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.
翻译:在调整发动机的终线测试中,产品夸度的评估很重要。由于时间的限制和变异的高度多样性,对声控测量比振动测量更经济。然而,声学数据受到工业扰动噪音的影响。因此,本研究的目的是调查在调整发动机末端测试中用于异常检测特征的稳健性。一个声学阵列记录了一个带有典型差错和声扰动的真实世界数据集。这包括生产中的工业噪音和系统生成的扰动,用于比较稳健性。总体而言,建议应用从日志信封谱中提取的特征以及心理声学特征。异常除色是通过使用隔离森林或更普遍的包装随机采矿器完成的。大多数扰动是可以规避的,而使用锤子或气压往往造成问题。一般而言,这些结果对于基于声学或振动测量的调控监测任务非常重要。富尔特尔摩,一个真实世界的问题描述是改进普通的硅处理和机器学习任务。</s>