Electric motors use about 68% of total generated electricity. Fault diagnosis of electrical motors is an important task, because it allows saving a large amount of money and time. An analysis of acoustic signals is a promising tool to improve the accuracy of fault diagnosis. It is essential to analyze acoustic signals to assess the state of the motor. In this paper, three electric impact drills (EID) were analyzed using acoustic signals: healthy EID, EID with damaged rear bearing, EID with damaged front bearing. Three angle grinders (AG) were analyzed: healthy AG, AG with 1 blocked air inlet, AG with 2 blocked air inlets. The authors proposed a method for feature extraction: SMOFS-NFC (Shortened Method of Frequencies Selection Nearest Frequency Components). Acoustic features vectors were classified by the nearest neighbor classifier and Naive Bayes classifier. The classification accuracy were in the range of 89.33–97.33% for three electric impact drills. The classification accuracy were in the range of 90.66–100% for three angle grinders. The presented method is very useful for diagnosis of bearings, ventilation faults and other mechanical faults of power tools. It can be also useful for diagnosis of similar power tools.

电动机消耗的电量约占总发电量的68％。电动机的故障诊断是一项重要的任务，因为它可以节省大量金钱和时间。声信号分析是提高故障诊断准确性的有前途的工具。分析声学信号以评估电动机状态至关重要。在本文中，使用声音信号分析了三个电冲击钻（EID）：健康EID，后轴承损坏的EID，前轴承损坏的EID。分析了三个角向磨光机（AG）：健康的AG，AG的进气口有1个阻塞，AG的进气口有2个阻塞。作者提出了一种特征提取方法：SMOFS-NFC（频率选择最近频率成分的缩短方法）。声学特征向量由最近邻分类器和朴素贝叶斯分类器分类。三个电钻的分类精度在89.33–97.33％的范围内。三台角磨机的分类精度在90.66–100％的范围内。所提出的方法对于轴承，通风故障和电动工具的其他机械故障的诊断非常有用。它对于诊断类似的电动工具也很有用。