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Condition-Based Maintenance and Residual Life Prediction

By: Chandan Deep Singh (Edited by) , Davinder Singh (Edited by) , Harleen Kaur (Edited by) , Kanwal Jit Singh (Edited by)

Not yet Published

Ksh 40,400.00

Format: Hardback or Cased Book

ISBN-10: 1119933129

ISBN-13: 9781119933120

Publisher: John Wiley & Sons Inc

Imprint: Wiley-Scrivener

Country of Manufacture: GB

Country of Publication: GB

Publication Date: Mar 21st, 2025

Publication Status: Active

Product extent: 304 Pages

Product Classification / Subject(s): Mechanical engineering & materials

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Condition-Based Maintenance and Residual Life Prediction is essential for those looking to effectively implement condition-based maintenance strategies and enhance fault detection through a comprehensive understanding of vibration data analysis and residual life prediction, addressing key challenges in the field. Issues related to condition-based maintenance include its high initial cost, new techniques that can be difficult to implement due to resistance, older equipment that can be difficult to retrofit with sensors and monitoring equipment, and difficult-to-access equipment during production that is difficult to spot-measure. Keeping the above issues in mind, a general handbook for condition-based maintenance and residual life prediction is required to carry out in fault detection. Condition-Based Maintenance and Residual Life Prediction aims to develop, analyze, and model condition-based maintenance and residual life prediction through vibration data. The analysis of vibration responses will aid in developing a fault detection system. The sources of vibration may be due to the presence of different types of defects, such as cracks in the shaft, a bent shaft, or misalignment of shafts. This will give designers a diagnostic tool for predicting the trends of vibration conditions, leading to early fault detection. The devised tool will be capable of quantifying the amplitude of vibration based on the severity of defects. With the features available in the devised diagnostic tool, the proposed model can be used for design, predictive maintenance, and condition-based maintenance.

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