Abstract:
In the context of textile manufacturing’s weaving section, efficient maintenance operations play a pivotal role in upholding
critical equipment’s peak performance and longevity. However, inconsistencies in data utilization during maintenance can lead
to equipment failures, downtimes, and decreased efficiency. To address this, this study endeavors to scrutinize these data dis
parities, focusing on the weaving section’s essential machinery. The objective encompasses identifying failure patterns, gauging
parameter impacts on system components, and proposing personalized maintenance strategies based on failure characteris
tics. The study employed the Weibull distribution plot to analyze data from 19 distinctive components, with shape (β) and
scale (η) parameters elucidating failure trends, distinguishing early-life and wear-out failures. The Anderson-Darling (AD)
statistic validated Weibull fitting. Visual aids and charts presented findings effectively. Analysis showcased distinct failure
patterns across system components, where shape parameters exceeding 1 denoted wear-out failures, and scale parameters re
vealed equipment lifespans. The study emphasized the necessity of bespoke maintenance approaches in response to equipment
failure traits. Tailoring strategies for early-life and wear-out failures is essential. The Weibull analysis aids in pinpointing
crucial maintenance junctures, optimizing schedules, and enhancing equipment reliability. This study’s contribution lies in ele
vating equipment dependability, curbing downtimes, and augmenting operational efficiency in textile manufacturing processes.
Recommendations encompass tailored maintenance strategies, prioritized preventive measures for wear-out-prone components,
comprehensive craftsman training, and exploring predictive techniques leveraging sensor data and AI.
