Early detection of factory machinery malfunctions is crucial in industrial applications. In machine anomalous sound detection (ASD), different machines exhibit unique vibration-frequency ranges based on their physical properties. Meanwhile, the human auditory system is adept at tracking both temporal and spectral dynamics of machine sounds. Consequently, integrating the computational auditory models of the human auditory system with machine-specific properties can be an effective approach to machine ASD. We first quantified the frequency importances of four types of machines using the Fisher ratio (F-ratio). The quantified frequency importances were then used to design machine-specific non-uniform filterbanks (NUFBs), which extract the log non-uniform spectrum (LNS) feature. The designed NUFBs have a narrower bandwidth and higher filter distribution density in frequency regions with relatively high F-ratios. Finally, spectral and temporal modulation representations derived from the LNS feature were proposed. These proposed LNS feature and modulation representations are input into an autoencoder neural-network-based detector for ASD. The quantification results from the training set of the Malfunctioning Industrial Machine Investigation and Inspection dataset with a signal-to-noise (SNR) of 6 dB reveal that the distinguishing information between normal and anomalous sounds of different machines is encoded non-uniformly in the frequency domain. By highlighting these important frequency regions using NUFBs, the LNS feature can significantly enhance performance using the metric of AUC (area under the receiver operating characteristic curve) under various SNR conditions. Furthermore, modulation representations can further improve performance. Specifically, temporal modulation is effective for fans, pumps, and sliders, while spectral modulation is particularly effective for valves.

翻译：工厂机械故障的早期检测在工业应用中至关重要。在机器异常声音检测（ASD）中，不同机器根据其物理特性表现出独特的振动频率范围。与此同时，人类听觉系统擅长追踪机器声音的时域和频域动态。因此，将人类听觉系统的计算听觉模型与机器特定属性相结合，可成为机器ASD的一种有效方法。我们首先使用费希尔比（F-ratio）量化了四类机器的频率重要性。量化的频率重要性随后被用于设计机器专用的非均匀滤波器组（NUFB），以提取对数非均匀频谱（LNS）特征。所设计的NUFB在F-ratio相对较高的频率区域具有更窄的带宽和更高的滤波器分布密度。最后，提出了从LNS特征导出的谱调制和时调制表示。这些提出的LNS特征及调制表示被输入到基于自编码器神经网络的检测器中进行ASD。在信噪比（SNR）为6 dB的故障工业机器调查与检测数据集训练集上的量化结果表明，不同机器的正常与异常声音之间的区分信息在频域中是非均匀编码的。通过使用NUFB突出这些重要频率区域，LNS特征能够在各种SNR条件下，基于AUC（受试者工作特征曲线下面积）指标显著提升性能。此外，调制表示能进一步改善性能。具体而言，时调制对风扇、泵和滑块有效，而谱调制对阀门尤其有效。