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：在齿轮电机生产线终端测试中，产品质量评估至关重要。由于时间限制及型号高度多样性，声学测量相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，本研究旨在探讨用于齿轮电机终端测试异常检测的特征鲁棒性。通过声学阵列采集包含典型故障与声学干扰的真实场景数据集，其中涵盖生产环境中的工业噪声与系统性产生的干扰，用以比较特征的鲁棒性。总体而言，本文提出采用对数包络谱提取的特征结合心理声学特征的方法。异常检测通过孤立森林或更通用的袋装随机挖掘器实现。多数干扰可被规避，但锤击或气压干扰常引发问题。这些结果对基于声学或振动测量的状态监测任务具有重要意义。此外，本文还呈现了真实场景问题描述，以改进通用信号处理与机器学习任务。