Automatic Fault Classification for Journal Bearings Using ANN and DNN

• Autorzy: Narendiranath Babu T. , Aravind A. , Rakesh A. , Jahzan M. , Rama Prabha D. , Ramalinga Viswanathan M.

Identyfikatory

DOI

10.24425/aoa.2018.125166

• Języki publikacji: EN

Abstrakty

EN

Journal bearings are the most common type of bearings in which a shaft freely rotates in a metallic sleeve. They find a lot of applications in industry, especially where extremely high loads are involved. Proper analysis of the various bearing faults and predicting the modes of failure beforehand are Essentials to increase the working life of the bearing. In the current study, the vibration data of a journal Bering in the healthy condition and in five different fault conditions are collected. A feature extraction metod is employed to classify the different fault conditions. Automatic fault classification is performed using artificial neural networks (ANN). As the probability of a correct prediction goes down for a higher number of faults in ANN, the method is made more robust by incorporating deep neural networks (DNN) with the help of autoencoders. Training was done using the scaled conjugate gradient algorithm and the performance was calculated by the cross entropy method. Due to the increased number of hidden layers in DNN, it is possible to achieve a high efficiency of 100% with the feature extraction method.

Słowa kluczowe

EN

journal bearing; fault classification; artificial neural networks; deep neural networks

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2018
• Tom: Vol. 43, No. 4
• Strony: 727--738
• Opis fizyczny: Bibliogr. 22 poz., fot., rys., wykr.

Twórcy

autor

Narendiranath Babu T.

• School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

autor

Aravind A.

• School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

autor

Rakesh A.

• School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

autor

Jahzan M.

• School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

autor

Rama Prabha D.

• School of Electrical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

autor

Ramalinga Viswanathan M.

• Department of Materials Engineering, University of Concepcion, Chile

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).