Fault diagnosis of angle grinders and electric impact drills using acoustic signals
Authors:
- Adam Glowacz,
- Ryszard Tadeusiewicz,
- Stanislaw Legutko,
- Wahyu Caesarendra,
- Muhammad Irfan,
- Hui Liu,
- Frantisek Brumercik,
- Miroslav Gutten,
- Maciej Sulowicz,
- Jose Alfonso Antonino Daviu,
- Thompson Sarkodie-Gyan,
- Pawel Fracz,
- Anil Kumar,
- Jiawei Xiang
Abstract
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 othermechanical faults of power tools. It can be also useful for diagnosis of similar power tools.
- Record ID
- CUT5339806de5f74dd9af6cfe8ef2929008
- Publication categories
- ;
- Author
- Journal series
- Applied Acoustics, ISSN 0003-682X, e-ISSN 1872-910X
- Issue year
- 2021
- Vol
- 179
- Pages
- [1-14]
- Article number
- 108070
- Other elements of collation
- fot.; schem.; tab.; wykr.; Bibliografia (na s.) - 13-14; Bibliografia (liczba pozycji) - 54; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 179
- Keywords in English
- degradation, acoustic, fault diagnosis, bearings, power tool, ventilation
- DOI
- DOI:10.1016/j.apacoust.2021.108070 Opening in a new tab
- URL
- https://www.sciencedirect.com/science/article/pii/S0003682X21001638 Opening in a new tab
- Language
- eng (en) English
- Score (nominal)
- 100
- Publication indicators
- Citation count
- 145
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT5339806de5f74dd9af6cfe8ef2929008/
- URN
urn:pkr-prod:CUT5339806de5f74dd9af6cfe8ef2929008
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.