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Bearing Fault Diagnosis Using Frequency Domain Features and Artificial Neural Networks

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Information and Communication Technology for Intelligent Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 107))

Abstract

Induction motors are widely used as a workhorse in modern industry. Rolling element bearings constitute an important mechanical component in these motors. Fault detection and diagnosis of bearings are of outmost concerns for prevention of machinery malfunction and abrupt failures. Vibration monitoring is considered as most effective technique for finding bearing-related faults. In this paper, frequency domain features are computed from experimentally collected data from three-phase induction motor and used to classify bearing conditions. Feed-forward back-propagation neural network is used to classify different conditions of bearings with high accuracy. Different number of hidden layer neurons and training algorithms are evaluated for their performance. The proposed procedure requires no pre-processing of vibration signal and proves its effectiveness for bearing fault detection.

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Authors and Affiliations

  1. National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh, India

    Amandeep Sharma, Rajvardhan Jigyasu, Lini Mathew & Shantanu Chatterji

Authors
  1. Amandeep Sharma
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  2. Rajvardhan Jigyasu
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  3. Lini Mathew
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  4. Shantanu Chatterji
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Corresponding author

Correspondence to Amandeep Sharma .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

    Suresh Chandra Satapathy

  2. Sabar Institute of Technology, Gujarat Technological University, Ahmedabad, Gujarat, India

    Amit Joshi

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Sharma, A., Jigyasu, R., Mathew, L., Chatterji, S. (2019). Bearing Fault Diagnosis Using Frequency Domain Features and Artificial Neural Networks. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-13-1747-7_52

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