Reliability Centered Maintenance Research Articles

Proposed Maintenance Planning Using Reliability Centered Maintenance (RCM) at PT. Djabesmen

Problems that occur in the production machine at PT. Djabesmen is the frequent occurrence of engine failure that causes low engine reliability. The purpose of this study is to find out causes high breakdown in Royal Board division of PT. Djabesmen, minimize the occurrence of a breakdown on the Main machine unit, as well as recommendations on the types of appropriate maintenance actions for the machine components being examined.
 In this study the analytical method used is Reliability Centered Maintenance (RCM), which is a maintenance analysis method used to determine critical components and improve maintenance systems that focus on improving engine reliability. 
 The results of this study indicate that in the main machine unit there are 15 failure modes classified into category B, 5 failure modes classified as category C, and 24 modes belonging to the D / B category. In selecting treatment actions, 23 failure modes used directed task conditions, 15 failure modes used finding filure and 6 other failure modes using the run to failure task.
 Keywords : Maintenance, Reliability, RCM, Main Machine, Failure

An FBWM-TOPSIS Approach to Identify Critical Feeders for Reliability Centered Maintenance in Power Distribution Systems

With the limited availability of resources and time in the power industry, condition-based maintenance schemes are deemed effective to maintain the system desirable performance at all times. In this context, reliability centered maintenance is the key to strategically manage the assets in power grids enforcing the performance metrics to be the system reliability indicators. To this end and to most effectively allocate the maintenance time and resources, the most critical components in the grid, i.e., those needing maintenance the most with massive failure consequences, should be first identified. This article proposes a new multicriteria decision-making scheme to identify critical feeders in power distribution systems. Unlike the previous efforts which have focused on techniques based on the analytical hierarchical process, a best-worst method is employed in this article to prioritize the system reliability criteria based on the experts' knowledge and judgments. The proposed approach can achieve a faster and more accurate outcome in prioritizing the system criteria for maintenance. In addition, fuzzy theory is utilized to address the prevailing uncertainties in the experts' judgments and decisions. Finally, TOPSIS technique is employed to prioritize distribution feeders for future inspection and maintenance allocation.

Joint Optimization in Condition-Based Maintenance and Inventory Policy for the Repairable System

In the modern industry, to reduce support pressure, a variety of suppliers have begun to use the same kind of units in many types of equipment. However, highly integrated equipment is more difficult to use in traditional reliability-centered maintenance (RCM) models, and researchers start steering based on the state monitoring method. This paper mainly discusses the remaining useful life (RUL) prediction method based on the Wiener process and introduces the improved condition-based maintenance policy. Combined with the (S-1, S) policy, the joint policy and the model considering the repairable policy are built for the multiunit system. Finally, the optional decision combination is obtained. Except that, the advantage of the joint optimization in a multiunit system and the characteristics of repairable policy in the joint model are analyzed.

Decision-making regarding the best maintenance strategy for electrical equipment of buildings based on fuzzy analytic hierarchy process; case study: elevator

PurposeThe purpose of this paper is to propose the best method of maintenance for electrical equipment of a building and especially elevators in order to achieve various benefits.Design/methodology/approachLiterature review shows that there are various methods such as corrective maintenance, condition-based maintenance, time-based maintenance and reliability-centered maintenance (RCM) for the maintenance of electrical equipment of a building. Selecting the most appropriate maintenance method, especially in large buildings such as commercial ones, is a multiple-criteria decision-making problem. To solve this problem, the fuzzy analytic hierarchy process is used in the present study. It considers the uncertainties of the experts' judgments to provide an optimized maintenance method.FindingsTheoretically, the RCM method will take a lot of time and cost. But in prioritizing the methods, RCM gained more points with regard to safety criteria and value added. The main finding of this study suggests that RCM can be used in the case of elevators and would improve the safety and reliability of buildings.Originality/valueThe authors believe that this study will be valuable because one of the problems in building maintenance complexes is the lack of knowledge about the best method to prevent the risks of elevators. The proposed method has compared with other methods in various sub-criteria, and the results have been fully analyzed.

Reliability modeling based on Maximum Entropy and non-central moments as an alternative for RCM schemes or replaceable systems

This paper offers an alternative to determine reliability-centered maintenance (RCM) schemes for replaceable systems, when replacement times are censored and only the information that maintenance technicians, from the subjectivity of their experience, is available. Using differential entropy in information theory, and exploiting Lagrangian optimization algorithms, a Generalized Probability Density of Maximum Entropy (GPDME) is extracted. Lagrangian techniques provide a set of parameters that characterize the GPDME, the estimation of the parameters is done by first order perturbation of the integral of non-central moments, with which, the GPDME is typically built. In the emerging industry, RCM maintenance plans are not a common standard, in an attempt to put into practice, the benefits of RCM to this industrial segment, a case study, where the presented methodology was applied is provided. In the discussion and conclusions section, the areas of opportunity that are observed in the methodology presented in this work are adressed.

Assessing the Effect of Reliability-Based Maintenance Approach in Job Shop Scheduling with Setup Time and Energy Consideration Using Simulation; A Simulation Study

ABSTRACT Maintenance in a stochastic job shop scheduling environment significantly impacts real-time scheduling problems with the environmental aspect. This simulation research assesses the effect of a reliability-based preventive maintenance approach to system performance for considering job shop scheduling problems with sequence-dependent setup time (SDST). Two types of reliability-based maintenance approaches are considered, i.e., reliability-centered preventive maintenance (RCPM) and a reliability-centered periodic preventive maintenance approach (RCPPM). The shop comprises 10 different machines and six job types. Six scheduling and two energy-oriented performance measures (Pms) are considered for evaluating the system’s performance. Results reveal that lower levels of reliability, namely, 0.74, 0.78, and 0.82 recommended for mean flow time, makespan, average operation energy consumption, average idle energy consumption, and total setups Pms. A 0.74 level of reliability is recommended for mean tardiness and the number of tardy jobs Pms for the RCPM approach. RCPPM approach provides the best system Pms by mean flow time, makespan, mean tardiness, average operation energy consumption, average idle energy consumption, and the number of tardy jobs when maintenance time is 5% of operation time. In contrast, mean setup, and total setups Pms are independent of maintenance time. Both approaches get compared, and its statistical analysis shows that if maintenance time is 15% or more than 15% of the operation time, the RCPM method is recommended. If maintenance time is 10% or less than 10% of the operation time, maintenance planning is recommended using the RCPPM approach. Considering real-time scheduling work with the environmental aspect represents the novelty of the present study.

Using the Multi-Criteria Model for Optimization of Operational Routes of Thermal Power Plants

The constant problems evidenced in the Brazilian hydrological scenario, where the source of hydraulic potential corresponds to about 63.9% of the energy matrix, coupled with the exponential growth in the supply of renewable energy, corroborates the importance of thermal power generation as the basis of Brazilian’s energy matrix. With the operation of thermal power plants, which characteristically involve a large number of systems, subsystems, and auxiliary equipment, there is a high demand for the use of methodologies for monitoring and controlling processes, analyzing failures, and implementing improvements and actions that increase the reliability and, consequently, reduce the failure rate. In this context, decision-making about prioritizing criticality for operational monitoring of an asset’s components, from the perspective of operation and maintenance planning and based on reliability-centered maintenance (RCM) concepts, can be considered a complex task. Given this, the research seeks to demonstrate how the “Measuring Attractiveness by a Category Based Evaluation Technique (MACBETH)” method can be applied to the development of a multiple criteria model to support decision making in ordering the criticality of systems in operational inspection routes of thermal power plants, to propose new routines execution methodologies, aiming at increasing the productivity of the operation team. According to the results of the judgment matrix, the ordering proposal allowed a strategy for the execution of the current operational routes, redefining criticality, periodicity, routing, and applied resources, maintaining the reliability of the plant. It limits itself to assessing the criticality of the subsystems and monitored points belonging to the boiler system. These are typical of thermoelectric plants classified within the Rankine thermodynamic cycle and represent a significant part of the evaluation proposal.

Appropriate strategy selection for reliability-centered maintenance of one-shot systems using fuzzy model

PurposeThis paper addresses special reliability-centered maintenance (RCM) strategies for one-shot devices by providing fuzzy inferences system with the assumption that, to data, there is no data available on their maintenance. As far as one-shot devices are concerned, the relevant data is inadequate.Design/methodology/approachIn this paper, a fuzzy expert system is proposed to effectively select RCM strategies for one-shot devices. In this research: (1) a human expert team is provided, (2) spatial RCM strategies for one-shot devices and parameters bearing upon those strategies are determined, (3) the verbal variables of the expert team are transformed into fuzzy sets, (4) the relationship between parameters and strategies are designed whereupon a model is developed by MATLAB software, (5) Finally, the model is applied to a real-life one-shot system.FindingsThe finding of this study indicates that the proposed fuzzy expert system can determine the parameters affecting the choice of the appropriate one-shot RCM strategies, and a fuzzy inference system can help for effective decision making.Originality/valueThe developed model can be used as a fast and reliable method for determining an appropriate one-shot RCM strategy, whose results can be relied upon with a suitable approximation in respect of the behavior test. To the best authors’ knowledge, this problem is not addressed yet.

Condition-Based Maintenance of HVAC on a High-Speed Train for Fault Detection

Reliability-centered maintenance (RCM) is a well-established method for preventive maintenance planning. This paper focuses on the optimization of a maintenance plan for an HVAC (heating, ventilation and air conditioning) system located on high-speed trains. The first steps of the RCM procedure help in identifying the most critical items of the system in terms of safety and availability by means of a failure modes and effects analysis. Then, RMC proposes the optimal maintenance tasks for each item making up the system. However, the decision-making diagram that leads to the maintenance choice is extremely generic, with a consequent high subjectivity in the task selection. This paper proposes a new fuzzy-based decision-making diagram to minimize the subjectivity of the task choice and preserve the cost-efficiency of the procedure. It uses a case from the railway industry to illustrate the suggested approach, but the procedure could be easily applied to different industrial and technological fields. The results of the proposed fuzzy approach highlight the importance of an accurate diagnostics (with an overall 86% of the task as diagnostic-based maintenance) and condition monitoring strategy (covering 54% of the tasks) to optimize the maintenance plan and to minimize the system availability. The findings show that the framework strongly mitigates the issues related to the classical RCM procedure, notably the high subjectivity of experts. It lays the groundwork for a general fuzzy-based reliability-centered maintenance method.

An optimization framework for opportunistic planning of preventive maintenance activities

Methodologies for designing maintenance policies, such as Reliability Centered Maintenance (RCM), provide a list of recommended preventive maintenance (PM) activities as well as the periodicity for their execution, to comply with safety and reliability standards. This information is then used to elaborate PM plans, defining the instants of execution of each PM activity in the policy throughout a planning horizon, while assuring adequate levels of equipment availability, labor and resource coordination, and spare parts management. Preferably, it is sought to group PM activities executions to form opportunistic work packages to reduce functionality interruptions on the equipment. To address the latter issue, this paper presents an optimization framework for the medium-term opportunistic planning of PM activities on a single machine. The proposed framework is formulated as a computationally tractable continuous time mixed-integer linear program (MILP) that determines the execution instants of PM activities, considering time window tolerances for advancing or delaying executions to maximize the number of grouped executions or, equivalently, minimizing the number of stoppages needed to comply with a PM policy. Diverse numerical experiments are conducted, considering variants of the model regarding the conservativeness in use of the time window tolerances and the inclusion of resource constraints for planning. The results show that the reductions on the number of stoppages of the PM plans, that can be achieved from different levels of time window tolerances, are more significant for PM policies composed by activities with more heterogeneous periodicities of execution.

Fuzzy reliability centered maintenance considering personnel experience and only censored data

The automotive industry uses Reliability Centered Maintenance (RCM) to establish efficient and cost-effective maintenance schedules of equipment (IATF 16949). The RCM methodology is based on equipment reliability which requires collection of failure times. However, in some cases it is not possible to record enough information. Sometimes in the field, only service times are available (these can be classified as “only censored data”). The most commonly found situation waiting until the component fails. In that sense, the RCM methodology mainly uses the classic Weibull model for estimating reliability indexes, which cannot be performed if there are only censored data. The maximum entropy can be used to propose an alternative reliability model that is able to work under the limited information conditions. Nevertheless, the uncertainty of only censored data and the limited information also affect the maximum entropy model. The information available, such as maintenance staff experience, must help provide accuracy to reliability estimation, which can be addressed through fuzzy numbers. Hence, a fuzzy maximum entropy approach is proposed to determine the maximum entropy reliability function considering the uncertainty and the maintenance staff knowledge; therefore, the replacement frequency. Finally, the proposed method was applied to an injection molding machine. Outcomes proved that the alternative model is a useful and reliable option if there are only censored data and personnel experience as available information. The RCM maintenance plan was scheduled using the proposed methodology and its results showed 40% savings in the annual maintenance costs, which represent an important benefit for company.

Determination of Maintenance Task on Tanker Vessel’s Marine Boiler Using Reliability Centered Maintenance (RCM) II Method

There are several types of tanker accidents: collision, fire, grounding etc. In fire accident, the major causes of ship accidents are explosions on marine boilers and damage to ship components. In addition to operational factors, maintenance factors are also the major cause of the accident. In this case, consideration is needed to improve the safety factor of the marine to the operational side. It is also needed to develop the maintenance with the Reliability Centered Maintenance (RCM) approach. This study explores the method of maintenance for marine boiler maintenance systems on tankers using the RCM II method. This study also determines the characteristics of asset failure to assist in assessing the level of risk to asset operations. Based on the results of the RCM analysis, it could be seen that there are 128 maintenance tasks consisting of 3 types of activities: Condition Monitoring (CM), planned maintenance (PM) and One-Time Change (OTC). For failure modes that have high risk, preventive maintenance is performed at intervals every 1 to 7 days. For the longest preventive maintenance, intervals carried out on the safety valve are every 2 years.

Analisa Perbaikan Manajemen Perawatan Dengan Metode Reliability Centered Maintenance Bagian Cnc Wirecut di PT X

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.

Maintenance Factors Affecting Increased Lutetium-177 Production

Abstract With lutetium (Lu-177) demand forecasted to increase following the conclusion of clinical trials and product registration in key jurisdictions, manufacturers are seeking to improve their production outputs. Naturally, the question falls to operation and maintenance of these production centers to ensure reliable supply to market. Maintenance in the radiopharmaceutical industry is a complex environment with many competing interactions between radiation safety and good manufacturing practice (GMP) requirements. With the view of doubling lutetium production by the end of 2020 and plans for continued increases over the coming years, a key question was whether the plant reliability could accommodate this increase sustainably. As plant use increases the potential frequency of failure as well as the impact of outages increases, it results in a general increase in risk. Since its development by the aviation industry in 1960s, the reliability centered maintenance (RCM) concept has seen adoption by many industries. The International Atomic Energy Agency (IAEA) developed a technical document on RCM applications in nuclear industry in May 2007 while the International Society of Pharmaceutical Engineers (ISPE) refers to the concept in their own baseline guides as well as other risk-based methods in line with international standards. Blame for reliability issues often falls at the feet of the equipment design with suggestions that wholesale redesign of equipment will solve reliability issues. Careful review of existing strategies against these baseline guidelines tells a different story with clear gaps between existing maintenance strategies and current best practice. This document details the results of that investigation, providing information on the gaps found and the systems implemented to bring maintenance strategies back in alignment with current best practice. The article also offers commentary on situations requiring compromises between those best practices defined by IAEA and ISPE.

A fuzzy DEMATEL-ANP-VIKOR analytical model for maintenance strategy selection of safety critical assets

In most industries, such as aerospace, manufacturing, transport and energy sectors, maintenance plays a vital role in improving the performance of safety critical equipment and facilities. It also helps industries achieve the largest possible efficiency, ensure workplace and environmental safety, and reduce unnecessary breakdowns and costs. Therefore, it is crucial for industries to adopt an optimal maintenance strategy for their critical systems and infrastructure. In this study, we aim to propose a novel analytical multi-criteria decision-making (MCDM) methodology for selecting the most suitable maintenance strategy in distillation units of oil refinery plants. The alternative maintenance strategies include run-to-failure (RTF), preventive maintenance (PM), condition-based maintenance (CBM), and reliability centered maintenance (RCM), which are evaluated with respect to 12 sub-criteria in three categories of economical, safety, and sustainability issues. The MCDM methodology consists of a DEMATEL-based analytic network process (ANP) method to determine the importance weights of decision criteria and a VIKOR method to rank the maintenance strategies. Also, interval type-2 fuzzy sets are used to capture uncertainty in experts’ individual judgments. Finally, a real case study is provided to show the applicability of the proposed methodology to an oil refinery plant. The results show that, thanks to advances in degradation modeling, sensor technology, and data analytics platforms, the RCM and CBM are the superior maintenance strategy for crude oil distillation systems.

Analysis of Subsea Energy Supply Systems for Improved Maintenance Using ANFIS and TOPSIS

The maintenance plan for the subsea energy supply system during the operation was optimized by firstly training the Adaptive Neuro-Fuzzy Inference System (ANFIS) model with historical data of process variables of the system such as voltage, current, power, and pressure in MATLAB software and then predicting the optimum output of the process using the trained model, which showed a good prediction of operational data after two cycles of computational analysis. The outputs from the trained model, coupled with expert opinions on historical data, were used to develop a Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria algorithm to select the best maintenance strategy. The reliability-centred maintenance, with a performance score of 0.811, ranked best amongst the maintenance strategies under the studied scenario. The result shows that the procedure could be applied in condition monitoring of operational subsea energy supply systems to predict impending faults through deviation error and prevent failure by the application of an appropriate maintenance strategy. Keywords: ANFIS, TOPSIS, Maintenance plan, Fault prediction and Subsea power supply systems DOI: 10.7176/ISDE/12-2-02 Publication date: April 30 th 2021

Elektrik İletim Hatlarının Güvenirliği için Uygun Olasılık Dağılım Seçimi ve Analizi

Reliability is the ability of a system or system components to function within a specified time under specified conditions. One of the most important criteria of electricity transmission systems is to be able to keep the energy on the system continuously within the limits and to be interrupted for the least possible time. The reliability study offers important advantages such as determining the appropriate operating range of the system and making the necessary intervention. In this study, it is aimed to show that when the maintenance and operation activities of electricity transmission lines are carried out with Reliability Centered Maintenance (RCM) in the foreground, the transmission lines can be interrupted for the least amount of time within the specified limits. In this direction, reliability analysis has been conducted using transmission line fault data. In the study, the Anderson-Darling goodness of fit test was performed to determine from which statistical distribution the transmission line fault data came from. The reliability of the transmission line was evaluated with the Log-normal distribution, which was determined by the goodness of fit. As a result of the reliability assessment, the reliability of the transmission line was found to be low. The advantages that transmission lines can provide when maintenance and operating activities are conducted based on reliability are presented.

Adopting machine learning and condition monitoring P-F curves in determining and prioritizing high-value assets for life extension

Many machine learning algorithms and models have been proposed in the literature for predicting the remaining useful life (RUL) of systems and components that are subject to condition monitoring (CM). However, in cases where data is ubiquitous, identifying the most suitable equipment for life-extension based on CM data and RUL predictions is a rather challenging task. This paper proposes a technique for determining and prioritizing high-value assets for life-extension treatments when they reach the end of their useful life. The technique exploits the use of key concepts in machine learning (such as data mining and k-means clustering) in combination with an important tool from reliability-centered maintenance (RCM) called the potential-failure (P-F) curve. The RCM process identifies essential equipment within a plant which are worth monitoring, and then derives the P-F curves for equipment using CM and operational data. Afterwards, a new index called the potential failure interval factor (PFIF) is calculated for each equipment or unit, serving as a health indicator. Subsequently, the units are grouped in two ways: (i) a regression model in combination with suitably defined PFIF window boundaries, (ii) a k-means clustering algorithm based on equipment with similar data features. The most suitable equipment for life-extension are identified in groups in order to aid in planning, decision-making and deployment of maintenance resources. Finally, the technique is empirically tested on NASA’s Commercial Modular Aero-Propulsion System Simulation datasets and the results are discussed in detail.

Determination of Production Instrumentation Equipment Maintenance Intervals In the Paper Industry

PT. XYZ is a manufacturing industry engaged in paper processing with afval raw materials. The problem faced is machine failure that occurs suddenly without predictability, this is because there is no scheduled maintenance (preventive main-tenance). The object of this research is focused on production instrumentation equipment. This study uses the Failure Mode and Effect Analyzer (FMEA) method to identify the causes of failure and the effects of these failures by determining the critical value of the component, namely the Risk Priority Number (RPN) which is the largest, then the Reliability Centered Maintenance (RCM) II Decision Worsheet method for determine maintenance intervals of production instrumentation equipment. Based on the results of RPN calculations in the FMEA method to determine the critical components of the Instrumentation equipment, namely the Control Valve, it can be seen that the highest total RPN value is found in three components, namely Restrictor with an RPN value of 390, Power Supply with RPN of 297, and also a Pilot Positioner. with an RPN value of 240. And with optimum maintenance intervals, among others, the Restrictor every 40 hours, the Power Supply every 41 hours, and the Pilot Positioner every 47 hours.

Production Machine Maintenance System Design Using Reliability Centered Maintenance

One of the indicators in increasing productivity is the level of reliability of production machines. The reliability level of a production machine is determined by the effective and efficient maintenance process of the company. A case study was conducted in a manufacturing company. In the period January until June 2019, the percentage of machine downtime is more than 3.0% (ideal benchmarking downtime for the manufacturing industry). Research carried out using Reliability Centered Maintenance (RCM). It starts with determining critical objects, then identifies each function of the assets, carries out FMEA analysis and calculates the highest risk of each failure mode, calculates proper maintenance intervals for the five highest failure modes, then ends with making a maintenance strategy by considering risk based inspection. This study aims to develop a maintenance strategy model that is better than what is currently used. By applying Reliability Centered Maintenance, machine reliability can be increased by an average increase of 39.34% and the right maintenance strategy can be found. Furthermore, it will get a new framework to become a new maintenance policy for the company.