Maintenance Strategy Selection Research Articles

Selection of an appropriate maintenance strategy using analytical hierarchy process of cement plant

Selection of an appropriate maintenance strategy using analytical hierarchy process of cement plant

Harvesting electricity from road traffic noise energy – a literature review

Recent developments in digital technologies, including big data and internet of things concepts, have shown promising results in achieving more appropriate and effective pavement engineering through a proactive asset management approach. This could be achieved through an early diagnosis of defects and selection of an appropriate maintenance strategy informed by more granular data by the utilisation of advanced sensing systems. Such a sensing system for pavement, which should include a combination of embedded sensors and surface data sensors (e.g. cameras) would require electricity, which could be problematic when considering the growing demand for electricity around the globe. Harvesting electricity from the pavement, for example from traffic noise, which is the focus of this article, could bring new hope for achieving self-efficient and sustainable sensing systems for roads. If effective, an electricity generation system from road traffic noise could be counted towards the net zero carbon dioxide target set by road authorities. A review of the literature revealed that the noise of air pumping between the tyre and the pavement surface is the critical noise source with the highest potential for electricity generation. Harvesting, storage and conversion of noise energy to electrical energy are still in the preliminary stages.

Reliability-based leading edge erosion maintenance strategy selection framework

Leading edge erosion has become one of the most prevailing failure modes of wind turbines. Its effects can evolve from an aerodynamic modification of the properties of the blade to a potential structural failure of the leading edge. The first produces a reduction of energy production and the second can produce a catastrophic failure of the blade. Considering the uncertainties and constraints involved in the design of optimal operation and maintenance (O&M) strategies for offshore assets and the influence of site-specific parameters on the dynamics of this particular failure mode, the task becomes complex. In this study, a framework to evaluate the influence of different maintenance strategies considering uncertainties in weather, material behaviour and repair success is presented. Monte Carlo Simulation (MCS) is used alongside a computational framework for Leading Edge Erosion (LEE) degradation to evaluate the lifetime cost distribution and probability of failure of the chosen maintenance strategies. The use of the framework is demonstrated in a case study considering a 5-MW offshore wind turbine located in the north of Germany. The influence of the modification of the maintenance interval or time between repairs and the comparison with maintenance activities executed only during months with milder weather is analysed in terms of cost and reliability. A Pareto front plot considering the probability of failure and the median of the cost is used to jointly compare strategies considering both aspects to provide a tool for risk-informed maintenance selection. Finally, the potential benefits of condition-based maintenance and autonomous decision-making systems are discussed. The case of study shows the benefits of repairs during summer months and the importance of the relation risk/O&M cost for different maintenance strategies.

신뢰성 민감도 분석을 이용한 MVDC 변환소 유지보수 전략선정 방안

신뢰성 민감도 분석을 이용한 MVDC 변환소 유지보수 전략선정 방안

A maintenance strategy selection method based on cloud DEMATEL-ANP

A maintenance strategy selection method based on cloud DEMATEL-ANP

New Hybrid MCDM Approach for an Optimal Selection of Maintenance Strategies: Results of a Case Study

Summary Industrial systems are becoming more sophisticated, and their failure can result in significant losses for the company in terms of production loss, maintenance costs, fines, image loss, etc. Conventional approaches to modeling and evaluating the failure mechanisms of these systems do not consider certain important aspects, such as the interdependencies between failure modes (FMs) with information and data containing uncertainties as they are generally collected from experts’ judgments. These restrictions may lead to improper decision-making. The use of more advanced techniques to model and assess the interdependencies among components’ failures under uncertainties seems to be more than necessary to overcome these deficiencies. It is in this context that the proposed approach fits. It consists of proposing a hybrid multicriteria decision-aking (MCDM) approach that combines several techniques for a better selection of maintenance strategies. Using the failure mode and effects analysis (FMEA) technique, the potential FMs of components, along with their causes and effects, are identified. The relative importance (or weight) of these FMs is determined using the fuzzy simple additive weighing (FSAW) method based on how they affect the system’s goals. The causal relationships between FMs and their final weights are determined by the fuzzy cognitive maps (FCM) method and the nonlinear Hebbian learning and differential evolution (NHL-DE) algorithm. Finally, based on the final FM weights provided by the FCM, the simple additive weighing (SAW) method is used to select the optimal maintenance strategies. The results of applying the proposed approach to an operating compressor lubrication and sealing oil system demonstrate its importance and usefulness in assisting system operators to efficiently allocate the optimal maintenance strategies, considering the strong correlation between FMs and their effects on system performance while accounting for the uncertainties associated with experts’ judgments. These correlation effects have led to changes in the assigned weights of the selected FMs. Specifically, the FM related to the low output of the lube/seal oil pump, which was initially assigned a lower priority, and with the correlation effects has become the first critical FM. This shift in prioritization emphasizes the need to address this particular FM promptly. By focusing on addressing these high-priority FMs, maintenance efforts can be optimized to prevent or mitigate more severe consequences. Among the various maintenance strategies evaluated, it was determined that the combination of condition-based maintenance (CBM) and precision maintenance (PrM) yields the most favorable outcome in terms of mitigating the impact of accidental failures and undesired events on the selected system.

Computationally-Affordable Unsupervised Machine Learning Algorithm to Identify the Level of Distress Severity in Pavement Functional Performance

Pavements continually experience various types of distresses that are manifested in the form of defects that will eventually lead to the pavement’s failure. Selection of appropriate preventive maintenance strategies in terms of timing and method retards the large backlog of poor conditions of pavements, therefore improves the system-wide performance in an efficient and cost-beneficial way. However, the pre-treatment functional performance condition of asphalt pavement has a significant impact on the efficacy of preventive maintenance, and to provide years of service, it is necessary to designate an appropriate preventive maintenance method according to the actual functional condition of pavement. This study presents a new unsupervised learning approach for 1) extracting high correlation factors to reduce the dimensionality of feature distress data, and 2) accurately separating attributes that define pavement functional performance into severity levels to effectively assess the practiced preventive maintenance. Three unsupervised machine learning algorithms including feature extract, relevant factor analysis, and cluster analysis were executed. Based on the severity level results of the pre-treated pavement, the effects of four treatment methods (thin overlay, slurry seal, chip seal, and crack seal) were evaluated by a statistical analysis. The preventive maintenance data from the Specific Pavement Studies-3 (SPS-3) experiment of the Long-Term Pavement Performance (LTPP) database are used for the analysis. The results demonstrated that selecting the appropriate maintenance method for the functional performance risk level of the pavement is beneficial in achieving successful treatment results.

Bakım Stratejisi Seçimi için Bulanık PIPRECIA ve Bulanık MOORA Yöntemlerinin Entegrasyonu

In today’s competitive environment, there is a pressure on companies for reducing costs and increasing the quality by providing on time delivery. Maintenance, plays an important role in reducing cost, improving quality, reducing failures, minimizing machine downtime, increasing productivity and as a result achieving objectives of company. The aim of this paper is to select best maintenance strategy for a manufacturing company by using an integrated fuzzy MCDM (Multi-Criteria Decision Making) approach. This approach is based on fuzzy PIPRECIA (Pivot Pairwise Relative Criteria Importance Assessment) and fuzzy MOORA (Multi Objective and Optimization on the Basis of Ratio Analysis) methods. The selection of maintenance strategy is a multi-criteria decision making (MCDM) problem. As this problem includes uncertainties and difficulty in evaluating alternatives and criteria with definite expressions, fuzzy MCDM approach is proposed for selecting the best maintenance strategy. As a result of the application of the proposed integrated method in the manufacturing company, the ranking of the maintenance strategies was obtained, and predictive maintenance strategy was determined as the most appropriate maintenance strategy for the company.

Developing an advanced reliability analysis framework for marine systems operations and maintenance

Power generation system reliability is of great concern for all ship operators irrespective of sector, as it provides the highest utility and ensures collective safety of operators, passengers, equipment and cargo. A novel approach to system reliability analysis using DFTA, FMECA and BBN applied to 4 DGs has been conducted. The outcomes provide insight on faults and component criticality to vessel maintenance and availability. Building from the understanding of how multiple factors can influenced maintenance in addition to routine or age-related wear and tear of machinery. This research looked into operators’ peculiar challenges regarding environment, operational demands and technology challenges that affects maintenance and system reliability. A framework based on outputs from DFTA minimal cut set, RPN from FMECA were used as inputs for BBN to analyse ship marine DG system availability. A BBN influence diagram was used to build a maintenance strategy DSS. Overall outcome for the maintenance strategy selection DSS indicates relatively high unavailability. Therefore, DGs with low availability were recommended to be on Corrective action and ConMon, while DGs with good availability were recommended to be on PMS.

Maintenance strategies and energy efficiency: a review

PurposeThis paper reviews the literature on maintenance strategies for energy efficiency as a potential maintenance approach. The purpose of this paper is to identify the main concept and common principle for each maintenance strategy for energy efficiency.Design/methodology/approachA literature review has been carried out on maintenance and energy efficiency. The paper systematically classified the literature into three maintenance strategies (e.g. inspection-based maintenance [IBM], time-based maintenance [TBM] and condition-based maintenance [CBM]). The concept and principle of each maintenance strategy are identified, compared and discussed.FindingsEach maintenance strategy's main concept and principle are identified based on the following criteria: data required and collection, data analysis/modeling and decision-making. IBM relies on human senses and common senses to detect energy faults. Any detected energy losses are quantified to energy cost. A payback period analysis is commonly used to justify corrective actions. On the other hand, CBM monitors relevant parameters that indicate energy performance indicators (EnPIs). Data analysis or deterioration modeling is needed to identify energy degradation. For the diagnostics approach, the energy degradation is compared with the threshold to justify corrective maintenance. The prognostics approach estimates when energy degradation reaches its threshold; therefore, proper maintenance tasks can be planned. On the other hand, TBM uses historical data from energy monitoring. Data analysis or deterioration modeling is required to identify degradation. Further analysis is performed to find the optimal time to perform a maintenance task. The comparison between housekeeping, IBM and CBM is also discussed and presented.Practical implicationsThe literature on the classification of maintenance strategies for energy efficiency has been limited. On the other hand, the ISO 50001 energy management systems standard shows the importance of maintenance for energy efficiency (MFEE). Therefore, to bridge the gap between research and industry, the proposed concept and principle of maintenance strategies will be helpful for practitioners to apply maintenance strategies as energy conservation measures in implementing ISO 50001 standard.Originality/valueThe novelty of this paper is in-depth discussion on the concept and principle of each maintenance strategy (e.g. housekeeping or IBM, TBM and CBM) for energy efficiency. The relevant literature for each maintenance strategy was also summarized. In addition, basic rules for maintenance strategy selection are also proposed.

APPLICATION OF THE MULTICRITERIA DECISION-MAKING FOR SELECTING OPTIMAL MAINTENANCE STRATEGY

Assessing the best set of maintenance guidelines for various types of failures is often a challenging and complex task. It requires understanding various factors such as safety aspects, environmental issues, energy savings, costs, budget constraints, system reliability, resource utilization, and more. Implementing the correct maintenance process is a critical step in production to increase reliability and improve the effectiveness and quality of the production system. Despite the significant importance of this issue, there are not many studies that analyse and develop procedures for selecting the optimal maintenance strategy. This paper presents the selection of the optimal maintenance strategy using multicriteria decision-making, specifically the Analytic Hierarchy Process (AHP), for a case study involving a company in the automotive industry. The defined alternatives are the four most commonly used machine maintenance strategies in the industry: corrective, preventive, condition- based maintenance, and total productive maintenance. The decision criteria considered in the analysis are: production quality, reliability, costs, and safety, along with their respective sub-criteria.

Artificial Intelligence Supported Aircraft Maintenance Strategy Selection with q-Rung Orthopair Fuzzy TOPSIS Method

In the aviation sector as unscheduled maintenance, repair and overhaul cost too much and these activities also negatively affect the prestige of the companies, deciding the most appropriate maintenance strategy is crucial. Today artificial intelligence methods, especially machine learning techniques facilitate failure detection and predict the wear and tear of the equipment before the occurrence of a serious failure. In this paper, artificial intelligence-supported corrective, predictive, and prescriptive maintenance methods are examined. Those most common aircraft maintenance approaches are compared regarding cost, reliability, failure detection, and downtime period using decision makers' subjective evaluations with the help of the q-rung orthopair fuzzy TOPSIS method which mitigates the drawbacks of uncertainty in human decision making. Stable and efficient results are obtained regarding the selection of an appropriate maintenance strategy. This article might be the first quantitative research that evaluates and compares AI-supported aircraft maintenance strategies.

A road maintenance management strategy evaluation and selection model by integrating Fuzzy AHP and Fuzzy TOPSIS methods: The case of Ethiopian Roads Authority

The construction industry is the most important for the social, economic, and political development of countries. The road construction sector is the largest in the construction industry. Inappropriate road maintenance leads to an inefficient and random way of spending maintenance costs. Therefore, road maintenance needs to prevent deterioration that shortens the service life of the road. Thus, the study was focused on a road maintenance strategy evaluation and selection model by integrating Fuzzy AHP and Fuzzy TOPSIS methods. The evaluation was a multiple-criteria decision-making problem. The fuzzy AHP technique was used to determine criterion weight. To deal with the uncertain judgment of decision makers, a fuzzy TOPSIS method is applied as an evaluation tool in a fuzzy environment where the uncertain, subjectivity, and imprecise judgments of decision makers are handled with linguistic values parameterized by triangular fuzzy numbers. To achieve the objective, the data were collected from primary and secondary sources. The method of data analysis for this study was made by integrated methodology, and the analysis was made by using Microsoft Excel. The study revealed that quality, maintenance time, environment, production cost, and profit are the most important selection criteria. The finding of the study revealed that periodic maintenance was the best relative closeness value among the considered alternatives. This study recommends that the analysis method can be implemented by the Ethiopian Roads Authority to demonstrate its practicality in selecting the appropriate strategy.

Post-warranty maintenance strategy selection using shape packages process

It is fair to assume that selection of a post-warranty maintenance strategy is one of the main challenges the capital-good owners face; however, there is relatively little research done in this area. This paper aims to assist asset owners in selecting the equipment maintenance package at the warranty termination time; it proposes the shape package process (SPP), which represents a comprehensive process that facilitates the choice between multiple packages, considering the cost and risk associated with each package.The main novelty of this paper is introducing two new parameters to compare maintenance packages: the risk reduction factor (RRF) to assess the maintenance task effectiveness in reducing non-financial risk and the value-added indicator (VAI) to evaluate the task efficiency in reducing the financial risk. Additionally, the suggested process includes mapping the risk profile associated with each package, enabling a cost-cutting process where the tasks with less value and risk mitigation can be easily identified and eliminated.A case study then demonstrates the process flexibility in comparing different maintenance packages using the analytical hierarchy process (AHP); while presenting RRF and VAI at the task and package levels when implemented in a cooling water system to select the post-warranty maintenance package from an asset owner's perspective.

A Hierarchical Decision Model Proposal for Maintenance Strategy Selection in the Hydroelectric Power Plants

Günümüzde enerji talebi her geçen gün artmaktadır. Enerji arzını sağlayan tesisler artan bu talebe kesintisiz bir şekilde cevap verecek politikalar ve uygulamalar geliştirmektedirler. Enerjide sürekliliği sağlamakta etkili olan unsurlardan biri de enerjinin üretildiği santralların bakım stratejilerini arzı kesintiye uğratmayacak şekilde belirlemektir. Bu çalışmada, bir hidroelektrik santralda en kritik elektriksel ekipmanın bulunması ve bu ekipman için bakım stratejisinin seçimi amaçlanmıştır. Bu çalışma iki aşamadan oluşmaktadır. İlk olarak en kritik elektriksel ekipman belirlenmiştir. Ekipmanların sıralanmasında kullanılan kriterlerin ağırlıklarının belirlenmesinde Pisagor Bulanık Analitik Hiyerarşi Prosesi (PBAHP) kullanılmıştır. Alternatiflerin sıralanmasında ise, PBAHP yöntemi ile TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yöntemi entegre edilmiştir. En kritik elektriksel ekipman olarak belirlenen generatör ekipmanı için en uygun bakım stratejisi revizyon bakım olarak belirlenmiştir.

Selection of Maintenance Strategies for Machines in a Series-Parallel System

In this paper, an assessment of the failure frequency of machines in a series-parallel structure was conducted. The analyses contain the decomposition of the system according to the general theory of complex systems. Based on the results obtained, a model for an optimal determination of the mean time to failure (MTTF) according to the expected value of the gamma distribution was proposed. For this purpose, the method of moments was used to determine the optimal values of the parameters of the estimated gamma distribution. The article is designed to be analytical. The object of consideration in this analysis is the real production system working in accordance with make-to-order, with a high degree of product customisation. Moreover, in the considered system occurs a dichotomy of mutually exclusive flows: push and pull. In the article, the main emphasis was placed on the applicability of the proposed MTTF value-shaping algorithm. Then, the maintenance strategy for each machine (reactive, preventive or predictive) was proposed. Maintenance strategy selection considered sustainable development principles in the criterion of minimizing maintenance actions, fulfilling the assumption of not interrupting the flow of the processed material. Based on inductive analyses, the concepts of improvement actions individually for each machine in the analysed subsystem were deductively defined. As a result, it was proved that a reactive maintenance strategy is appropriate for machines that have manufacturing reserves and are low priority. The equipment possessing manufacturing reserves but also having an impact on the risk of interrupting the flow of the processed material should be operated in accordance with a preventive maintenance strategy. A predictive maintenance strategy was proposed for the machines with the highest priority, which simultaneously do not have manufacturing reserves and the risk of manufacturing line operation interruption is high. The considerations were conducted with a holistic approach, taking into account the main functional areas of the enterprise.

Selection of an Optimal Maintenance Strategy Under Uncertain Conditions: An Interval Type-2 Fuzzy AHP-TOPSIS Method

The selection of an optimal maintenance strategy is one of the principal strategic decisions that must be taken in many contexts in order to maintain an asset with minimum deterioration and to deliver maximum output with high quality. When considering maintenance cost, reliability, and safety level of industrial assets, decision makers must select an appropriate maintenance strategy, preferably, with a known degree of uncertainty. This article utilizes a new interval type-2 fuzzy (IT2F) multicriteria decision-making method based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS), which can effectively handle the degree of uncertainty in group decision making and assist in the selection of the most feasible and optimal industrial asset maintenance strategy. The proposed method is compared with the results obtained from the conventional AHP-TOPSIS and type-1 fuzzy AHP-TOPSIS—the rank obtained from IT2F-AHP-TOPSIS is identical to those methods and can be used as an effective alternative to the type-1 fuzzy method. Compared with conventional AHP-TOPSIS and type-1 fuzzy AHP, IT2F-AHP-TOPSIS has the added advantage that it allows decision makers to define the membership function with greater flexibility and is able to handle uncertainty during decision making.

A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis

Gas path fault diagnosis of a gas turbine is a complex task involving field data analysis and knowledge-based reasoning. In this paper, a data-knowledge hybrid driven method for gas path fault diagnosis is proposed by integrating a physical model-based gas path analysis (GPA) method with a fault diagnosis ontology model. Firstly, a physical model-based GPA method is used to extract the fault features from the field data. Secondly, a virtual distance mapping algorithm is developed to map the GPA result to a specific fault feature criteria individual described in the ontology model. Finally, a fault diagnosis ontology model is built to support the automatic reasoning of the maintenance strategy from the mapped fault feature criteria individual. To enhance the ability of selecting a proper maintenance strategy, the ontology model represents more abundant knowledge from several sources, such as fault criteria analysis, physical structure analysis, FMECA (failure mode, effects, and criticality analysis), and the maintenance logic decision tool. The availability of the proposed hybrid driven method is verified by the field fault data from a real GE LM2500 PLUS gas turbine unit. The results indicate that the hybrid driven method is effective in detecting the path fault in advance. Furthermore, diversified fault information, such as fault effects, fault criticality, fault consequence, and fault detectability, could be provided to support selecting a proper maintenance strategy. It is proven that the data-knowledge hybrid driven method can improve the capability of the gas path fault detection, fault analysis, and maintenance strategy selection.

Analysis of the Influence of Component Type and Operating Condition on the Selection of Preventive Maintenance Strategy in Multistage Industrial Machines: A Case Study

The study of industrial multistage component’s reliability, availability and efficiency poses a constant challenge for the manufacturing industry. Components that suffer wear and tear must be replaced according to the times recommended by the manufacturers and users of the machines. This paper studies the influence of the individual maintenance values of Main Time To Repair (MTTR), Time To Provisioning (TTPR) and Time Lost Production (TLP) of each component, including the type of component and operation conditions as variables that can influence deciding on the best preventive maintenance strategy for each component. The comparison between different preventive maintenance strategies, Preventive Programming Maintenance (PPM) and Improve Preventive Programming Maintenance (IPPM) provide very interesting efficiency and availability results in the components. A case study is evaluated using PPM and IPPM strategies checking the improvement in availability and efficiency of the components. However, the improvement of stock cost of components by adopting IPPM strategy supposes the search of another more optimal solution. This paper concludes with the creation of a multidimensional matrix, for that purpose, to select the best preventive maintenance strategy (PPM, IPPM or interval between PPM and IPPM) for each component of the multistage machine based on its operating conditions, type of component and individual maintenance times. The authors consider this matrix can be used by other industrial manufacturing multistage machines to decide on the best maintenance strategy for their components.

Risk-based maintenance strategy selection for wind turbine composite blades

Blades are one of the most important components, in terms of capital and operational costs, of wind turbines. In contrast with some other elements of the turbine, the knowledge regarding the failure modes and processes of the composite materials that form the blade are less known. Therefore, providing a systematic study breaking down the individual components of the blade and analysing the criticality of each of the failure modes affecting them is the first step toward the implementation of measures reducing operational and maintenance costs. The scarce information that is publicly available regarding wind turbine blade failures and their constant evolution inclines risk analysis toward studies based on expert opinion. In this study, the criticality of the failure modes is determined through a failure mode and effects analysis (FMEA) that examines the severity and likelihood of occurrence of the failure modes identified. In light of the results of criticality, the current and potential maintenance strategies covering the different failure modes will be identified, along with the applicable monitoring techniques found in the literature, to propose risk mitigation strategies. The results of this study may be utilized for further exploration of operation and maintenance analysis toward the development of dynamic decision support systems.