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：在齿轮电机终检线中，产品质量评估至关重要。由于时间限制和型号多样性，声学测量比振动测量更具经济性。然而，声学数据会受到工业干扰噪声的影响。因此，本研究旨在探究用于齿轮电机终检线异常检测的特征鲁棒性。通过声阵列采集了包含典型故障和声学干扰的真实数据集，其中涵盖生产过程中产生的工业噪声及系统性人为干扰，用于比较特征鲁棒性。总体而言，本文提出使用对数包络谱特征与心理声学特征相结合的方法。异常检测采用孤立森林或更具通用性的袋装随机挖掘算法实现。多数干扰源均可被有效规避，但使用锤击或压缩空气时仍存在问题。一般而言，这些结果对于基于声学或振动测量的状态监测任务具有重要意义。此外，本文还呈现了一个真实场景问题描述，以改进通用的信号处理与机器学习任务。