The benefits of predictive maintenance in manufacturing excellence: a case study to establish reliable methods for predicting failures

Abstract

In the course of manufacturing excellence, decision makers are consistently confronted with the task of making choices that will enhance and meet the plant's requirements. To this end, it is essential to maintain machines and equipment in a timely manner, which can prove to be one of the primary challenges. Predictive maintenance (PdM) technology can enable real-time maintenance, providing numerous bene ts such as reduced downtime, lower costs, and improved production quality. This article tries to demonstrate e cient physical parameters used in PdM eld. The paper presents a case study operated in industrial production process to compare between the most used algorithm in predicting equipment failures. Future research can improve prediction accuracy with other arti cial intelligence tools.

Advanced ML for predictive maintenance: a case study on remaining useful life prediction and reliability enhancement

“…The objective of predictive maintenance is to reduce costs associated with maintenance activities. Traditional maintenance approaches, such as preventive maintenance, often result in unnecessary maintenance actions, leading to increased costs for equipment replacements and unproductive downtime [3]. The Remaining Useful Life (RUL) refers to the estimated amount of time or usage an asset has before it is expected to fail or become unreliable.…”

Enhancing industrial decision-making through ML-integrated frameworks and multi-criteria decision-making approach

“…Decades of accumulated data-driven statistics unveil that the Analytic Hierarchy Process (AHP) stands as the most widely adopted approach mainly because of the algorithm's simplicity and effectiveness as well as its unique ability to capture and incorporate users' perceptions effectively, particularly when addressing intricate and multifaceted problems while identifying and minimizing inconsistencies in opinions. (Aziz et al, 2016;Munier and Hontoria, 2021;Wu and Tiao, 2018) AHP offers a structured three steps process centered around numerical values through pair-wise comparisons (Dos Santos et al, 2019b). The rst step, involves the construction of a hierarchical structure, where the performance goal assumes the top-level position, criteria are placed at the second level, and alternatives are delineated at the third level.…”