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Predictive Maintenance of Bearing Machinery Using MATLAB

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Congress on Intelligent Systems

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 111))

Abstract

In recent years, health monitoring of machines has become increasingly important in the manufacturing and maintenance industry. Unexpected failures of machine equipment can have disastrous effects, such as production interruptions and expensive equipment repair. Being one of the most fragile elements of rotating machinery, rolling bearings are a must-have. Failure in machines is a natural phenomenon. Due to this reason, a strong maintenance strategy has to be put in place, so that the interruptions and downtimes can be handled in advance. Predictive maintenance is a technique that tracks equipment performance during regular service using condition monitoring techniques in order to detect and fix possible faults before they cause failure. Predictive maintenance has had a major impact on the manufacturing sector as it lets you find sufficient time to plan ahead of the machine failure. This helps in reducing the time to re-initiate the machine after it has been repaired. It also helps in pinpointing problems in our machines and giving information on the parts which need to be repaired before they reach their useful life. Therefore, using a predictive maintenance approach, we not only reduce machine downtime but also help in reducing repair cost. As a result, this method is adaptable and can be used in a variety of situations and be useful in diagnosis of a large number of machines. Signal processing and vibration analysis methods implemented in MATLAB can be effective for understanding real-time machine status. Extracting time-domain features from machine data using spectral kurtosis and envelope spectrum techniques, predictive machine maintenance can be achieved since the unplanned downtimes and maintenance expenses can be reduced if industrial machinery breaks.

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Acknowledgements

The dataset was downloaded from the MFPT website at https://www.mfpt.org/fault-data-sets/ [21, 25].

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Authors and Affiliations

  1. Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), Pune, India

    Karan Gulati, Shubham Tiwari, Keshav Basandrai & Pooja Kamat

Authors
  1. Karan Gulati
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  2. Shubham Tiwari
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  3. Keshav Basandrai
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  4. Pooja Kamat
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Corresponding author

Correspondence to Karan Gulati .

Editor information

Editors and Affiliations

  1. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, India

    Mukesh Saraswat

  2. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India

    Harish Sharma

  3. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    K. Balachandran

  4. Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Gulati, K., Tiwari, S., Basandrai, K., Kamat, P. (2022). Predictive Maintenance of Bearing Machinery Using MATLAB. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 111. Springer, Singapore. https://doi.org/10.1007/978-981-16-9113-3_10

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