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\begin{document}

\title{Analyzing Incoherence and Inconsistencies in Data Utilization within
Maintenance Operations for Critical Equipment in the Weaving Section of
Textile Manufacturing Processes}



\author[1]{Bett Kipchumba}%
\author[1]{Peter  Chemweno}%
\author[1]{Eric Nganyi}%
\author[1]{Jerry  Ochola}%
\affil[1]{Moi University}%


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  \date{May 23, 2024}


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\begin{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 disparities, 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
characteristics. The study employed the Weibull distribution plot to
analyze data from 19 distinctive components, with shape (\selectlanguage{greek}β\selectlanguage{english}) and scale
(\selectlanguage{greek}η\selectlanguage{english}) 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 revealed 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 elevating 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.%
\end{abstract}%



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