A Reliability-Centered Maintenance Study for an Individual Section-Forming Machine

PurposeThis study focuses on the application of reliability-centered maintenance (RCM) to a textile industry steam boiler. The study aims to demonstrate the development and application of RCM to a steam boiler used in the textile industry.Design/methodology/approachRCM is a structured process that develops maintenance activities needed on physical resources in their operational environment to realize their inherent reliability by logically incorporating an appropriate mixture of reactive, preventive, condition-based and proactive maintenance methods. A detailed analysis of the RCM approach is presented to develop preventive maintenance (PM) program and improve the reliability and availability of the steam boiler system.FindingsThe research reveals that the identification of PM tasks is a good indicator of the PM program's efficiency and can serve as an important maintenance-related downtime source. It is also discovered that the majority of maintenance programs that claim to be proactive are, in fact, reactive. This article also shows how RCM may be successfully implemented to any system, resulting in increased system reliability.Research limitations/implicationsThe paper focuses on a pilot study of the development and implementation of the RCM technique to a textile industry steam boiler. It is suggested that the developed RCM model can be applied to the entire plant.Originality/valueThe paper presents a comprehensive RCM model framework as well as an RCM decision framework, providing maintenance managers and engineers with a step-by-step approach to RCM implementation. The proposed framework is significant in that it may be utilized for both qualitative and quantitative analysis at the same time.

8 - Implementation—Carrying RCM to the Floor

Q: Due to constraints in our biomed budget, I was wondering: Is there anyway to reduce the rotations for sterilizers in our preventive maintenance (PM) program?A: Sure. In fact, several years ago, we reduced the rotation and the items we change on sterilizers. Our facility has 11 operating rooms (OR) and two labor-and-delivery rooms. Our sterilizers get fairly heavy use between 6 a.m. and 11 p.m. But we're definitely not as busy as facilities that run 24 hours a day, 365 days a year. So we reduced the PM schedule for some of them to a semiannual basis. This process has worked very well. We have not had any more failures than we had when we did them quarterly. We saved ourselves time and the materials we would have had in replacing those quarterly parts. Certain sterilizers, like the washer-sterilizers, still undergo quarterly PM due to their fairly heavy use and the fact that we only have two in the OR. If one goes down, the impact to our customer is significant.I am making my suggestions based on the assumption that your facility has a good preventive maintenance program and some sort of a program that enables you to track equipment. By tracking equipment repairs, you can monitor the results if you reduce your facility's PM schedule. Our facility continuously monitors work requests through a program in our database. This program allows use to “write” almost any report one can imagine. For example, one of our performance indicators is repeat work requests. We have a report that compiles all such requests during a given period—three months, in our case. Each technician is given the piece of the report related to the equipment that he or she is responsible for. The technician then checks the work requests for that equipment, looking for anything that can be incorporated into a regular PM schedule and prevent a failure. This allows your facility to “hold all the cards,” so to speak. If someone at your facility periodically monitors your success, you will be able to adjust your PM program accordingly.As for what parts can be reduced, that depends on several factors such as use, steam quality, water quality, and maintenance program effectiveness. Our water here tends to be higher in calcium than most parts can tolerate. So we keep water traps and water solenoids on a semiannual schedule. That said, our steam quality is fairly good, and we push our steam parts as long as possible. When we looked at ways to reduce cost, we looked along the line of steam—not water.Another key factor is staff care with equipment. For example, we once had someone in the instrument room who would slam the doors of the sterilizers so hard that the machines shook. We talked to him on several occasions, but he slammed the door right up until his last day at our facility. If that was not bad enough, after slamming it one time, this now-former employee cranked hard on the handle and then brought it to me to report, “It's broke.” Ha, ha! While he worked at our facility, we stepped up the PM frequency on the doors. We would check the handles for tightness, the arms that wind out to make sure they were not missing their holders, the door seal which would snap if brittle and the door was slammed, and the door cover to make sure it had not been bent.But let me end by returning to the big picture. You can and should alter your PM schedule to maximize your resources. Working with a good database, a good PM program can save you time and money. Remember: Garbage in, garbage out.

Reliability‐Centred Maintenance (RCM) is an economical maintenance approach that places more emphasis on sustaining system functionality than just fixing individual hardware parts. Failure Mode and Effects Analysis (FMEA), one of the main techniques in RCM, is used to determine possible failure modes, their underlying causes, and how they affect system performance. This study identifies the greatest risk that affects the system by using a higher risk priority number (RPN) and clearly illustrates the maintenance plan for each failure scenario. By putting in place a systematic RCM policy, the study seeks to reduce these risks. The results show that, in terms of the impact of failure, the machines in the spinning portion are the most crucial. By lowering risk through a focused FMEA approach, the RCM methodology's implementation shows notable economic benefits. The suggested approach improves maintenance efficiency by concentrating on the most important 20% of failure causes. According to the findings, implementing RCM resulted in a 31.5% decrease in labour expenses. Additionally, compared to the case company Bahir Dar Textile Share Company's (BDTSC) present run‐to‐failure maintenance policy, the proposed maintenance plan reduced the yearly spare parts costs by 28.12%. The results highlight RCM's potential for enhancing maintenance plans, cutting expenses, and raising overall operational effectiveness in the textile industry.

2 - Preventive Maintenance—Definition and Structure

The Kentucky Transportation Cabinet’s (KYTC) bridge inventory is rapidly aging. As such, the Cabinet needs to identify and implement relevant preventative maintenance (PM) actions to extend the useful service lives of those structures. Maintenance actions, costs, and application interval/repair durability information have been obtained for both cyclical and condition-based bridge PM. Additionally, information rehabilitation actions were acquired from a regional Department of Transportation (DOT) survey and from other literature. As part of this study, the Kentucky Transportation Center (KTC) researchers monitored a KYTC bridge washing project to assess its level of effectiveness and to identify potential improvements. Based upon these tasks, recommended guidance was provided to KYTC for use when implementing bridge PM programs. The guidance includes a discussion of critical PM activities, strategies to adopt training and work documents (i.e., special notes) for contract work, and the implementation of work standards/special operating procedures for work performed by KYTC personnel. This report also includes details on the tools and training KYTC district-level personnel will need and outlines a roadmap for conducting pilot programmatic PM work in several districts.

PT The lathe is one of the essential machines in production activities in this company. The amount of downtime caused by machine failure will reduce product quality and quantity. Therefore, this research aims to identify the factors that most influence the risk of lathe failure so that repair priorities and maintenance strategies can be determined. This can be achieved by using the Failure Mode and Effect Analysis (FMEA) method. FMEA is a method that can be used to identify the causes and impacts of each possible failure mode on machine components by systematically explaining the levels of failure so that appropriate prevention or repair can be carried out. The FMEA method shows the Risk Priority Number (RPN) value as a reference in determining the choice of maintenance strategy, namely, predictive maintenance, preventive maintenance or corrective maintenance. The research results show that the highest RPN value is found in the motor shaft pulley component at 320 and Nut Screw at 210. Maintenance priorities are determined based on the Pareto diagram principle. The appropriate maintenance strategy carried out by PT. Keywords: FMEA, Lathe, Maintenance, RPN

Purpose:Though FMEA (Failure Mode and Effects Analysis) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge it has never been validated against actual incident learning data. The objective of this study was to perform an FMEA analysis of an SBRT (Stereotactic Body Radiation Therapy) treatment planning process and validate this against data recorded within an incident learning system.Methods:FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, and dosimetrists. Potential failure modes were identified through a systematic review of the workflow process. Failure modes were rated for severity, occurrence, and detectability on a scale of 1 to 10 and RPN (Risk Priority Number) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that had been active for two years. Differences were identified.Results:FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near‐miss events related to SBRT planning. FMEA failed to anticipate 13 of these events, among which 3 were registered with severity ratings of severe or critical in the incident learning system. Combining both methods yielded a total of 76 failure modes, and when scored for RPN the 13 events missed by FMEA ranked within the middle half of all failure modes.Conclusion:FMEA, though valuable, is subject to certain limitations, among them the limited ability to anticipate all potential errors for a given process. This FMEA exercise failed to identify a significant number of possible errors (17%). Integration of FMEA with retrospective incident data may be able to render an improved overview of risks within a process.

Electric Submersible Pump (ESP) is one of the most critical oil & gas equipment in Tuban East Java (TEJ) field. This research applies Reliability Centered Maintenance (RCM) to oil & gas to optimize maintenance methods for ESP at the TEJ field. The RCM was applied based on the historical failure events at equipment which caused Loss Production Opportunity (LPO). Start with Pareto chart, the RCM followed by Failure Modes and Effect Analysis (FMEA) that produced the Risk Priority Number (RPN) for the 80% problems. The last step provided the optimum maintenance periods using Weibull’s statistic. The result shows that flat cable in ESP has the highest RPN. Flat cable requires an optimum maintenance period of 509 days and a total cost of US$59,342. The total cost consists of Periodic Maintenance (PM) and Risk costs.

Aircraft are intricate flying machines equipped with advanced technologies and state-of-the-art information and communication systems. Maintaining these aircraft to ensure maximum availability and minimal downtime is a critical and challenging task. Therefore, aircraft maintenance, repair, and servicing are of utmost importance. This research work aims to develop a methodology for reliability centred maintenance (RCM) to enhance aircraft availability. An RCM approach with the Multi attribute decision making (MADM) technique for assigning criticality of systems was implemented to improve aircraft availability. Failure mode and effects analysis (FMECA) was utilized for the failure investigation in the aircraft systems. However, instead of the Risk priority number (RPN) due to its inherent flaws, the Analytical hierarchy process (AHP) was utilized to evaluate the criticality of systems. The remedial measures to minimize the failure frequency and downtime were proposed. The improvement in availability, post implementation of proposed preventive maintenance was validated with positive results. An improvement of approximately 6.62% in availability and a reduction of about 15.71% in the total downtime was observed on the implementation of proposed preventive measures. The availability analysis was carried out for aircraft systems; however, the model could also be used for other repairable systems following a similar maintenance philosophy.

As industries face increasing pressure to maintain complex systems with minimal downtime and optimized cost structures, traditional static maintenance strategies fall short of addressing real-time uncertainty and evolving operational conditions. Reliability-Centered Maintenance (RCM), while foundational, must adapt to incorporate probabilistic reasoning and continuous decision-making to remain effective. This paper presents a dynamic RCM modeling framework that integrates Failure Mode and Effects Analysis (FMEA) with Bayesian decision-theoretic approaches to enable real-time, risk-informed maintenance interventions. The model begins with a comprehensive failure mode mapping using FMEA to identify critical assets, failure causes, and effects. Each failure mode is assigned dynamic risk priority numbers (RPNs) that evolve based on operational data, sensor inputs, and environmental variability. A Bayesian belief network is layered onto this framework to update prior failure probabilities as new data becomes available, capturing the stochastic nature of degradation. Decision nodes within the Bayesian structure enable cost-risk trade-offs to be evaluated in real time, ensuring that optimal maintenance actions are selected under uncertainty. Furthermore, the model accommodates adaptive learning through posterior updates, refining both failure predictions and policy recommendations over time. A case study involving an energy generation plant demonstrates a 31% improvement in mean time between failures (MTBF) and a 24% reduction in maintenance costs. The dynamic fusion of qualitative failure analysis with quantitative Bayesian inference allows for smarter, context-aware decision-making and predictive readiness. This study provides a robust framework for implementing intelligent RCM strategies in Industry 4.0 environments where responsiveness, resilience, and safety are paramount.

Purpose: Omitting potential failure modes or inferencing inappropriate failure modes during failure mode and effects analysis (FMEA) implementation can lead to a lack of understanding of the causes of failures incurred or might lead to introducing inappropriate improvements. Therefore, it is critical to accurately infer the failure modes, which, in turn, requires selecting a suitable failure mode inference method because the results of such methods generally vary depending on the purpose and context of FMEA implementation.<BR>Methods: In this study, the advantages and disadvantages of functional and hardware approaches were compared and analyzed by applying them in mechanical and circuit components with respect to the implementation of component FMEA.<BR>Results: It was observed that the hardware approach can be more desirable for identifying the causes and mechanism of failures to prevent omissions and address the failure modes. However, when it becomes difficult to represent failure modes based solely on physical aspects, the functional approach can be used simultaneously with the hardware approach.<BR>Conclusion: Based on the aforementioned comparison and analysis, guidelines for selecting an effective FMEA method for implementing FMEA in organizations have been presented. However, this study focused only on component FMEA and did not consider system FMEA. Thus, the procedure for system FMEA, in terms of FMEA objectives and implementation, should also be examined.

PT X is one of the manufacturing companies in Indonesia, which specializes in manufacturing molds and dies which offers products to the internal group and the domestic market. This study discusses the policy analysis of machine repair maintenance management using the RCM (Reliability Centered Maintenance) method approach at the PT X manufacturing company. Some of the problems that occur are sudden engine failure, which will cause over production. One machine that is considered critical is the CNC Wirecut K90 machine. With the application of using RCM it is expected to improve machine reliability through several systematic RCM implementations: system selection and information collection, defining system boundaries, system descriptions and function block diagrams, describing system functions and functional failures, compiling Failure Mode and Effect Analysis (FMEA), composing Logic Tree Analysis (LTA), selection of actions. The new policy set by the Realibility Centered Maintenance (RCM) method consists of 14 failure modes that are resolved by time-directed (CD) and 6 failure modes that are overcome by run to failure (RTF). In the FMEA analysis, there are 4 components that most often fail to function, namely the electrode pin, wire guide, filter and contact fit. The determination of this component is based on the RPN value and data in the field. In the RCM analysis, the electrode pin, wie guide and contact fit components are included in the run to failure (RTF) treatment policy category. Meanwhile, the filter component is in the category of condition direction (CD) maintenance policy with an average failure rate of 29,951 days.

Purpose: Failure Mode and Effects Analysis (FMEA) provides a proactive method of improving the quality and safety of treatments by identifying and correcting hazards points in the process of clinical care. FMEA, however, is often thought to be prohibitively labor intensive. This study outlines a method of streamlined FMEA, conducted with limited resources, and assesses its feasibility and effectiveness. Methods: FMEA was performed on the external beam service of a clinic treating approximately 650 patients per year on three linear accelerators. A facilitator and local team leader were identified, and a plan was developed to complete the exercise in four one‐ hour meetings as follows: 1 (core group), introduction and process mapping, 2 (all staff) identification of failure modes from expert user input, 3 (core group) scoring of failure modes according to an FMEA risk priority number, RPN, i.e. the product of severity, occurrence and detectability scores, and 4 (all staff) error proofing of the top‐five ranked failure modes. Results: Fifty‐ two failure modes were identified, 43 of which were scored and ranked. Specific interventions were developed for the five highest ranked failure modes. FMEA scoring after intervention indicated that the average RPN score for the top five modes decreased from 273 to 161 (p=0.03) while FMEA scoring of a control group of failure modes with no intervention did not show a significant change in RPN (p=0.07). The exercise was accomplished within the expected timeline and required 55 total hours of staff time and 20 hours of facilitator effort. Conclusion: Streamlined FMEA analysis is feasible with a relatively modest effort, and can reduce the risk profile of a facility. This may open the possibility of performing repeat FMEA on a regular basis.

One of the goals of good management is to operate with a predictive and preventive maintenance program that prevents unscheduled utility service interruptions and machine breakdowns. A reasonable cost of such a program contributes to the profitability of manufacturing plants and results in reduction of operating costs and increased productivity. This all can be accomplished by organizing, managing, and controlling maintenance. A good maintenance program should include designing, laying out, and constructing facilities with maintenance in mind; providing for reliable utilities and their uses; developing a maintenance organization and program while the facility is under construction; developing a simple, practical, predictive, and preventative maintenance program; supplementing an in‐house maintenance organization with contract maintenance; motivating the maintenance workers; organizing safety as an important function of any maintenance program; including environmental care and appreciation in any maintenance program; developing an energy conservation program as part of the maintenance program; creating a partnership with suppliers and equipment manufacturers; cooperating with other service groups of the facility; and controlling costs and assuring budget responsibility.