论文标题
讨论在齿轮电动机的结束测试中，在工业令人不安的噪声下检测声学异常检测的特征
Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors
论文作者
论文摘要
在齿轮电动机的结束测试中，产品质量的评估很重要。由于时间限制和变体的高多样性，声音测量比振动测量更经济。但是，声学数据受工业不安噪声的影响。因此，这项研究的目的是研究用于齿轮运动端测试中用于异常检测的特征的鲁棒性。声学阵列记录了具有典型故障和声学干扰的现实世界数据集。这包括生产中的工业噪声和系统产生的干扰，用于比较鲁棒性。总体而言，建议应用从对数 - 玻璃频谱中提取的功能以及精神声学特征。通过使用隔离林或更普遍的随机矿工来完成异常脱位。大多数干扰都可以规避，而使用锤子或气压通常会引起问题。通常，这些结果对于基于声学或振动测量的调节监测任务很重要。向热 - 提出了一个现实世界中的问题描述，以改善常见的Sig-nal处理和机器学习任务。
In the end-of-line test of geared motors, the evaluation of product qual-ity is important. Due to time constraints and the high diversity of variants, acous-tic measurements are more economical than vibration measurements. However, the acoustic data is affected by industrial disturbing noise. Therefore, the aim of this study is to investigate the robustness of features used for anomaly detection in geared motor end-of-line testing. A real-world dataset with typical faults and acoustic disturbances is recorded by an acoustic array. This includes industrial noise from the production and systematically produced disturbances, used to compare the robustness. Overall, it is proposed to apply features extracted from a log-envelope spectrum together with psychoacoustic features. The anomaly de-tection is done by using the isolation forest or the more universal bagging random miner. Most disturbances can be circumvented, while the use of a hammer or air pressure often causes problems. In general, these results are important for condi-tion monitoring tasks that are based on acoustic or vibration measurements. Fur-thermore, a real-world problem description is presented to improve common sig-nal processing and machine learning tasks.
