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Identifying Condition Indicators for Artificially Intelligent Fault Classification in Rolling Element Bearings

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Vibration Engineering and Technology of Machinery, Volume I (VETOMAC 2021)

Part of the book series: Mechanisms and Machine Science ((Mechan. Machine Science,volume 137))

Abstract

Bearing condition monitoring is significant in industries due to increased machine reliability and decreased production loss due to machinery breakdown. With the advancement of Artificial Intelligence (AI), Machine Learning (ML) techniques are reasonably useful to build condition monitoring systems for real-world applications. ML algorithms help distinguish faulty bearings from healthy ones and classify the related fault types using the extracted time-domain and frequency-domain features. This study recognizes distinctive features or condition indicators that effectively separate different fault groups and are worthy of training an ML model. Box plot and scatter plot of fault features are used to identify these condition indicators. Vibration datasets representing various faults are taken from the open-source Case Western Reserve University (CWRU) bearing database. A number of time-domain features are extracted from the ensemble data of bearing fault classes, consisting of healthy bearing, inner race fault, ball fault, and outer race fault. Our investigation indicates that more than one condition indicator is better for separating the fault categories. Six different ML models are trained using the condition indicators and the best-performing model is found through the classification accuracy, training time, and prediction speed of the classifier.

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Abbreviations

CWRU:

Case Western Reserve University

\(CF\):

Crest Factor

\(IF\):

Impulse Factor

\({K}_{u}\):

Kurtosis

REB:

Rolling Element Bearing

\({S}_{k}\):

Skewness

\(\overline{X}\):

Mean

\(\sigma\):

Standard Deviation

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Authors and Affiliations

  1. Department of Mechanical Engineering, Aligarh Muslim University, Aligarh, India

    Mohd Atif Jamil & Sidra Khanam

Authors
  1. Mohd Atif Jamil
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  2. Sidra Khanam
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Corresponding author

Correspondence to Mohd Atif Jamil .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati, Assam, India

    Rajiv Tiwari

  2. Department of Mechanical Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India

    Y. S. Ram Mohan

  3. Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Ashish K. Darpe

  4. Department of Mechanical Engineering, B.M.S. College of Engineering, Bengaluru, Karnataka, India

    V. Arun Kumar

  5. Department of Mechanical Engineering, Indian Institute of Technology Patna, Patna, Bihar, India

    Mayank Tiwari

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Jamil, M.A., Khanam, S. (2023). Identifying Condition Indicators for Artificially Intelligent Fault Classification in Rolling Element Bearings. In: Tiwari, R., Ram Mohan, Y.S., Darpe, A.K., Kumar, V.A., Tiwari, M. (eds) Vibration Engineering and Technology of Machinery, Volume I. VETOMAC 2021. Mechanisms and Machine Science, vol 137. Springer, Singapore. https://doi.org/10.1007/978-981-99-4721-8_22

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  • DOI: https://doi.org/10.1007/978-981-99-4721-8_22

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-4720-1

  • Online ISBN: 978-981-99-4721-8

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