Proper Maintenance Strategy Research Articles

AHP-VIKOR-based methodology for determining maintenance criticality

The paper aims to develop a new methodology based on integrated multi-attribute decision making approaches, i.e., analytic hierarchy process (AHP) and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) technique for finding the maintenance criticality of the system by ranking its failure causes. The ranking is framed by considering five criteria namely likelihood of failure occurrence, likelihood of non-detection, downtime length, standby part criticality and safety risk. In the proposed methodology, first the priority weight of each criterion is calculated by using AHP approach and then VIKOR method is applied for finding the ranking of failure causes in order to select the proper maintenance strategy. An actual case study of pulping system for a paper production industry is performed to select the maintenance strategy in order to demonstrate the use of the proposed methodology.

Maintenance management of load haul dumper using reliability analysis

PurposeLoad haul dumper (LHD) is one of the main ore transporting machineries used in underground mining industry. Reliability of LHD is very significant to achieve the expected targets of production. The performance of the equipment should be maintained at its highest level to fulfill the targets. This can be accomplished only by reducing the sudden breakdowns of component/subsystems in a complex system. The identification of defective component/subsystems can be possible by performing the downtime analysis. Hence, it is very important to develop the proper maintenance strategies for replacement or repair actions of the defective ones. Suitable maintenance management actions improve the performance of the equipment. This paper aims to discuss this issue.Design/methodology/approachReliability analysis (renewal approach) has been used to analyze the performance of LHD machine. Allocations of best-fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K–S) test. Parametric estimation of theoretical probability distributions was made by utilizing the maximum likelihood estimate (MLE) method.FindingsIndependent and identical distribution (IID) assumption of data sets was validated through trend and serial correlation tests. On the basis of test results, the data sets are in accordance with IID assumption. Therefore, renewal process approach has been utilized for further investigation. Allocations of best-fit distribution of data sets were made by the utilization of Kolmogorov–Smirnov (K–S) test. Parametric estimation of theoretical probability distributions was made by utilizing the MLE method. Reliability of each individual subsystem has been computed according to the best-fit distribution. In respect of obtained reliability results, the reliability-based preventive maintenance (PM) time schedules were calculated for the expected 90 percent reliability level.Research limitations/implicationsAs the reliability analysis is one of the complex techniques, it requires strategic decision making knowledge for the selection of methodology to be used. As the present case study was from a public sector company, operating under financial constraints the conclusions/findings may not be universally applicable.Originality/valueThe present study throws light on this equipment that need a tailored maintenance schedule, partly due to the peculiar mining conditions, under which they operate. This study mainly focuses on estimating the performance of four numbers of well-mechanized LHD systems with reliability, availability and maintainability (RAM) modeling. Based on the drawn results, reasons for performance drop of each machine were identified. Suitable recommendations were suggested for the enhancement of performance of capital intensive production equipment. As the maintenance management is only the means for performance improvement of the machinery, PM time intervals were estimated with respect to the expected rate of reliability level.

Choosing a Proper Maintenance and Repair Strategy for Tractors (in Urmia)

Abstract Maintenance plays an important role in reliability, availability, production quality, risk reduction, efficiency increasing, equipment security, etc. Therefore, maintenance strategies have a special position in industry. Simultaneously, effect of tractor power is considerable in agriculture; therefore, it is necessary to pay attention to the probability of failure, repair time and causes of malfunctioning. Many variables, including cost, depreciation, breakdowns, annual performance, etc., have major impact on tselection of a maintenance strategy. Therefore, these variables were determined with the help of experts. Subsequently, these were clustered into several main criteria groups on the basis of factor analysis using SPSS software. Obtained results were used in a hierarchical structure to suggest the most appropriate maintenance strategy based on pair-wise comparison. According to the regional conditions and available facilities, results showed that the proactive maintenance strategy proved to be the best suggested option from four maintain strategies, namely emergency maintenance, proactive maintenance, preventive maintenance, predictive maintenance.

AHP-VIKOR based methodology for determining maintenance criticality

The paper aims to develop a new methodology based on integrated multi-attribute decision making approaches, i.e., analytic hierarchy process (AHP) and Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) technique for finding the maintenance criticality of the system by ranking its failure causes. The ranking is framed by considering five criteria namely likelihood of failure occurrence, likelihood of non-detection, downtime length, standby part criticality and safety risk. In the proposed methodology, first the priority weight of each criterion is calculated by using AHP approach and then VIKOR method is applied for finding the ranking of failure causes in order to select the proper maintenance strategy. An actual case study of pulping system for a paper production industry is performed to select the maintenance strategy in order to demonstrate the use of the proposed methodology.

Maintenance optimization of power systems with renewable energy sources integrated

This paper proposes a quantitative maintenance optimization problem for developing reliability centred maintenance for a power system with renewable energy sources. Reliability and cost are two important interlinked aspects considered by system operators in many deregulated power systems. Reliability centred maintenance is an effective method to consider both of these aspects when performing the maintenance optimization. Nevertheless, this method has not adequately studied for a power system with renewable energy sources included.According to the maintenance optimization problem proposed in this paper, first, the most critical components of the system are selected. Then, a set of maintenance strategies are proposed for all critical components. After that, the total cost of each maintenance strategy for all critical components are calculated as the summation of operation, maintenance, environmental, and interruption costs. Finally, the best maintenance strategy for each critical component is selected by identifying the lowest total cost of different maintenance strategies. The proposed method is tested on IEEE 14-bus system. The results show that the proposed maintenance optimization method provides a useful way for deciding the most proper maintenance strategies for the studied system.

Maintenance policy selection using fuzzy failure modes and effective analysis and key performance indicators

Maintenance policy selection (MPS) plays an important role in determining a proper maintenance strategy based on the real equipment condition. This study is intended to address the concept of MPS proposing an approach to improve current maintenance selection methods. Further, an integrated three-step model is introduced for MPS using fuzzy failure mode and effects analysis (FFMEA) and fuzzy analytical hierarchy process (FAHP). In the first step, a combination of FFMEA and FAHP are applied to calculate the risk of equipment. For the risk priority number computation, three dimensions including severity, occurrence, and detection and their identified sub-dimensions are weighted by three domain experts. The second step is aimed at evaluation of all criteria that crucially affect MPS where four key performance indicators weighted by AHP are defined for equipment criticality assessment. Finally, a novel fuzzy approach is proposed to choose a proper maintenance strategy for each facility according to RPN and criticality scores. A case study is conducted to demonstrate the applicability of the proposed method.

Maintenance policy selection using fuzzy failure modes and effective analysis and key performance indicators

Maintenance policy selection (MPS) plays an important role in determining a proper maintenance strategy based on the real equipment condition. This study is intended to address the concept of MPS proposing an approach to improve current maintenance selection methods. Further, an integrated three-step model is introduced for MPS using fuzzy failure mode and effects analysis (FFMEA) and fuzzy analytical hierarchy process (FAHP). In the first step, a combination of FFMEA and FAHP are applied to calculate the risk of equipment. For the risk priority number computation, three dimensions including severity, occurrence, and detection and their identified sub-dimensions are weighted by three domain experts. The second step is aimed at evaluation of all criteria that crucially affect MPS where four key performance indicators weighted by AHP are defined for equipment criticality assessment. Finally, a novel fuzzy approach is proposed to choose a proper maintenance strategy for each facility according to RPN and criticality scores. A case study is conducted to demonstrate the applicability of the proposed method.

Extending a pessimistic–optimistic fuzzy information axiom based approach considering acceptable risk: Application in the selection of maintenance strategy

Maintenance is considered as an important factor in the improvement of machine performance. Indeed, maintenance plays an important role in maintaining reliability, availability, quality of products, reducing risk, improving efficiency, and safety. In maintenance problems, the desired real data cannot easily be found, thus decision making by applying expert knowledge is the only way. However, due to the existence of uncertainties in these assessments, the accuracy of evaluations may be questioned. The aim of this study was to present an appropriate mathematical model for decision making in the assessment and selection of proper maintenance strategy in risky situations. For this purpose, a model based on fuzzy axiomatic design (FAD) is presented, wherein each evaluation has both optimistic and pessimistic fuzzy scores, as the fuzzy evaluations themselves have risks. In order to improve the accuracy of the presented method, a new concept called “acceptable risk” has been suggested. A real case study, to measure the effectiveness of the proposed model, is conducted in a rolling mill company and the obtained results are compared with other FAD concepts.

Ranking maintenance strategies for sustainable maintenance plan in manufacturing systems using fuzzy axiomatic design principle and fuzzy-TOPSIS

PurposeMaintenance plans are programmes, which follow maintenance appraisals, contain information of what to do and the time approximates for accomplishments. They also deal with how to carry out maintenance jobs. In contemporary period, curiosity has proliferated about how sustainability affects manufacturing plans. The purpose of this paper is to offer a comprehensive notion of maintenance sustainability in maintenance planning. The literature has downplayed maintenance sustainability but may support in understanding how to crack the present company-community conflicts about the negative influence of manufacturing on the environment.Design/methodology/approachThis study develops the idea of selecting the proper maintenance strategy based on integrated fuzzy axiomatic design (FAD) principle and fuzzy-TOPSIS. This work suggests that the maintenance function is an uncertain, activity-oriented system. To fully appreciate the proposed framework, the work employs data from a cement manufacturing plant to test the structure. This study offers 20 influential factors on which it build the fundamental structure of maintenance system sustainability for manufacturing concerns. A novel literature contribution that departs from existing conceptions is the classical determination of weights of each sustainability factor, employing fuzzy entropy weighting approach. Furthermore, work innovatively determines the ranking of some important tenets of sustainability in maintenance and optimises the maintenance consumables employing the FAD principle.FindingsInterestingly, the output of the investigation revealed differences as the work adopts fuzzy-TOPSIS in comparison with FAD principle.Originality/valueCase examination of a real-life manufacturing venture validated the claims, showing maintenance workforce training as a top-echelon strategy for maintenance system sustainability.

Maintenance strategy selection with risky evaluations using RAHP

AbstractIn recent years, maintenance, especially its preventive type, has been seen as an effective and considerable factor in improving the functions of machines. Maintenance plays a vital role in organizations by keeping and increasing the reliability, accessibility, the quality of products, risk mitigation, return improvement, and safety. An effective maintenance programme can be realized by implementing a proper maintenance strategy. Therefore, maintenance and its strategies have a special status in industries. However, selecting a proper maintenance strategy has always been a complex process because maintenance is a nonrepetitive task. Likewise, the lack of failure records and constant changes in the conditions of machines makes it further complicated. Hence, the decision‐making also depends on experts' opinions and because some kind of risk and uncertainty are always there in experts evaluations, the reliability of evaluations is questionable. The present study was aimed to develop a risk‐based model grounded on the analytical hierarchical process, namely, RAHP, to meet this need of maintenance. A case study from a steel rolling company was considered to evaluate the effectiveness of this model.

A new fuzzy approach to identify the critical risk factors in maintenance management

Maintenance is carried out to prevent the occurrence of events that lead to malfunction and interruption of the production process or the operation of the concerned equipment. One of the main approaches in maintenance is to identify the risk of equipment failure mode. Whereas by reducing the high risk of failure mode the reliability and availability of equipment enhanced and the cost of shutdown reduced, identifying the risk of failure is important. In this paper, a fuzzy hybrid approach, including failure mode and effective analysis (FMEA), decision-making trial and evaluation laboratory technique (DEMATEL), and analytic network process (ANP), is presented to select an appropriate maintenance policy through identifying the risk of failures. The authors aim to develop a risk-based method for selecting a proper maintenance strategy to have available and reliable tamping equipment in railway of Iran. Because of considering the same weight of risk factors, failure occurrence, failure severity, and failure detection ability in FMEA, the traditional FMEA method cannot correctly predict the system’s behavior. So, in this paper, the risk factors as fuzzy variables are proposed and evaluated using fuzzy linguistic terms and fuzzy ratings. In a case study, the present approach is utilized for evaluation and determination of the risk of failure modes for a railway company. At first, fuzzy FMEA is used to identify the main risk and sub-risk of failure modes. Second, fuzzy DEMATEL method is used in order to put forward the interrelationship among the main risks which are determined through fuzzy FMEA. Then, the weights of the sub-risks are calculated by fuzzy ANP approach on the basis of cause–effect relationships that are exposed through DEMATEL method. Finally, the weights of sub-risks are determined by multiplying the weights which have been obtained from fuzzy FMEA to the weights of ANP supermatrix. The weights of sub-risks were determined on the basis of these examined dependencies, and these weights were used for a tamping machine to determine maintenance policy. Finally, some strategies and suggestions have been drawn concerning the needs to reduce the risks and improve the equipment’s availability.

A dual sensor signal fusion approach for detection of faults in rotating machines

The growing industrial sector utilizes machinery that needs to be monitored continuously by proficient experts and robust software to ensure a proper maintenance strategy. Condition monitoring using vibration signal analysis is one of the chief methods used in predictive maintenance strategy for rotating machinery. Generally, sound signal analysis is considered as secondary as it involves noise. In this paper, the signals for various rotating machinery faults are studied by simulating them in a machine fault simulator at various speeds and the best features are fused to obtain more efficiency in the fault diagnosis of rotating machinery. The vibration signal data obtained from accelerometers and sound signal data from a microphone is extracted for features using wavelet transform. The best features from vibration and sound signals are selected using the decision tree algorithm and are fused. Further, the features are classified using an artificial neural network and the corresponding efficiency at various motor speeds is found. The results of this paper imply that the signal fusion of vibration and sound by the decision tree algorithm is effective in machine fault diagnosis methodologies.

An Application of Reliability Centered Maintenance Using RPN Mean and Range on Conventional Lathe Machine

RCM provides a structured and practical approach for arriving at an acceptable maintenance strategy for each component of a given system. In this paper, a study on reliability centered maintenance (RCM) has been conducted to reduce the total number of failures and its effects on the conventional lathe machine. The risk priority number (RPN) is, one of the key factors in determining the critical ranking of a failure mode of the mechanical equipment but there is a drawback in determining the critical ranking from RPN. There might be more than one failure modes which can share the same RPN number and therefore critical ranking cannot be assigned to those failure modes. Initially, the Severity, Occurrence and Detection rating integrating with failure mode and effect analysis (FMEA) is taken from several experts and then RPN is calculated for each of those experts. Then the criticality of the failure mode of the functionally significant items of conventional lathe machine is judged on the basis of mean RPN value as well as using the range. Overall the risk or criticality level of each failure mode is identified. After that, RCM logic is used for selection of a proper maintenance strategy for each failure mode.

Multicriteria FMECA Based Decision-Making for Aluminium Wire Process Rolling Mill through COPRAS-G

This paper presents a multifactor decision-making approach based on “grey-complex proportional assessment (COPRAS-G) method” in a view to overcome the limitations of Failure Mode Effect and Criticality Analysis (FMECA). In this model, the scores against each failure mode are expressed in grey number instead of crisp values to evaluate the criticalities of the failure modes without uncertainty. The suggested study is carried out to identify the weights of major failure causes for bearings, gears, and shafts of aluminium wire rolling mill plant. The primary findings of the paper are that sudden impact on the rolls seems to be most critical failure cause and loss of power seems to be least critical failure cause. It is suggested to modify the current control practices with proper maintenance strategy based on achieved maintainability criticality index (MCI) for different failure causes. The outcome of study will be helpful in deriving optimized maintenance plan to maximize the performance of process industry.

Proposed Risk Model of Maintenance Management for Power Transformer in Transmission System

Risk on acquisition and maintenance costs of electrical equipments, in particular power transformer, has been faced by electric utilities so that proper maintenance strategies for reducing the risk with acceptable reliability to the system are concerned. Risk model, developed by combining transformer condition and network characteristic with applied scoring and weighting approach, is proposed. Diagnostic tests such as electrical and oil tests are utilized for condition assessment, while operating characteristics of the transformer are used for network assessment. In addition, the developed risk model with higher priority on the transformer network respect to the transformer condition with 70%-30% weighting, provides recommended maintenance zones that are categorized by risk levels of the transformer. The risk level of the transformer is calculated by means of trigonometry technique. The transformer with high risk is maintained first for its maintenance task. The test results and the network characteristic data of the transformers rating of 115/22 kV and 230/115 kV are selected and collected for supporting the proposed risk model. Subsequently, maintenance actions of the obtained risky transformers are subsequently planned suitably to improve reliability and stability of the transmission system.

On the Assessment of Performance and Robustness of Condition-Based Maintenance Strategies

Many research works have shown in the literature that condition-based maintenance strategies are very efficient in keeping highest systems availability at lowest maintenance costs. The economic performance of such strategies are usually assessed through the well-known long-run expected maintenance cost rate criterion. However, ones do not often study their robustness in the sense that maintenance costs can significantly vary from a renewal cycle to another. Obviously, assessing a maintenance strategy according to the above cost criterion does not assure its robustness, because at the same time it may lead to over or under budgeting estimation. In this context, the present paper aims develop a cost model that allows quantifying both the economic performance and robustness to assess the condition-based maintenance strategies. Two representative strategies are investigated: a periodic inspection and replacement strategy, and a quantile-based inspection and replacement strategy. The comparisons of these strategies allows us to find out factors which mostly affect their performance and robustness, hence suggesting proper maintenance strategy.

An integrated fuzzy analytic hierarchy process and fuzzy multiple-criteria decision-making simulation approach for maintenance policy selection

Selecting a proper maintenance strategy in an attempt to preclude failures is of critical significance in system engineering due to its fallbacks in the safety and economics of plants operation. This process is a typical multiple-criteria decision-making (MCDM) problem that involves both tangible and intangible parameters that are often in conflict with each other. In this paper, an integrated analytic hierarchy process (AHP)–fuzzy MCDM approach is proposed to perform a comprehensive comparison between different maintenance policies. For this purpose, various criteria are taken into account that are different in nature, as some give a crisp value obtained from simulation, some are defined in linguistic terms based on experts’ opinions and some are in the form of triangular fuzzy numbers. The AHP method is used to determine the importance weights of the criteria. Subsequently, a distance-based fuzzy MCDM approach is employed to rank different maintenance policies and select the most appropriate one. Moreover, the fuzzy technique for the order of prioritization by similarity to ideal solution is used for verification of the proposed integrated approach. Lastly, the impact of each criterion on the rankings is examined. Four commonly implemented maintenance policies, namely condition-based, time-based, failure-based and opportunistic, are considered in this study. Also, a real-world example is presented to demonstrate the applicability of the proposed approach. The most significant feature of this approach lies in its capability in incorporating data in the forms of linguistic variables, triangular fuzzy numbers and crisp numbers into the evaluation process.

Clustering and sensing with decentralized detection in vehicular ad hoc networks

In the near future, vehicles will be more and more advanced sensing platforms: for instance, at least one smartphone (with several on-board sensors) is likely to be inside each vehicle. Smartphone-based inter-vehicle communications thus support the creation of vehicular sensor networks (VSNs). In this paper, we analyze the performance of clustered VSNs, where (hierarchical) decentralized detection schemes are used to estimate the status of an observed spatially constant phenomenon of interest. Clustering makes processing efficient and the architecture scalable. Our approach consists of the creation, during a downlink phase, of a clustered VSN topology through fast broadcast of control messages, started from a remote sink (e.g., in the cloud), through a novel clustering protocol, denoted as cluster-head election irresponsible forwarding (CEIF). This clustered VSN topology is then exploited, during an uplink phase, to collect sensed data from the vehicles and perform distributed detection. The performance of the proposed scheme is investigated considering mostly IEEE 802.11b (smartphone-based) as well as IEEE 802.11p (inter-vehicle) communications in both highway-like and urban-like scenarios. Our results highlight the existing trade-off between decision delay and energy efficiency. The proposed VSN-based distributed detection schemes have to cope with the “ephemeral” nature of clusters. Therefore, proper cluster maintenance strategies are needed to prolong the cluster lifetime and, as a consequence, the maximum amount of data which can be collected before clusters break. This leads to the concept of decentralized detection “on the move.”

Assessment of distance-based multi-attribute group decision-making methods from a maintenance strategy perspective

Maintenance has been acknowledged by industrial management as a significant influencing factor of plant performance. Effective plant maintenance can be realized by developing a proper maintenance strategy. However, selecting an appropriate maintenance strategy is difficult because maintenance is a non-repetitive task such as production activity. Maintenance also does not leave a consistent traceable record that can be referred to during the decision-making process. The involvement of tangible and intangible factors in the assessment process further increases the complexity of the decision-making process. The technique of preference order by similarity to ideal solution (TOPSIS) is one of the most well-known decision- making methods and has been widely used by organiza- tions to conduct effective decisions regarding maintenance issues. TOPSIS has also evolved by integrating different approaches such as the fuzzy concept. Although numerous TOPSIS applications for maintenance decision making have been published, the effectiveness of crisp TOPSIS and fuzzy TOPSIS needs to be investigated further. This paper attempts to present a comparison between conventional crisp TOPSIS and fuzzy TOPSIS from a group mainte- nance decision-making perspective by an empirical illus- tration. Sensitivity analysis is conducted to demonstrate further the resilience of crisp TOPSIS and fuzzy TOPSIS.

Railway Security System based on Wireless Sensor Networks: State of the Art

Railways are large infrastructures and are the prime mode of transportation in many countries. The railways have become a prime means of transportation owing to their capacity, speed, and reliability. Even a small improvement in performance of railways has significant economic benefits to rail industry. Thus, a proper maintenance strategy is required to govern optimization of inspection frequency and/or improvement in skill and efficiency. Accidents happening due to track breaking have been a big problem for railways for life security and timely management of services. This breakage needs to be identified in real time before a train actually comes near to the broken track and get subjected to an accident. In this paper, different kinds of rail defects inspection and maintenance methods are described and a basic algorithm is readdressed that makes use of wireless acoustic sensors for detecting cracks and breakages in the railway tracks.