Design of Industrial Equipment Maintenance Analysis Methodology through the use of Kalman Filter and Dynamic Bayesian Networks
Abstract
Due to the high level of industrial competition companies strive to enhance their operational efficiency by reducing costs without affecting the quality of their products. One of the most common approaches to achieving this goal is optimizing the operation of production equipment. Therefore, this research develops a methodology for failure analysis aimed at identifying root causes and improving equipment and machinery performance. The methodology employs dynamic Bayesian network for failure analysis. This tool provides valuable information about the probability of failure occurrence, allowing the prioritization of corrective actions to eliminate failures and reduce their incidence. Additionally, for equipment requiring continuous monitoring, the Kalman filter and, when applicable, the extended Kalman filter are employed., Its purpose is to eliminate noise in the data acquisition process, ensuring reliable information for analysis. Moreover, it enables the accurate estimation of certain variables in locations where direct measurement is challenging or unfeasible. Implementing this methodology leads to substantial improvements in the failure analysis process, making corrective actions more effective in eliminating failures.
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References
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