Enhancement in Bearing Fault Classification Parameters Using Gaussian Mixture Models and Mel Frequency Cepstral Coefficients Features

• Autorzy: Atmani Youcef , Rechak Said , Mesloub Ammar , Hemmouche Larbi

Identyfikatory

DOI

10.24425/aoa.2020.133149

• Języki publikacji: EN

Abstrakty

EN

Last decades, rolling bearing faults assessment and their evolution with time have been receiving much interest due to their crucial role as part of the Conditional Based Maintenance (CBM) of rotating machinery. This paper investigates bearing faults diagnosis based on classification approach using Gaussian Mixture Model (GMM) and the Mel Frequency Cepstral Coefficients (MFCC) features. Throughout, only one criterion is defined for the evaluation of the performance during all the cycle of the classification process. This is the Average Classification Rate (ACR) obtained from the confusion matrix. In every test performed, the generated features vectors are considered along to discriminate between four fault conditions as normal bearings, bearings with inner and outer race faults and ball faults. Many configurations were tested in order to determinate the optimal values of input parameters, as the frame analysis length, the order of model, and others. The experimental application of the proposed method was based on vibration signals taken from the bearing datacenter website of Case Western Reserve University (CWRU). Results show that proposed method can reliably classify different fault conditions and have a highest classification performance under some conditions.

Słowa kluczowe

EN

bearing faults; Gaussian mixture models; Mel frequency cepstral coefficients; feature extraction; diagnosis

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2020
• Tom: Vol. 45, No. 2
• Strony: 283--295
• Opis fizyczny: Bibliogr. 29 poz., rys., tab., wykr.

Twórcy

autor

Atmani Youcef

• Ecole Nationale Polytechnique – ENP, El Harrach, Algiers, Algeria
• Ecole Nationale Supérieure de Technologie – ENST, Dergana, Algiers, Algeria

autor

Rechak Said

• Ecole Nationale Polytechnique – ENP, El Harrach, Algiers, Algeria

autor

Mesloub Ammar

• Ecole Militaire Polytechnique – EMP, Bordj El Bahri, Algiers, Algeria

autor

Hemmouche Larbi

• Ecole Militaire Polytechnique – EMP, Bordj El Bahri, Algiers, Algeria

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).