Statistical mapping and data collection of critical equipment failures in the weaving section of textile manufacturing

The study's foundation was 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. The desired efficiency and production are 90% and 194.76 m, respectively; however, the preliminary results indicated less. This indicated a gap that must be closed by implementing regular and appropriate maintenance plans. Additionally, 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 indicated downtime up to 60 days, the productivity was estimated at 194.76 m, and efficiency was 90%. The results showed that the critical equipment were the essential piece of machinery in the production line of a textile mill.

emphasizes the need for an effective maintenance time scheduling strategy in a fabric manufacturing factory.The paper presents several innovative points that contribute to the field of maintenance strategies in the weaving section of textile mills.It emphasizes the importance of implementing a consistent and coherent maintenance time scheduling strategy to improve machine availability and prevent breakdowns.The study identifies discrepancies and inconsistencies in the maintenance of critical equipment and explores the impact of run-to-failure maintenance and prolonged downtime on machine availability and overall productivity. 1The paper highlights the significance of implementing an effective maintenance strategy to enhance machine reliability, extend machine life, and reduce maintenance and replacement costs.Utilizing mapping and data collection techniques, the study assesses failure frequency, downtime, and time between maintenance for critical equipment.The analysis of failure data helps determine root causes, categorize failures, and assign risk probability numbers.The study uncovers factors such as improper use, inadequate materials, overstressed components, improper setup, improper installation, power surges, handling damages, and poor quality control that contribute to failure.Overall, this research provides valuable insights into improving maintenance strategies and optimizing productivity in the weaving section of textile mills.
The paper begins by providing an overview of a textile mill as a manufacturing industry.It then proceeds to explore and analyze maintenance strategies in the weaving section using methods such as Failure Mode and Effect Analysis (FMEA) and the Fishbone Diagram.The methodology involves data collection through interviews, questionnaires, and real-time monitoring of maintenance activities.The critical equipment is identified, and a Fishbone Diagram is utilized to uncover root causes.The results and discussions focus on assessing criticality, modeling the maintenance strategy, and drawing deductions.In conclusion, the paper offers recommendations for optimizing maintenance strategies in the weaving section of the textile mill.

Background
A textile mill is a manufacturing industry that encompasses various processes, including spinning, weaving, dyeing, printing, garment finishing, and garment manufacturing.Each of these processes contributes to the production line, with specific production targets.However, these targets are not being met due to inconsistencies in the maintenance processes and schedules.In a production line, it is essential for all machines to operate without breakdowns that may require halting the entire production line.Therefore, the reliability, maintainability, and operability of the machines become crucial factors. 1 The primary purpose of performing maintenance is to extend the machine's life and increase the time between failures.Implementing a better maintenance strategy can reduce machine interruptions, thus improving reliability.Machine reliability is paramount to the productivity of a facility, especially when proper maintenance strategies are employed to enhance production. 2 A well-designed maintenance strategy can significantly reduce maintenance requirements by up to 80% and minimize production losses. 3The weaving section in the textile mill heavily relies on a breakdown maintenance strategy and schedules, with little emphasis on preventive maintenance scheduling. 4Unfortunately, some of the planned preventive maintenance schedules in the weaving section are overlooked, with more attention given to lubrication and dusting off the machines.Textile mills have three weaving technologies in the weaving department: projectile, rapier, and air jet.Within the rapier weaving technology, there exist dobby and tappet mechanisms.A dobby loom refers to a loom that controls all the warp yarns using a dobby system.Tappets imply a shedding mechanism by a loom that utilizes a tool placed on the peak of the loom to create patterns using a limited number of healds developed by the tappet and cam motion.In the weaving department, the machine efficiency is low, making it a critical section within the weaving department.There are 88 machines that play a crucial role in fabric formation, and an overview of a section of these machines provides important insights into maintenance.
A preliminary survey on the critical equipment shows the effectiveness of current strategies in terms of availability.21% of the machines are available throughout the shift without stoppages.Additionally, downtime ranges from 10 min to 24 h, while other machines can stop working for up to 60 days.The time needed to scan for failure ranges from 10 to 30 min.The target efficiency and production are 90% and 194.76 m, respectively, which point to a gap that needs to be addressed through the adoption of consistent and proper maintenance schedules.Furthermore, the situation depicted in Figure 1 indicates a lack of an optimized maintenance strategy.The weaving equipment forms a critical section due to dominant cases of breakdown maintenance (run to failure maintenance) and inconsistencies in preventive maintenance strategies.The critical equipment in the weaving section lacks a clear maintenance program and relies on the experience of the maintenance crew rather than the original equipment manufacturer (OEM).A preliminary survey on failure, downtime, F I G U R E 1 A scenario of a cleaned machine part with a brush leaving no choked gears with waste.Illustration of a cleaned machine part, highlighting effective waste removal and unobstructed gears using a brush.The images depict a loom weaving machine from a textile mill, specifically showcasing the rapier weaving technology.Notably, in this textile mill, there are 88 machines utilizing various mechanisms, including dobby and tappet.Figure presents the as-is scenario and the to-be scenario where maintenance practices have been used to restore the machine component.The as-is scenario depicts the machine part in its original state, showing choked gears and waste buildup.However, in the to-be scenario, the machine part has been meticulously cleaned using a brush, resulting in a pristine condition with no choked gears or waste.This transformation highlights the effectiveness of maintenance practices in enhancing the performance and longevity of machine components.
availability, productivity, and efficiency reveals that the scheduling of machine maintenance is not coherent.The data points to the need for a robust maintenance scheduling program, an effective failure analysis, and a more appropriate approach to maintenance.Furthermore, Figure 1 indicates a lack of optimized and consistent maintenance practices that are necessary to increase equipment life and prevent premature failure. 5Additionally, operating the machines under the "not good" situation depicted in Figure 1 increases the likelihood of wear-out failures.

1.2
The underlying gaps in the approach of maintenance and method used The research is based on a comprehensive situation analysis of the weaving section in a textile mill, which aims to address the gaps and inconsistencies in maintenance practices.It is crucial to establish a consistent and coherent maintenance time scheduling strategy to ensure machine availability and prevent breakdowns.The existing strategy, characterized by run-to-failure maintenance and prolonged downtime, compromises machine availability and incurs increased maintenance and replacement costs.Thus, an effective maintenance strategy is necessary to optimize productivity in fabric manufacturing factories.This paper presents innovative points that contribute to the field of maintenance strategies in the weaving section of textile mills.By assessing failure frequency, downtime, and time between maintenance for critical equipment, mapping and data collection techniques are employed.Root causes of failures are determined, and failures are classified as mechanical or electrical based on severity and probability.The collected failure data includes various factors such as improper use, inadequate materials, overstressed components, improper setup, improper installation, power surges, handling damages, and poor quality control.The goals of 90% efficiency and 194.76 m of production in the textile mill indicate the desired level of productivity and operational performance.Currently, the factory falls short of these goals due to discrepancies in maintenance practices leading to increased downtime and reduced output.The factory is still lacking in the measures of maintenance.To improve output, a more proactive maintenance strategy, training for maintenance personnel, optimized workflow processes, and efficient production planning can be implemented.These improvements may involve financial investments but can contribute to closing the performance gap and achieving the desired goals.On the overall cost, continuous monitoring and adjustment of maintenance strategies and operational processes are essential for sustained progress.

Methods used
A comprehensive and scientifically rigorous approach is employed in this study.The mapping of critical equipment and associated systems or parts in the weaving section is conducted as the initial step.This mapping process provides a clear understanding of the key components involved.Subsequently, data collection is carried out to capture information on the frequency of failures and the duration between maintenance events for the identified critical equipment.To ensure accuracy and reliability, real-time data are collected through in-depth interviews with relevant personnel, who possess valuable insights and knowledge.Additionally, questionnaires are administered to gather additional information and perspectives.By employing these robust data collection methods, the study ensures the validity and scientific rigor of the findings.

Obtaining data
A comprehensive study was conducted on a total of 88 machines in the weaving section, along with 10 machines in the sizing section, 6 machines in the yarn warping section, and 4 machines in the yarn winding section.The research focused on capturing both mechanical and electrical failures that occurred within these machines.Additionally, the study examined the preparedness and capabilities of the personnel in responding to these failures.By considering a wide range of machine types and failure scenarios, the research aims to provide a holistic understanding of the maintenance challenges and opportunities in the weaving section of the textile mill.This approach ensures that the findings are robust and applicable to the real-world operational context.

Failure data
The failure data obtained in this study plays a crucial role in understanding the distribution of failures across different departments.It provides valuable insights into the quantity of failures occurring in each department, allowing for a comprehensive analysis.Moreover, when a failure event takes place, the duration of machine downtime is meticulously recorded to facilitate the optimization of maintenance practices.The impact of failures extends beyond mere machine downtime, encompassing various aspects such as the maintenance team's involvement, potential penalties, idle machines, missed profit opportunities, reduced machine efficiency, and overall loss of production.By collaborating with the OEM, the study investigates the root causes of failures specifically in critical machines.Classification of failures into mechanical or electrical categories is based on the severity and probability, thereby providing a comprehensive understanding of failure dynamics within the department's control. 6The determination of risk probability numbers further enhances the accuracy of the analysis.The collected failure data encompasses a wide range of cases, including instances of improper use, inadequate materials, overstressed components, improper setup, improper installation, power surges, handling damages, and poor quality control. 6By systematically examining these factors, the study ensures a robust and scientific approach to understanding the causes and consequences of failures in the weaving section of the textile mill.

Failure mode and effect analysis
The method is commonly used in identifications of possible failures based on a design looking into a manufacturing process, product, or service.The method entails the system analysis that is assessment based on planning and preparation part, structure analysis, and function analysis.In addition, the failure data and its analysis are assumed before the optimization is done to find out the criticality. 7The value of severity is multiplied together in order to get the risk priority number.The item with the highest risk priority number (RPN) is given much attention. 7

Failure mode and effects analysis
In a scenario where improvements of goals, development of new controls, and analysis of failures on an existing process, services, and product needs to be done, Failure Mode and Effects Analysis (FMEA) stands out. 8Indeed, FMEA emerges as the solution when done periodically throughout the life of the processes.FMEA was utilized in this study as a systematic approach to address the gaps and inconsistencies in maintenance practices within the weaving section of the textile mill.
FMEA is a well-established method for analyzing potential failure modes, their causes, and their effects on the system.The research employed FMEA to evaluate the maintenance strategies, procedures, and schedules in place, aiming to identify weaknesses and areas for improvement.The FMEA process involved a thorough analysis of the existing maintenance practices to identify potential failure modes that could occur in critical equipment.Factors such as machine design, operating conditions, and maintenance history were taken into account during this assessment.Each failure mode was then evaluated based on its severity, probability of occurrence, and detectability.By calculating a RPN for each failure mode, which was obtained by multiplying the severity, probability, and detectability scores, the research prioritized the most critical failure modes for further investigation and improvement.This allowed them to focus their efforts on addressing the failure modes that had the highest potential impact on machine availability and performance.Through the FMEA process, the study identified the critical failure modes that significantly affected machine performance and availability in the weaving section.By understanding these failure modes, their causes, and their effects, the research was able to propose targeted improvements and develop more effective preventive maintenance strategies.The aim was to minimize downtime, enhance machine availability, and optimize maintenance efficiency.

Fishbone diagram
The fishbone diagram is a cause-and-effect diagram that is useful in tracking down the reasons for certain events, imperfections, defects, variations, and failures.To utilize the Fishbone Diagram, the research first identified the problem at hand, which in this case was the gaps and inconsistencies in maintenance practices.They then determined the main categories of factors that could potentially contribute to these issues.In the context of maintenance, these categories are often referred to as the six Ms: machines, man, methods, materials, Mother Nature, and measurements. 9The research conducted a thorough analysis of each category and identified potential factors within each one that could be influencing the maintenance practices.For example, under the "machines" category, factors such as machine design, age, and condition were considered.Under the "man" category, factors such as operator training and skill levels were examined.Once all the potential factors were identified and categorized, the research proceeded to analyze the relationships between these factors and the gaps and inconsistencies in maintenance practices.The Fishbone Diagram helped visually represent these relationships, with the problem (gaps and inconsistencies) at the head of the fishbone and the potential factors as the bones branching off from it. 9Through this analysis, the research gained a comprehensive understanding of the underlying causes contributing to the maintenance issues.The Fishbone Diagram allowed them to identify the key factors that needed to be addressed in order to improve maintenance practices and reduce gaps and inconsistencies.Based on the insights gained from the Fishbone Diagram analysis, the research proposed specific actions and recommendations to address the identified root causes.These recommendations aimed to enhance the maintenance procedures, optimize resource allocation, improve training programs, and implement better monitoring and measurement systems. 10

Data collection
During situation analysis, data collection was done using the observation using check sheets, interviews, and questionnaires with both open and closed-ended questions.Each personnel or participant was presented with a questionnaire.
The check sheet was be filled while at the same time monitoring the failures in the entire weaving section.A repeat of the same was done for a month in the critical machines.

Interviews with maintenance team
The maintenance teams were interviewed to provide important information as far as the maintenance strategies are concerned.In essence, interviews formed the basis of mapping the maintenance strategy of the critical equipment.Interviews provided information on the maintenance strategies and the critical equipment at a textile factory. 11The interviews provided both qualitative and quantitative information that can be interpreted.Given that interviews were structured, unstructured, and semi-structured, the most important aspect of the method was the data collected. 9Finally, all the interview questions are provided in Appendix.

Questionnaires to maintenance staff
A list of questions was prepared in order to help in data collection concerning a textile factory's maintenance strategies.
A series of open-ended questions provide the respondents with an opportunity to express their critical thinking concerning the state of machine maintenance at a textile factory. 12Also, closed-ended questions are quite important in restricting the depth of response.Most of the machine operators and the maintenance provided information on failure rates, downtime, and the duration needed to scan for failures.The questions used in this process are provided in Appendix.

Real-time data collection on maintenance activities
Recording the maintenance sessions and activities at the textile factory was captured through an observation process.
In this method, data pertaining identification of critical failure type, number of failures, availability, downtime, and the productivity of the machines were collected. 11The work performance during most of the maintenance and installation process is captured and used for the analysis and development of the optimized maintenance strategies.

Determining the critical equipment
In this case, FMEA presents a basis of risk assessment of the machines as far as failures and downtime is concerned.FMEA procedure is as follows; i. Identifying all of the probable failure modes.ii.Assign a value on a 1-10 scale to severity, probability of occurrence, and the probability of detection for each potential failure mode.iii.Get the RPN by multiplying the three numbers for each failure mode.iv.Using RPN as the priority value, rank the failure modes.The highest score demands the most urgent improvements activity.

Fishbone diagram process
The producer is as follows; i. Define the problem to be solved in relation to maintenance strategy.ii.Establish the main causes of the problem.iii.Establish the reasons leading to the key causes.iv.Establish the most likely causes.

Assessment of the criticality
Table 1 presents the assessments of the critical equipment.In this case, the score was determined using the mechanical and electrical failures of the machines in the weaving department.
According to the results of critical assessment of equipment in the weaving section shown in Table 1, the Yarn Winding Machine had a value of 24; the Yarn Warping Machine had a value of 20, the Sizing machine had a value of 82, and the Looms had a value of 124.It was established from the results that the weaving department is the critical department and the critical equipment is the looms.From the criticality assessment of the textile mill production departments, the weaving section post the highest score indicating that the section has some notable number failures as far as maintenance TA B L E 1 Failure mode and effects analysis for critical equipment.activities are concerned. 13In this regard, the modeling of the maintenance in the weaving department was performed to assess the maintenance strategy used and to analyze the failure data to come up with an optimized maintenance strategy.

Modeling of the maintenance strategy in the critical department
Figure 2 shows a fishbone diagram that presents the maintenance strategy results based on the fishbone diagram assessment.The diagram is based on the data that was collected using questionnaires and observation of the maintenance activities at the facility.During the data collection, the factors that contributed to the majority of failures were recorded and assessed in relation to the nature of maintenance that followed once a failure occurred in the critical department.

Discussions
Based on the new findings, to improve the goal of achieving 90% efficiency and a production output of 194.76 m in the textile mill, several actions can be taken.To improve maintenance strategies and scheduling for critical equipment in the weaving department of the textile mill, several steps can be taken.First, the department should transition from a reactive to a proactive maintenance approach by implementing regular inspections, cleaning, lubrication, and calibration to address potential issues before they lead to breakdowns.Clear maintenance schedules should be established, outlining specific tasks, intervals, and responsibilities for each critical equipment to ensure consistency and minimize unexpected failures. 14Additionally, condition monitoring techniques such as vibration analysis, thermography, and oil analysis can be utilized to monitor equipment health in real-time, enabling early detection of potential failures and timely maintenance interventions (see Figure 2).The department should also enhance its preventive maintenance strategies by providing proper training to maintenance personnel and ensuring they have the necessary skills and knowledge to carry out tasks accurately.Collaboration with equipment manufacturers can provide valuable insights into recommended maintenance practices and access to technical support. 15Implementing predictive maintenance systems based on data analytics and machine learning algorithms can enable proactive maintenance planning, reducing unplanned downtime.Finally, continuous monitoring and improvement should be emphasized by regularly reviewing failure data, downtime records, and maintenance activities, allowing for adjustments to maintenance schedules and procedures to optimize reliability, minimize downtime, and enhance overall efficiency and production in the weaving department (see Figure 2).

Conclusion
During the shift, 21% of the machines operated continuously, while downtime ranged from 10 min to 24 h.Some machines experienced extended downtime of up to 60 days.It took between 10 and 30 min to perform a failure scan.The target efficiency and production were set at 90% and 194.76 m, respectively.However, the preliminary results indicate a gap between the desired and actual performance.To address these inconsistencies and incoherence in maintenance practices, it is crucial to implement regular and appropriate maintenance schedules.The FMEA results highlight the criticality of weaving machines in the weaving department based on the analysis of failure data.The risk priority numbers for mechanical and electrical failures of looms are 84 and 40, respectively.By adopting data-driven preventive maintenance strategies based on failure frequency and time intervals, the issues of inconsistency and incoherence in maintenance practices can be effectively resolved.These maintenance strategies helped optimize the performance of machines, minimize downtime, and improve overall efficiency and productivity in the weaving section of the textile manufacturing processes.

Recommendations for maintenance practices
Based on the conversations, the following specific measures can be implemented to optimize maintenance time scheduling and improve reliability in the weaving section of the textile mill: 1. Implement a Preventive Maintenance Program: Develop a comprehensive preventive maintenance program that includes regular inspections, cleaning, lubrication, and calibration of critical equipment in the weaving section.This proactive approach can help identify and address potential issues before they lead to breakdowns. 2. Establish Clear Maintenance Schedules: Create well-defined maintenance schedules that outline specific tasks, intervals, and responsibilities for each machine in the weaving section.This ensures that maintenance activities are performed regularly and consistently, reducing the likelihood of unexpected failures and downtime.3. Utilize Condition Monitoring Techniques: Implement condition monitoring techniques such as vibration analysis, thermography, and oil analysis to assess the health and performance of critical equipment in real-time.This enables early detection of potential failures and allows for timely maintenance interventions.4. Train Maintenance Personnel: Provide proper training and education to maintenance personnel on the importance of preventive maintenance and condition monitoring techniques.Equip them with the necessary skills and knowledge to carry out effective maintenance activities and identify signs of impending failures.5. Collaborate with Equipment Manufacturers: Establish partnerships with equipment manufacturers (OEMs) to gain insights into recommended maintenance practices, optimal maintenance intervals, and access to technical support.
Leverage their expertise to enhance maintenance strategies and improve machine reliability.6. Implement Predictive Maintenance Systems: Implement predictive maintenance systems that leverage data analytics and machine learning algorithms to predict equipment failures based on historical data patterns.This enables proactive maintenance planning, reducing unplanned downtime and optimizing maintenance schedules.7. Regularly Review and Update Maintenance Strategies: Continuously monitor and evaluate the effectiveness of maintenance strategies in the weaving section.Analyze failure data, identify recurring issues, and adjust maintenance schedules and procedures accordingly to optimize reliability and minimize downtime.
By implementing these measures, the textile mill can transition from a reactive, breakdown-based maintenance approach to a proactive and preventive strategy.This shift in maintenance practices will help optimize maintenance time scheduling, improve reliability, reduce unplanned downtime, and enhance overall productivity in the weaving section.

5.1.2
Recommendation for future research A recommendation for future research is to explore the application of big data analytics in maintenance management.By leveraging machine-generated data and utilizing advanced analytics techniques, such as data mining and predictive modeling, researchers can optimize maintenance practices, predict equipment failures, and improve overall equipment availability.Integrating big data analytics with Internet of Things technologies can enable real-time monitoring and remote diagnostics, leading to proactive maintenance interventions.This research can contribute to enhancing maintenance strategies through data-driven decision-making.

Failure 2
Fishbone diagram indicating the nature of dominant maintenance strategy.Fishbone diagram illustrating the dominant maintenance strategy and identifying the causes of run-to-failures.The diagram categorizes all the factors under the 6Ms framework, namely Manpower, Method, Machine, Material, Measurement, and Mother Nature.The cause-and-effect diagrams present various aspects, including man, machine, method, material, measurement, and Mother Nature, which contribute to the underlying problem of inconsistency and incoherent maintenance practices.This depiction in the form of a Fishbone diagram helps identify the nature of the dominant maintenance strategy.By analyzing these interconnected factors, we gain a comprehensive understanding of the root causes that lead to maintenance inefficiencies and inconsistencies.This diagram serves as a valuable tool for diagnosing and addressing the underlying issues, enabling the development of more effective and targeted maintenance strategies.