(untitled) (Journal of Advances and Scholarly Researches in Allied Education, 2024)
Reliability modeling plays a crucial role in assessing the performance and lifespan of industrial systems, especially when subjected to environmental factors such as particulate matter and operational time constraints. This study examines the impact of prolonged operational periods on system reliability, integrating environmental degradation factors into predictive maintenance strategies. Traditional reliability models often overlook the influence of environmental stressors, leading to inaccurate failure predictions and inefficient maintenance schedules. This research incorporates environmental failure parameters, including particulate contamination and temperature fluctuations, into reliability modeling to enhance accuracy in failure forecasting. By integrating data-driven approaches and statistical failure models, the study proposes a framework that improves predictive maintenance strategies, minimizes unplanned downtimes, and optimizes system performance. The findings emphasize the necessity of accounting for environmental conditions in reliability analysis, ensuring a more comprehensive assessment of manufacturing and industrial equipment reliability. The study contributes to advancing maintenance decision-making processes, reducing operational risks, and increasing the efficiency of industrial systems operating under challenging environmental conditions.