Application of EMD ANN and DNN for Self-Aligning Bearing Fault Diagnosis

• Autorzy: Narendiranath B. T. , Aravind A. , Rakesh A. , Jahzan M. , Rama P. D.

Identyfikatory

DOI

10.24425/122364

• Języki publikacji: EN

Abstrakty

EN

Self-aligning roller bearings are an integral part of the industrial machinery. The proper analysis and prediction of the various faults that may happen to the bearing beforehand contributes to an increase in the working life of the bearing. This study aims at developing a novel method for the analysis of the various faults in self-aligning bearings as well as the automatic classification of faults using artificial neural network (ANN) and deep neural network (DNN). The vibration data is collected for six different faults as well as for the healthy bearing. Empirical mode decomposition (EMD) followed by Hilbert Huang transform is used to extract instantaneous frequency peaks which are used for fault analysis. Time domain and time-frequency domain features are then extracted which are used to implement the neural networks through the pattern recognition tool in MATLAB. A comparative study of the outputs from the two neural networks is also performed. From the confusion matrix, the efficiency of the ANN has been found to be 95.7% and using DNN has been found to be 100%.

Słowa kluczowe

EN

self-aligning bearing; fault classification; artificial neural networks; deep neural network

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2018
• Tom: Vol. 43, No. 2
• Strony: 163--175
• Opis fizyczny: Bibliogr. 17 poz., fot., rys., tab., wykr.

Twórcy

autor

Narendiranath B. T.

• School of Mechanical Engineering, VIT University, Vellore, Tamil Nadu, India

autor

Aravind A.

• School of Mechanical Engineering, VIT University, Vellore, Tamil Nadu, India

autor

Rakesh A.

• School of Mechanical Engineering, VIT University, Vellore, Tamil Nadu, India

autor

Jahzan M.

• School of Mechanical Engineering, VIT University, Vellore, Tamil Nadu, India

autor

Rama P. D.

• School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).