Aparna Gupta and Chaipal Lawsirirat
This article aims to analyze strategically optimal maintenance actions for a multi‐component system whose deterioration is observed through a monitoring system set in place to…
Abstract
Purpose
This article aims to analyze strategically optimal maintenance actions for a multi‐component system whose deterioration is observed through a monitoring system set in place to support condition‐based maintenance.
Design/methodology/approach
Deterioration of a multi‐component system is modeled by a continuous‐time jump diffusion model which incorporates interaction between the components of the system. A simulation‐based optimization heuristic is developed to obtain strategically optimum maintenance actions. The methodology is applied to an illustrative example.
Findings
The article finds that the framework facilitates analyzing at a strategic level the role of degree of response to the deterioration of components for the overall functionality of a multi‐component system. The optimal solution for the illustrative example recommends a provider to perform a variety of opportunistic maintenance.
Practical implications
In this article, a framework is developed to determine strategically optimal maintenance actions for a multi‐component system whose deterioration is observed in real‐time through embedded monitoring units set in place to support condition‐based maintenance (CBM). The framework facilitates analyzing at a strategic level the role of degree of response to the deterioration of components for the overall functionality of a multi‐component system. A strategically optimal maintenance policy can then be enhanced to develop a detailed tactical maintenance strategy. This approach is expected to benefit the management of long‐term service agreements, where a service contract is sold bundled with a product, which makes a provider responsible for maintaining the product over a specified contract period.
Originality/value
Besides a tactical approach for performing maintenance, in order to stay profitable in the long‐run, a decision maker needs to assess the strategic performance of maintenance strategies adopted. This framework is a first attempt to facilitate this analysis at a strategic level for a monitoring‐enabled multi‐component system.
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Hamid Reza Golmakani and Fahimeh Fattahipour
This paper aims to address the effect of inspection intervals on cost function in condition‐based maintenance (CBM) and show how selecting an appropriate inspection scheme may…
Abstract
Purpose
This paper aims to address the effect of inspection intervals on cost function in condition‐based maintenance (CBM) and show how selecting an appropriate inspection scheme may reduce the cost associated to a CBM program.
Design/methodology/approach
In CBM, replacement policy is often defined as a threshold for replacement or leaving an item in operation until next inspection, depending on monitoring information. The control limit replacement policy framework, already reported by some research referred to in this paper, is utilized to determine the optimal replacement threshold. Having released the assumption that the inspections are performed at fixed and constant intervals, an iterative procedure is proposed to evaluate alternative inspection schemes and their associated total average cost of replacements and inspections.
Findings
The paper proposes an approach in which preventive and failure replacement costs as well as inspection cost are taken into account to determine the optimal replacement policy and an age‐based inspection scheme such that the total average cost of replacements and inspections is minimized.
Practical implications
In many practical situations where CBM is implemented, e.g. manufacturing processes, inspections require labor, specific test devices, and sometimes suspension of the operations. Thus, when inspection cost is considerable, by applying the proposed approach, one can obtain an inspection scheme that reduces the cost.
Originality/value
Using the approach proposed in the paper, a cost‐effective age‐based inspection scheme for a system under CBM is determined.
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A.K.S. Jardine, V. Makis, D. Banjevic, D. Braticevic and M. Ennis
Notes earlier work which commented on the formation of a research group to develop condition‐based maintenance (CBM) decision models and associated software. This paper provides…
Abstract
Notes earlier work which commented on the formation of a research group to develop condition‐based maintenance (CBM) decision models and associated software. This paper provides an update on the research direction that has been taken since 1995. In particular, the structure of software for CBM decision making is highlighted, along with possible future research directions.
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Farnoosh Naderkhani, Leila Jafari and Viliam Makis
The purpose of this paper is to propose a novel condition-based maintenance (CBM) policy with two sampling intervals for a system subject to stochastic deterioration described by…
Abstract
Purpose
The purpose of this paper is to propose a novel condition-based maintenance (CBM) policy with two sampling intervals for a system subject to stochastic deterioration described by the Cox’s proportional hazards model (PHM).
Design/methodology/approach
In this paper, the new or renewed system is monitored using a longer sampling interval. When the estimated hazard function of the system exceeds a warning limit, the observations are taken more frequently, i.e., the sampling interval changes to a shorter one. Preventive maintenance is performed when either the hazard function exceeds a maintenance threshold or the system age exceeds a pre-determined age. A more expensive corrective maintenance is performed upon system failure. The proposed model is formulated in the semi-Markov decision process (SMDP) framework.
Findings
The optimal maintenance policy is found and a computational algorithm based on policy iteration for SMDP is developed to obtain the control thresholds as well as the sampling intervals minimizing the long-run expected average cost per unit time.
Research limitations/implications
A numerical example is presented to illustrate the whole procedure. The newly proposed maintenance policy with two sampling intervals outperforms previously developed maintenance policies using PHM. The paper compares the proposed model with a single sampling interval CBM model and well-known age-based model. Formulas for the conditional reliability function and the mean residual life are also derived for the proposed model. Sensitivity analysis has been performed to study the effect of the changes in the Weibull parameters on the average cost.
Practical implications
The results show that considerable cost savings can be obtained by implementing the maintenance policy developed in this paper.
Originality/value
Unlike the previous CBM policies widely discussed in the literature which use sequential or periodic monitoring, the authors propose a new sampling strategy based on two sampling intervals. From the economic point of view, when the sampling is costly, it is advantageous to monitor the system less frequently when it is in a healthy state and more frequently when it deteriorates and enters the unhealthy state.
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Basim Al‐Najjar and Wenbin Wang
Rolling element bearing failures in paper mill machines are considered in relation to their critical role in the machine function. The use of expensive, sophisticated and highly…
Abstract
Rolling element bearing failures in paper mill machines are considered in relation to their critical role in the machine function. The use of expensive, sophisticated and highly automated equipment and machines and the intention to achieve higher quality products, longer machine life, higher machinery effectiveness and safer operating processes were the main driving force motivating efforts to improve the maintenance concept during the last 50 years. In this paper, a conceptual model that integrates the available condition information, the deterministic models used in condition monitoring based upon mechanical theory and the probabilistic models used in the area of operational research is developed and its applicability is discussed. This model covers fault detection of a mechanical component such as a rolling element bearing, prediction of its vibration level in the near future, assessment of the probability of failure of a component over a finite period of time of interest.
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Albert H.C. Tsang, W.K. Yeung, Andrew K.S. Jardine and Bartholomew P.K. Leung
This paper aims to discuss and bring to the attention of researchers and practitioners the data management issues relating to condition‐based maintenance (CBM) optimization.
Abstract
Purpose
This paper aims to discuss and bring to the attention of researchers and practitioners the data management issues relating to condition‐based maintenance (CBM) optimization.
Design/methodology/approach
The common data quality problems encountered in CBM decision analyses are investigated with a view to suggesting methods to resolve these problems. In particular, the approaches for handling missing data in the decision analysis are reviewed.
Findings
This paper proposes a data structure for managing the asset‐related maintenance data that support CBM decision analysis. It also presents a procedure for data‐driven CBM optimization comprising the steps of data preparation, model construction and validation, decision‐making, and sensitivity analysis.
Practical implications
Analysis of condition monitoring data using the proportional hazards modeling (PHM) approach has been proved to be successful in optimizing CBM decisions relating to motor transmission equipment, power transformers and manufacturing processes. However, on many occasions, asset managers still make sub‐optimal decisions because of data quality problems. Thus, mathematical models by themselves do not guarantee that correct decisions will be made if the raw data do not have the required quality. This paper examines the significant issues of data management in CBM decision analysis. In particular, the requirements of data captured from two common condition monitoring techniques – namely vibration monitoring and oil analysis – are discussed.
Originality/value
This paper offers advice to asset managers on ways to avoid capturing poor data and the procedure for manipulating imperfect data, so that they can assess equipment conditions and predict failures more accurately. This way, the useful life of physical assets can be extended and the related maintenance costs minimized. It also proposes a research agenda on CBM optimization and associated data management issues.
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Kym Fraser, Hans-Henrik Hvolby and Tzu-Liang (Bill) Tseng
Maintenance and its management has moved from being considered a “necessary evil” to being of strategic importance for most competitive organisations around the world. In terms of…
Abstract
Purpose
Maintenance and its management has moved from being considered a “necessary evil” to being of strategic importance for most competitive organisations around the world. In terms of the identification and use of organisational-wide maintenance management models the picture is not clears from both a literature and practical perspective. The purpose of this paper is to shed light on the various models and their use in real-world applications, and in doing so, explores the gap between academic research and practice.
Design/methodology/approach
For this paper two comprehensive reviews of the literature were undertaken, first, to identify and categorise the various maintenance management models, and second, to determine the depth of empirical evidence for the popular models in real-world applications. Descriptive analysis of both the practical examples and empirical evidence rates (EER) for maintenance related journals is provided.
Findings
Within the literature 37 maintenance management models were identified and categorised. From these, three models were found to be popular: total productive maintenance (TPM), condition based maintenance, and reliability centred maintenance. While several thousand papers discussed these three models, only 82 articles were found to provide empirical evidence.
Research limitations/implications
While providing a sound foundation for future research the outcomes are based solely on academic literature. Analysis of EER outside the field of maintenance is needed to make comparisons.
Practical implications
The paper offers practitioners a detailed contemporary overview of maintenance management models along with tabulated results of practical examples to present day organisations. Such practical-focused papers are very limited within academic literature.
Social implications
With EER as low as 1.5 per cent for some journals this paper acts as a reminder to researchers that they have an obligation to society to spend taxpayer funded research on addressing social needs and real-world problems.
Originality/value
This paper makes a concerted attempt to link academic research with management and operational practitioners. While the paper is critical of the current academic imbalance between theory and practice, a number of suggestions to improve EER are offered in the conclusions.
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Nader S. Santarisi and Raid M. Almomany
The cement industry provides high wear environment for many casting units which results in high production costs and consumes both the time and efforts of different available…
Abstract
Purpose
The cement industry provides high wear environment for many casting units which results in high production costs and consumes both the time and efforts of different available skills to investigate and replace the worn parts. Aims to consider a predictive system for the wear of such units that will reduce the related unpredicted incidents.
Design/methodology/approach
In this work, cement mill shell liners are non‐repairable castings and subjected to accelerated deterioration process as the operational hours and hence the production increases. Both decision variables and their interaction were used to construct models that best describe the wear process of the shell lines for two compartments' ball mill. Data from past records were used and analyzed using the statistical package SPSS version 10.0.
Findings
The best regression model for each compartment was found to be exponential function but with different shape parameters. Some data were used to validate the model using statistical calculations of errors. The proposed models were used to develop the best operational time for liners' replacement strategy.
Practical implications
The findings of the optimal replacement time resulted in higher mill productivity, lower specific power consumption, reduced spare parts cost and reduced production loss for unjustified mill stoppages.
Originality/value
Develops a mathematical model for cement mill shell liners' life to determine the optimal replacement strategy.