Recognition of Acoustic Signals of Commutator Motors
Source abstract
Abstract
Most faults can stop a motor, and time is lost in fixing the damaged motor. This is a reason why it is essential to develop fault-detection methods. This paper describes the acoustic-based fault detection of two commutator motors: the commutator motor of an electric impact drill and the commutator motor of a blender. Acoustic signals were recorded by a smartphone. Five states of the electric impact drill and three states of the blender were analysed: for the electric impact drill, these states were healthy, damaged gear train, faulty fan with five broken rotor blades, faulty fan with 10 broken rotor blades, and shifted brush (motor off); for the blender, these states were healthy, faulty fan with two broken rotor blades, and faulty fan with five broken rotor blades. A feature extraction method, MSAF-RATIO-27-MULTIEXPANDED-4-GROUPS (Method of Selection of Amplitudes of Frequency Ratio of 27% Multiexpanded 4 Groups), was developed and used for the computation of feature vectors. The nearest mean (NM) and support vector machine (SVM) classifiers were used for data classification. Analysis of the recognition of acoustic signals was carried out. The analysed value of TEEID (the total efficiency of recognition of the electric impact drill) was equal to 96% for the NM classifier and 88.8% for SVM. The analysed value of TEB (the total efficiency of recognition of the blender) was equal to 100% for the NM classifier and 94.11% for SVM.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Fault Diagnosis Techniques · Engineering Diagnostics and Reliability · Material Properties and Failure Mechanisms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
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
References (40)
Show source-provided references
1. Combined Diagnosis of PD Based on the Multidimensional Parameters
2. Wavelet transforms and pattern recognition on ultrasonic guides waves for frozen surface state diagnosis10.1016/j.renene.2017.03.052 ↗
3. Application of Wavelet Packet Entropy Flow Manifold Learning in Bearing Factory Inspection Using the Ultrasonic Technique10.3390/s150100341 ↗
4. A Comparison Study between Acoustic Sensors for Bearing Fault Detection under Different Speed and Load Using a Variety of Signal Processing Techniques
5. Juengert, A. (2008). Damage Detection in wind turbine blades using two different acoustic techniques. NDT.net e-J. Nondestruct. Test.
6. Adaptive fault feature extraction from wayside acoustic signals from train bearings10.1016/j.jsv.2018.04.004 ↗
7. Fault detection in roller bearing operating at low speed and varying loads using Bayesian robust new hidden Markov model10.1007/s12206-018-0802-8 ↗
8. Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning10.1109/TIE.2017.2767551 ↗
9. Induction machine fault detection using smartphone recorded audible noise10.1049/iet-smt.2017.0104 ↗
10. Development of low-cost non-contact structural health monitoring system for rotating machinery10.1098/rsos.172430 ↗
11. Glowacz, A. (2018). Acoustic-based fault diagnosis of commutator motor. Electronics, 7.10.3390/electronics7110299 ↗
12. Gholamrezaei, S., Alirezaee, S., Ahmadi, A., Ahmadi, M., and Erfani, S. (2015). Sound Target Localization in a 2-D Microphone Array. Canadian Conference on Electrical and Computer Engineering, Proceedings of the 2015 IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE), Halifax, NS, Canada, 3–6 May 2015, IEEE.10.1109/CCECE.2015.7129440 ↗
13. Deep Learning for Infrared Thermal Image Based Machine Health Monitoring10.1109/TMECH.2017.2722479 ↗
14. Huo, Z.Q., Zhang, Y., Sath, R., and Shu, L. (2017). Self-adaptive Fault Diagnosis of Roller Bearings using Infrared Thermal Images. IEEE Industrial Electronics Society, Proceedings of the IECON 2017—43rd Annual Conference of the IEEE Industrial Electronics Society, Beijing, China, 29 October–1 November 2017, IEEE.10.1109/IECON.2017.8217062 ↗
15. Liu, Z.W., Wang, J.J., Duan, L.X., Shi, T.F., and Fu, Q. (2017, January 16–18). Infrared Image Combined with CNN Based Fault Diagnosis for Rotating Machinery. Proceedings of the 2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Shanghai, China.10.1109/SDPC.2017.35 ↗
16. Bearing fault identification of three-phase induction motors bases on two current sensor strategy10.1007/s00500-016-2217-8 ↗
17. Stator fault analysis of three-phase induction motors using information measures and artificial neural networks10.1016/j.epsr.2016.09.031 ↗
18. A Signal-Based Fault Detection and Tolerance Control Method of Current Sensor for PMSM Drive10.1109/TIE.2018.2813991 ↗
19. Detection of half broken rotor bar fault in VFD driven induction motor drive using motor square current MUSIC analysis10.1016/j.ymssp.2018.03.001 ↗
20. Broken rotor bar detection via four-band wavelet packet decomposition of motor current10.1007/s00202-017-0674-4 ↗
21. Recognition of armature current of DC generator depending on rotor speed using FFT, MSAF-1 and LDA10.17531/ein.2015.1.9 ↗
22. Vibration and Temperature Measurement Based Indicator of Journal Bearing Malfunction
23. Vibration-Based Intelligent Fault Diagnosis for Roller Bearings in Low-Speed Rotating Machinery10.1109/TIM.2018.2806984 ↗
24. Misfire and valve clearance faults detection in the combustion engines based on a multi-sensor vibration signal monitoring10.1016/j.measurement.2018.04.062 ↗
25. Semi-automated diagnosis of bearing faults based on a hidden Markov model of the vibration signals10.1016/j.measurement.2018.05.040 ↗
26. Fault detection enhancement in wind turbine planetary gearbox via stationary vibration waveform data10.1177/1461348417725950 ↗
27. Automated vibration-based fault size estimation for ball bearings using Savitzky-Golay differentiators10.1177/1077546317723227 ↗
28. Hamadache, M., Lee, D., Mucchi, E., and Dalpiaz, G. (2018). Vibration-Based Bearing Fault Detection and Diagnosis via Image Recognition Technique under Constant and Variable Speed Conditions. Appl. Sci. Basel, 8.10.3390/app8081392 ↗
29. K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited10.1016/j.ymssp.2015.10.007 ↗
30. Automated feature learning for nonlinear process monitoring—An approach using stacked denoising autoencoder and k-nearest neighbor rule10.1016/j.jprocont.2018.02.004 ↗
31. Xiong, J.B., Zhang, Q.H., Peng, Z.P., Sun, G.X., Xu, W.C., and Wang, Q. (2017). A Diagnosis Method for Rotation Machinery Faults Based on Dimensionless Indexes Combined with K-Nearest Neighbor Algorithm. Math. Probl. Eng., 6872060.10.1155/2017/6872060 ↗
32. Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method10.1186/s10033-018-0202-0 ↗
33. A discrete hidden Markov model fault diagnosis strategy based on K-means clustering dedicated to PEM fuel cell systems of tramways10.1016/j.ijhydene.2018.04.163 ↗
34. Jiang, Z.N., Hu, M.H., Feng, K., and Wang, H. (2018). A SVDD and K-Means Based Early Warning Method for Dual-Rotor Equipment under Time-Varying Operating Conditions. Shock. Vib., 5382398.10.1155/2018/5382398 ↗
35. Shi, Z.L., Song, W.Q., and Taheri, S. (2016). Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings. Entropy, 18.10.3390/e18030070 ↗
36. Fault diagnosis of single-phase induction motor based on acoustic signals10.1016/j.ymssp.2018.07.044 ↗
37. Support vector machine in machine condition monitoring and fault diagnosis10.1016/j.ymssp.2006.12.007 ↗
38. A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines10.1016/j.neucom.2017.07.032 ↗
39. Adaptive neuro-fuzzy inference system for deburring stage classification and prediction for indirect quality monitoring10.1016/j.asoc.2018.01.008 ↗
40. Pajaziti, A., Gojani, I., Shala, A., and Kopacek, P. (2005, January September). Optimization of biped gait synthesis using fuzzy neural network controller. Proceedings of the ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Long Beach, CA, USA. Parts A and B.10.1115/DETC2005-84191 ↗