Acoustic-based fault detection has a high potential to monitor the health condition of mechanical parts. However, the background noise of an industrial environment may negatively influence the performance of fault detection. Limited attention has been paid to improving the robustness of fault detection against industrial environmental noise. Therefore, we present the Lenze production background-noise (LPBN) real-world dataset and an automated and noise-robust auditory inspection (ARAI) system for the end-of-line inspection of geared motors. An acoustic array is used to acquire data from motors with a minor fault, major fault, or which are healthy. A benchmark is provided to compare the psychoacoustic features with different types of envelope features based on expert knowledge of the gearbox. To the best of our knowledge, we are the first to apply time-varying psychoacoustic features for fault detection. We train a state-of-the-art one-class-classifier, on samples from healthy motors and separate the faulty ones for fault detection using a threshold. The best-performing approaches achieve an area under curve of 0.87 (logarithm envelope), 0.86 (time-varying psychoacoustics), and 0.91 (combination of both).

翻译：基于声学的故障检测在监测机械部件健康状态方面具有巨大潜力。然而，工业环境中的背景噪声可能对故障检测性能产生负面影响。目前，针对提升故障检测对工业环境噪声鲁棒性的研究尚不充分。为此，我们提出伦茨生产背景噪声（LPBN）真实世界数据集，并开发了一套用于齿轮电机出厂检测的自动化噪声鲁棒听觉检测（ARAI）系统。该系统通过声学阵列采集含有轻微故障、严重故障或健康状态的电机数据。基于齿轮箱专家知识，我们提供了一项基准测试，用于比较心理声学特征与不同类型包络特征的性能。据我们所知，这是首次将时变心理声学特征应用于故障检测。我们采用健康电机样本训练当前最先进的单类分类器，并通过阈值分离故障样本实现检测。性能最优的方法达到了0.87（对数包络）、0.86（时变心理声学）和0.91（两者组合）的曲线下面积值。