ichsan, M.M. Quantitative Descriptive Decision-Making: Mathematical Models for Bearing Wear Lifetime Stage Diagnosis in Vibration Analysis. Preprints2024, 2024011749. https://doi.org/10.20944/preprints202401.1749.v1
APA Style
ichsan, M.M. (2024). Quantitative Descriptive Decision-Making: Mathematical Models for Bearing Wear Lifetime Stage Diagnosis in Vibration Analysis. Preprints. https://doi.org/10.20944/preprints202401.1749.v1
Chicago/Turabian Style
ichsan, M.M. 2024 "Quantitative Descriptive Decision-Making: Mathematical Models for Bearing Wear Lifetime Stage Diagnosis in Vibration Analysis" Preprints. https://doi.org/10.20944/preprints202401.1749.v1
Abstract
In the evolving landscape of vibration analysis, the integration of machine learning technologies for semi-automated analytical processes presents challenges in establishing clear cause-and-effect relationships in decision-making formulas. This research addresses this gap by exploring analytical diagnosis through residual signals, enabling the identification of anomalies and underperformance in machinery. Vibration signature analysis, focusing on distinctive vibration patterns, proves instrumental in detecting bearing defects and optimizing maintenance for enhanced operational efficiency and safety. Mathematical modeling, particularly worst-case analysis, provides a structured framework for decision-making under uncertainty. The research employs descriptive methods to analyze bearing wear data, transforming it into quantitative data for multiple regression equations. The derived equations reveal the dominance of harmonics in determining bearing wear stages, with PeakVue and overall vibration further indicating damage progression. The findings emphasize the significance of harmonic detection and high-frequency vibration analysis in predicting bearing health, offering a comprehensive approach to enhance machinery reliability. The study concludes with practical recommendations for bearing damage diagnosis based on the developed mathematical models, providing valuable insights for predictive maintenance strategies.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
