异,提高在杂的目标环境中的性能.
- 整合了Mel频率的cepstral系数,降噪,数据增强和指数加权移动平均值,用于功能增强和时间序列分析.
主要成果:
- VAE在时间序列数据上证明了有效的故障诊断,但在噪音条件下显示性能下降.
- 通过将源域模型调整为目标域,DANN在杂环境中显著提高了诊断性能.
- 联合VAE-DANN方法在不同噪声水平 (信号与噪声比率) 中保持了高的诊断准确性.
结论:
- 拟议的VAE和DANN组合为在环境噪音的情况下对机器故障诊断提供了强大的解决方案.
- 这种方法有效地克服了环境领域的转变,确保可靠的故障检测和分类.
- 这种方法对工业应用具有重大潜力,需要精确的基于声学的机器健康监测.
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