Vibration and acoustic emission have received great attention of the research community for condition-based maintenance in rotating machinery. Several signal processing algorithms were either developed or used efficiently to detect and classify faults in bearings and gears. These signals are recorded, using sensors like tachometer or accelerometer, connected directly or mounted very close to the system under observation. This is not a feasible option in case of complex machinery and/or temperature and humidity. Therefore, it is required to sense the signals remotely, in order to reduce installation and maintenance cost. However, its installation far away from the intended device may pollute the required signal with other unwanted signals. In an attempt to address these issues, sound signal-based fault detection and classification in rotating bearings is presented. In this research work, audible sound of machine under test is captured using a single microphone and different statistical, spectral and spectro-temporal features are extracted. The selected features are then analyzed using different machine learning techniques, such as K-nearest neighbor (KNN) classifier, support vector machine (SVM), kernel liner discriminant analysis (KLDA) and sparse discriminant analysis (SDA). Simulation results show successful classification of faults into ball fault, inner and outer race faults. Best results were achieved using the KLDA followed by SDA, KNN and SVM. As far as features are concerned, the average FFT outperformed all the other features, followed by average PSD, RMS values of PSD, PSD and STFT.

中文翻译：

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利用声音信号自动有效地检测旋转机械中的故障

振动和声发射已引起研究界对旋转机械中基于状态的维护的高度重视。已开发或有效使用了几种信号处理算法来检测和分类轴承和齿轮中的故障。这些信号是使用转速计或加速度计等传感器记录的，这些传感器直接连接或安装在非常靠近被观察系统的位置。在复杂的机械和/或温度和湿度的情况下，这不是可行的选择。因此，需要远程感测信号，以减少安装和维护成本。但是，将其安装在距离预期设备较远的地方可能会污染所需的信号以及其他有害信号。为了解决这些问题，提出了基于声音信号的旋转轴承故障检测与分类方法。在这项研究工作中，使用单个麦克风捕获被测机器的声音，并提取不同的统计，频谱和光谱时间特征。然后使用不同的机器学习技术来分析选定的特征，例如K近邻（K NN）分类器，支持向量机（SVM），核线性判别分析（KLDA）和稀疏判别分析（SDA）。仿真结果表明，将故障成功分类为球形故障，内外故障。使用KLDA，SDA，K NN和SVM可获得最佳结果。就特征而言，平均FFT优于所有其他特征，其次是平均PSD，PSD的RMS值，PSD和STFT。