Department of Automatic Control and Robotics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Kraków, Poland.
Sensors (Basel). 2019 Jan 11;19(2):269. doi: 10.3390/s19020269.
Increasing demand for higher safety of motors can be noticed in recent years. Developing of new fault detection techniques is related with higher safety of motors. This paper presents fault detection technique of an electric impact drill (EID), coffee grinder A (CG-A), and coffee grinder B (CG-B) using acoustic signals. The EID, CG-A, and CG-B use commutator motors. Measurement of acoustic signals of the EID, CG-A, and CG-B was carried out using a microphone. Five signals of the EID are analysed: healthy, with 15 broken rotor blades (faulty fan), with a bent spring, with a shifted brush (motor off), with a rear ball bearing fault. Four signals of the CG-A are analysed: healthy, with a heavily damaged rear sliding bearing, with a damaged shaft and heavily damaged rear sliding bearing, motor off. Three acoustic signals of the CG-B are analysed: healthy, with a light damaged rear sliding bearing, motor off. Methods such as: Root Mean Square (RMS), MSAF-17-MULTIEXPANDED-FILTER-14 are used for feature extraction. The MSAF-17-MULTIEXPANDED-FILTER-14 method is also developed and described in the paper. Classification is carried out using the Nearest Neighbour (NN) classifier. An acoustic based analysis is carried out. The results of the developed method MSAF-17-MULTIEXPANDED-FILTER-14 are very good (total efficiency of recognition of all classes-TE = 96%, TECG-A = 97%, TE = 100%).
近年来，人们对电机更高安全性的需求不断增加。开发新的故障检测技术与电机的更高安全性有关。本文介绍了一种使用声信号检测电冲击钻（EID）、咖啡研磨机 A（CG-A）和咖啡研磨机 B（CG-B）故障的技术。EID、CG-A 和 CG-B 使用的是换向器电机。使用麦克风对 EID、CG-A 和 CG-B 的声信号进行了测量。分析了 EID 的 5 个信号：健康、15 个断转子叶片（故障风扇）、弯曲的弹簧、电刷移位（电机关闭）、后滚珠轴承故障。分析了 CG-A 的 4 个信号：健康、后滑动轴承严重损坏、轴损坏且后滑动轴承严重损坏、电机关闭。分析了 CG-B 的 3 个声信号：健康、后滑动轴承轻微损坏、电机关闭。使用了均方根（RMS）、MSAF-17-MULTIEXPANDED-FILTER-14 等方法进行特征提取。本文还开发并描述了 MSAF-17-MULTIEXPANDED-FILTER-14 方法。使用最近邻（NN）分类器进行分类。进行了基于声学的分析。开发的 MSAF-17-MULTIEXPANDED-FILTER-14 方法的结果非常好（所有类别的总识别效率 TE=96%，TECG-A=97%，TE=100%）。
