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\begin{document}

\title{Utilizing Weibull Distribution for Fitting System Data and Time Between
Maintenance Operations to Derive Maintenance Schedules and Parameters
for Critical Equipment in a Textile Industry}



\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{June 18, 2024}


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\begin{abstract}
In the context of a textile industry, where inconsistent maintenance
scheduling and disjointed maintenance strategies could lead to
breakdowns, reduced efficiency, and safety concerns, the need for
reliable maintenance schedules and coherent strategies became paramount.
This study endeavored to address this challenge by harnessing the power
of the Weibull distribution. Its application involved scrutinizing
system data and the time intervals between maintenance operations for
critical equipment, with the overarching goal of deriving maintenance
schedules and parameters that amplified both reliability and
performance. To realize this objective, a methodological approach rooted
in the Weibull distribution was employed. The analysis encompassed not
only failure data examination but also the calculation of the Mean Time
Between Failures (MTBF), offering insights into the system's
reliability. The study delved into the intricate connections among
Weibull distribution parameters, hazard functions, and reliability
functions. To validate the derived models, an array of techniques such
as data fitting, probability plots, and regression analysis were
systematically undertaken. Consequently, the study unveiled a spectrum
of failure patterns contingent upon the shape parameters identified.
These patterns encompassed premature, random, and wear-out failure
modes, each necessitating specific maintenance strategies tailored to
optimize equipment performance and ensure safety. The calculated MTBF
values shed light on the equipment's reliability, while the derived
probability density functions, survival functions, and hazard functions
enriched the comprehensive understanding of the system's behavior. It
was established that a shape of 1.46503 implies that most of the
failures are associated with early wear-out failure. By pinpointing the
failure modes and aligning corresponding maintenance approaches, the
study not only enhanced equipment performance but also elevated safety
standards.The study also proposed avenues for improving analysis
accuracy through diverse data collection, real-time monitoring, and
exploring dynamic parameter adjustments to accommodate evolving
operational conditions.%
\end{abstract}%



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\textbf{Hosted file}

\verb`manuscript august 2.docx`  available at \url{https://authorea.com/users/605352/articles/659224-utilizing-weibull-distribution-for-fitting-system-data-and-time-between-maintenance-operations-to-derive-maintenance-schedules-and-parameters-for-critical-equipment-in-a-textile-industry}

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