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An Automatic Approach to Diagnose Bearing Defects Using Time-Domain Analysis of Vibration Signal

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Advances in Electrical and Computer Technologies (ICAECT 2020)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 711))

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Abstract

Bearing defects are the most frequent occurring faults in any electrical machine. In this perspective, this paper presents a novel time-domain methods incorporating feature reduction method and back propagation feedforward neural network (BPNN) to identify bearing defects. For this, thirty-six standard vibration datasets related to healthy, inner raceway, and ball defects were derived from the Case Western Reserve University (CWRU) website. Four single point defects levels as 7, 14, 21, and 28 mils of inner raceway and ball defects were investigated for effective diagnosis of bearing defects. Initially, nine time-domain features were extracted from each vibration datasets, and then these features were ranked using Fisher’s ranking method to selected top four most discriminating features for effective classification of bearing conditions using BPNN algorithm. The effectiveness of proposed scheme to diagnose bearing defects was corroborated using performance parameters as accuracy (ACC), sensitivity (SE), and specificity (SP). The proposed algorithm has achieved maximum fault classification ACC as 94.87%.

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Author information

Authors and Affiliations

  1. Research Scholar, Electrical Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India

    Om Prakash Yadav

  2. Professor, Department of Electrical Engineering, National Institute of Technology, Kurukshetra, Haryana, India

    G. L. Pahuja

Authors
  1. Om Prakash Yadav
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  2. G. L. Pahuja
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Corresponding author

Correspondence to Om Prakash Yadav .

Editor information

Editors and Affiliations

  1. Department of Electrical and Electronics Engineering, SVS College of Engineering, Coimbatore, Tamil Nadu, India

    Thangaprakash Sengodan

  2. Department of Electronic and Communication Engineering, Kuwait College of Science and Technology, Doha, Kuwait

    M. Murugappan

  3. Department of Computer and Information Sciences, Covenant University, Ota, Nigeria

    Sanjay Misra

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Yadav, O.P., Pahuja, G.L. (2021). An Automatic Approach to Diagnose Bearing Defects Using Time-Domain Analysis of Vibration Signal. In: Sengodan, T., Murugappan, M., Misra, S. (eds) Advances in Electrical and Computer Technologies. ICAECT 2020. Lecture Notes in Electrical Engineering, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-15-9019-1_106

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  • DOI: https://doi.org/10.1007/978-981-15-9019-1_106

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9018-4

  • Online ISBN: 978-981-15-9019-1

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