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 (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 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.

Validating maintenance strategies is crucial for industrial equipment reliability. Regression analysis establishes correlations between plans and Mean Time Between Failures (MTBF). This study validates maintenance schedules and parameters for critical equipment in a textile factory using regression analysis of system data and maintenance intervals. Employing the Monte Carlo Simulation technique, it analyzes relationships between input variables (maintenance activities, equipment age, operating conditions) and MTBF. An R-squared value of over 0.70 confirms the significance of the regression model. Survey design identifies critical departments, and real-time equipment failure data supports the methodology. Regression analysis yields a significant model (R-squared = 85.56%) with 18 input variables contributing to MTBF variance. Sensitivity analysis reveals their hierarchical impact. Conclusions emphasize regression analysis’s efficacy in validating maintenance strategies, showcasing the input variables’ significance. Findings underscore tailored maintenance plans and suggest predictive analytics expansion. Recommendations include adaptive strategies, predictive analytics integration, optimal maintenance intervals determination, cost-benefit analyses, and spare parts inventory optimization.

Unexpected equipment failure in machines interrupts production schedules and creates costly downtime. Therefore, the importance of timely equipment maintenance is to extend the machine lifespan, prevent unplanned downtime, and reduce the need to buy equipment. Textile factories tend to have overcapacity of looms with inconsistent maintenance time schedules. The main objective of the research was to establish a suitable maintenance schedule time and parameters by assessing the state of maintenance practices of the critical equipment in the weaving section. The maintenance time schedules of rapier, and air-jet looms were studied. Weibull distribution, and Monte Carlo simulation were undertaken followed by regression analysis of the data. The setup of the Monte Carlo simulation entailed 1000 instances of the random values from the systems in the critical equipment. The data were optimized through Monte Carlos regression modeling and Weibull distribution analysis to get shape parameter and the scale parameter of 1.47 and 1683.46 hours. In conclusion, the findings indicated that weaving looms were the critical equipment. The model's shape parameter of 1.47 described a steady increase in the risk of wear-out failure during the early life of the machines. Also, the value of the shape parameter suggested early wear-out failure and premature failures after installation. The optimal time interval for maintenance operations was 1683.46 hours from the scale parameter. The findings indicated that looms had an inconsistent and incoherent maintenance time scheduling approach. According to the results, it is recommended that preventive maintenance schedules be done once every 1683.46 hours.

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.

The study’s foundation is a scenario analysis of a textile mill’s weaving department, with the goal of determining the necessity of a reliable and comprehensive plan for scheduling maintenance time. According to the background information and problem statement, incidents of Run failure maintenance and lengthy downtime (up to 60 days) undermine the machines’ availability (Schmidt, Galar & Wang, 2016).The desired efficiency and production are 90% and 194.76 m, respectively, however, the preliminary result indicate less. This indicates a gap that must be closed by implementing regular and appropriate maintenance plans. Additionally, the the incoherent and inconsistencies points at a lack of an efficient maintenance plan. It was established that the current strategy is not optimized and does not ensure machine availability because there are disparities and irregularities in the maintenance of crucial equipment. The objective of the study was to map out the critical equipment and collect data on the number and time between failures encountered in the weaving section of the textile manufacturing processes. Failure mode and effect analysis and fish-bone diagram were used in the analysis of the data. Mapping results indicates downtime up to 60 days, the productivity was estimated at 194.76 meters, and efficiency was 90%. The results showed that weaving looms were the essential piece of machinery.