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:在监测机械部件的健康状况方面,基于声波的断层检测潜力很大,然而,工业环境的背景噪音可能会对发现故障的性能产生消极影响。对改进对工业环境噪音的故障检测力度给予了有限的注意。因此,我们介绍冷冻生产背景噪音(LPBN)真实世界数据集和自动和噪声摄像检查系统(ARAI),用于对配对发动机进行终端检查。声波阵列用于从有轻微过错、重大过错或健康的发动机获取数据。根据对变速箱的专家知识,提供了一种基准,将心理声学特征与不同类型的信封特征进行比较。我们最了解的是,我们首先采用时间变化的心理感应特征来检测故障。我们用一个最先进的单级级测试器对健康马达标的样品进行了培训,用临界值分别对故障进行检测。最佳表现的方法在0.87的曲线下,即0.86(阵列),0.86和0.86(同步),以及0.86(同步式),(同步式),以及0.86)。</s>