Analysis of the possibility of using an audio signal to detect the state of the machine based on the sound it generates

• Autorzy: Majda-Zdancewicz Ewelina , Pierzak Wiktor

Identyfikatory

DOI

10.15199/48.2025.02.49

Warianty tytułu

PL

Analiza możliwości wykorzystania sygnału akustycznego do detekcji stanu maszyny na bazie generowanego przez nią dźwięku

• Języki publikacji: EN

Abstrakty

EN

As part of the experiments, research was carried out to detect the condition of the electric machine. In the first place, a laboratory stand was designed to test acoustic signals. This process included, m.in, the selection of an electric machine, load system, measuring devices, recording device, and the definition of its operating states. The recordings were recorded in the Laboratory of Electrical Machines at the Institute of Electronic Systems of the Faculty of Electronics of the Military University of Technology using the SZUAa54a induction motor. The feature extraction process involved the generation of spectral descriptors. The method of decision trees and the k nearest neighbor method were used as the classification algorithm. The proposed method gives an efficiency of 95.53%.

PL

W ramach eksperymentów przeprowadzono badania mające na celu wykrycie stanu maszyny elektrycznej. W pierwszej kolejności zaprojektowano stanowisko laboratoryjne do badania sygnałów akustycznych. Proces ten obejmował m.in. dobór maszyny elektrycznej, układu obciążeniowego, urządzeń pomiarowych, urządzenia rejestrującego oraz określenie jego stanów pracy. Nagrania rejestrowano w Laboratorium Maszyn Elektrycznych Instytutu Systemów Elektronicznych Wydziału Elektroniki Wojskowej Akademii Technicznej przy użyciu silnika indukcyjnego SZUAa54a. Proces ekstrakcji cech obejmował generację deskryptorów widmowych. Jako algorytm klasyfikacji zastosowano metodę drzew decyzyjnych oraz metodę najbliższego sąsiada. Proponowana metoda daje sprawność 95,53%.

Słowa kluczowe

EN

diagnostic; acoustic signal; feature extraction; machine condition detection

PL

diagnostyka; sygnał akustyczny; ekstrakcja cech; detekcja stanu maszyny

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2025
• Tom: R. 101, nr 2
• Strony: 219--223
• Opis fizyczny: Bibliogr. 27 poz., rys., tab.

Twórcy

autor

Majda-Zdancewicz Ewelina

• Military University of Technology, Faculty of Electronics, ul. Gen. Sylwestra Kaliskiego 2, 00-908 Warsaw

• https://orcid.org/0000-0001-5435-6246

autor

Pierzak Wiktor

• Military University of Technology, Faculty of Electronics, ul. Gen. Sylwestra Kaliskiego 2, 00-908 Warsaw

Bibliografia

• [1] Abramov IV, Nikitin YR, Abramov AI, Sosnovich EV, Bozek P. Control and Diagnostic Model of Brushless Dc Motor. Journal of Electrical Engineering-Elektrotechnicky Casopis 2014; 65 (5): 277-282.
• [2] Cempel Cz., Diagnostyka wibroakustyczna maszyn, PWN, Warszawa, 1989.
• [3] Cempel Cz., Tomaszewski F.: Diagnostyka Maszyn. Zasady ogólne. Przykłady zastosowań., Radom 1992.
• [4] Bagavathiappan S, Lahiri BB, Saravanan T, et al. Infrared thermography for condition monitoring – A review. Infrared Phys Technol 2013; 60: pp. 35–55.
• [5] Głowacz A., Diagnostyka maszyny prądu stałego oparta na rozpoznawaniu dźwięku z zastosowaniem LPC i GSDM, Przegląd Elektrotechniczny (Electrical Review), Vol. 86, No. 6/2010, pp. 243-24
• [6] AlShorman O, Alkahatni F, Masadeh M, et al. Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study. Advances in Mechanical Engineering. 2021;13(2).
• [7] Medoued A, Mordjaoui M, Soufi Y, et al. Induction machine bearing fault diagnosis based on the axial vibration analytic signal. Int J Hydrogen Energ 2016; 41: pp. 12688–12695
• [8] Sinha JK, Elbhbah K. A future possibility of vibration based condition monitoring of rotating machines. Mech Syst Signal Pr 2013; 34: pp. 231–240.
• [9] Jadin MS, Taib S. Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography. Infrared Phys Technol 2012; 55: 236–245.
• [10] Diallo D, Benbouzid MEH, Hamad D, et al. Fault detection and diagnosis in an induction machine drive: a pattern recognition approach based on concordia stator mean current vector. IEEE Trans Energ Conversion 2005; 20: pp. 512–519.
• [11] Lopes TD, Goedtel A, Palácios RHC, et al. Bearing fault identification of three-phase induction motors bases on two current sensor strategy. Soft Comput 2016; 21: pp. 6673–6685.
• [12] Roscoe NM, Judd MD. Harvesting energy from magnetic fields to power condition monitoring sensors. IEEE Sensor J 2013; 13: pp. 2263–2270.
• [13] Kim EY, Tan AC, Mathew J, et al. Condition monitoring of low speed bearings: A comparative study of the ultrasound technique versus vibration measurements. Aust J Mech Eng 2008; 5: pp. 177–189.
• [14] Feng S, Fan B, Mao J, et al. Prediction on wear of a spur gearbox by on-line wear debris concentration monitoring. Wear 2015; 336–337: pp. 1–8.
• [15] Loutas T, Roulias D, Pauly E, et al. The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mech Syst Signal Pr 2011; 25: pp. 1339–1352.
• [16] Van Hecke B, Yoon J, He D. Low speed bearing fault diagnosis using acoustic emission sensors. Appl Acoust 2016; 105: pp. 35–44.
• [17] Holguín-Londoño M, Cardona-Morales O, Sierra-Alonso EF, et al. Machine fault detection based on filter bank similarity features using acoustic and vibration analysis. Math Prob Eng 2016; 2016: pp. 1–14.
• [18] Kurek J., Osowski S., Diagnostic feature selection for efficient recognition of different faults of rotor bars in the induction machine, Przegląd Elektrotechniczny (Electrical Review), Vol. 86, n. 1/2010, pp. 121–123
• [19] Diagnostics of Direct Current motor with application of acoustic signals, reflection coefficients and K-Nearest Neighbor classifier
• [20] Kim J, Kim J-M. Bearing fault diagnosis using grad-CAM and acoustic emission signals. Appl Sci 2020; 10: 2050.
• [21] Barusu MR, Sethurajan U, Deivasigamani M. Non-invasive method for rotor bar fault diagnosis in three-phase squirrel cage induction motor with advanced signal processing technique. J Eng 2019; 2019: 4415–4419. Crossref.
• [22] Li H, Feng G, Zhen D, et al. A normalized frequency domain energy operator for broken rotor bar fault diagnosis. IEEE Trans Instrument Measure 2020. PubMed.
• [22] Glowacz A. Recognition of acoustic signals of synchronous motors with the use of MoFS and selected classifiers. Measurement Sci Rev 2015; 15: pp. 167–175.
• [23] Pan Q, Zhou R, Su J, et al. Automatic localization of the rotorstator rubbing fault based on acoustic emission method and higher-order statistics. J Mech Sci Technol 2019; 33:pp. 513– 524.
• [24]https://www.mathworks.com/help/audio/ref/audiofeatureextracto r.html
• [25] Peeters, G. A Large Set of Audio Features for Sound Description (Similarity and Classification) in the CUIDADO Project; CUIDADO: Paris, French, 2014
• [26] Glowacz A., Głowacz Z., Diagnostics of DC Machine Based on Analysis of Acoustic Signals with Application of MFCC and Classifier Based on Words, March 2012, Archives of Metallurgy and Materials
• [27] Diagnostics of Direct Current motor with application of acoustic signals, reflection coefficients and K-Nearest Neighbor classifier, Przegląd Elektrotechniczny (Electrical Review), Vol. 88, n. 5a/2012, pp. 231–233