Sep 2026· International Journal of Science and Research Archive· Vol 20, pp. 569-583· 0 citations
Abstract
Smart industrial systems must balance equipment reliability, energy use, maintenance cost, production continuity, and technical resources. This study proposes a multi-objective predictive maintenance and energy-aware resource optimization framework for industrial decision-making. The framework combines equipment health assessment, Remaining Useful Life (RUL) estimation, anomaly detection, failure probability, and maintenance prioritization. K-means clustering determines the Anomaly Level (AL), while Weibull survival analysis estimates Failure Probability (FP). These measures form a Hybrid Risk Index (HRI) for determining the Optimal Maintenance Point (OMP). NSGA-II evaluates maintenance timing, asset selection, and technician allocation under maintenance cost, energy, quality, and production objectives. Performance is compared with preventive maintenance, condition based maintenance, and reliability only optimization under changing industrial conditions.
This study investigates a multi‐machine, multi‐period preventive maintenance and replacement scheduling problem under reliability deterioration. Each machine is modeled as a repairable system whose degradation follows a power‐law process, while maintenance actions influence future reliability through an effective‐age...
Chih-Chiang Fang· Quality and Reliability Engi...· 0 citations
An integrated framework combining supervised machine learning classification with mathematical optimization to predict equipment failures and minimise maintenance costs under prediction uncertainty is developed, ensuring prediction uncertainty propagates into scheduling decisions and bridging predictive analytics with...
Nooshin Salehabadi, Ming-Yuan Chen· Journal of Quality in Mainte...· 0 citations
Under random inspection, some partial system maintenance models still do not account for dependence among components, which may bias system reliability assessment and consequently affect the optimality of maintenance strategies. To address this issue, this paper proposes an optimal preventive maintenance (PM) strateg...
Wen-Qi Wang, Gui-Mei Jiao· Quality and Reliability Engi...· 0 citations
The results demonstrate that integrating machine learning with predictive maintenance strategies significantly improves system reliability, reduces downtime, and enhances overall solar farm efficiency.
Oyiogu Dennis, Nwokporo Sunday Celestine· International journal of re...· 0 citations
This study develops an integrated Reliability-Centered Maintenance (RCM) framework combining Failure Mode and Effects Analysis (FMEA), Weibull reliability modeling, and cost-based preventive-maintenance interval optimization for critical production machinery. Using 48 corrective-maintenance records collected between Ma...
S. H. Yuningsih, Kiki Zakaria, V. Rusyn· Operations Research: Interna...· 0 citations
As critical rotating machinery at gas transmission stations, air compressors suffer from limitations of fixed‐interval maintenance during long‐term operation, which fails to reflect actual equipment health, while maintenance threshold setting and rapid degradation identification remain highly experience‐dependent. Th...
