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Open AccessArticle

Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals

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 2019, 19(2), 269; https://doi.org/10.3390/s19020269
Received: 20 December 2018 / Revised: 7 January 2019 / Accepted: 8 January 2019 / Published: 11 January 2019
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—TED = 96%, TECG-A = 97%, TECG-B = 100%). View Full-Text
Keywords: motor; mechanical fault; detection; RMS; sound; drill; safety; pattern; bearing; fan; shaft motor; mechanical fault; detection; RMS; sound; drill; safety; pattern; bearing; fan; shaft
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Glowacz, A. Fault Detection of Electric Impact Drills and Coffee Grinders Using Acoustic Signals. Sensors 2019, 19, 269.

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