LAUSR

Abstract Electrical motors are used in industry and in home appliances. They are important for people. Electrical motors convert electric energy into mechanical work. They generate acoustic signals. In this research the author uses signal processing methods and acoustic signals of two commutator motors of electric coffee grinders and one motor of an electric impact drill. A technique of fault detection of mechanical faults of three commutator motors is presented. Following acoustic signals of the first electric coffee grinder are measured and analysed: healthy, with one missing screw, with a rear faulty sliding bearing and faulty shaft, with a burned out motor (motor off). Following acoustic signals of the second electric coffee grinder are measured and analysed: healthy, with a slightly damaged rear sliding bearing, with a moderately damaged rear sliding bearing, motor off. Following acoustic signals are measured for the electric impact drill: healthy, slightly damaged front bearing, moderately damaged front bearing, motor off. An analysis of acoustic signal was carried out using the developed MSAF-RATIO-24-MULTIEXPANDED-FILTER-8 (Method of Selection of Amplitudes of Frequency Ratio of 24% Multiexpanded Filter 8 Hz) and k-means clustering. The obtained results are very good. A total efficiency of recognition is in the range of 95–96%.