W Wang

University of Salford, Salford, ENG, United Kingdom

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Publications (13)14.05 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: This paper is concerned with a problem identification and problem focus process in maintenance modelling. It endeavours to describe the process of moving from vague problem understanding towards more specific problem formulation and problem focus in the pursuit of practical decision making. This process was conducted using several analytical tools that complemented each other such as regression analyses, snapshot modelling and delay time modelling. As in many case studies related to maintenance modelling, this study also makes use of the experience of experts. It can be seen from the paper that subjective data estimates can prove to be a useful input for modelling. The analysis shows how simple modelling of maintenance problems can provide useful insights and better understanding of the problem in hand.
    International Journal of Production Research 02/2008; 46(4):1031-1046. · 1.46 Impact Factor
  • W Wang, AH Christer
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    ABSTRACT: In this paper, we present three solution algorithms for an established multi-component inspection system model based upon the delay time concept. First, Algorithm 1 is developed for obtaining the system replacement time if the defect arrival process is non-homogeneous. Algorithm 2 is presented as an extension to Algorithm 1 in which the non-constant optimal inspection intervals are also determined. Algorithm 3 is a numerical algorithm for solving an integral equation arising within the model in the case of opportunistic inspection at failures. Finally, an example is given to demonstrate the algorithm set in practice. The proof of the existence and uniqueness of solutions are presented.
    Computers & Operations Research 01/2003; · 1.91 Impact Factor
  • W Wang
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    ABSTRACT: This paper reports on a study of modelling condition monitoring intervals. The model is formulated based upon two important concepts. One is the failure delay time concept, which is used to divide the failure process of the item into two periods, namely a normal working period followed by a failure delay time period from a defect being first identified to the actual failure. The other is the conditional residual time concept, which assumes that the residual time also depends on the history condition information obtained. Stochastic filtering theory is used to predict the residual time distribution given all monitored information obtained to date over the failure delay time period. The solution procedure is carried out in two stages. We first propose a static model that is used to determine a fixed condition monitoring interval over the item life. Once the monitored information indicates a possible abnormality of the item concerned, that is the start of the failure delay time, a dynamic approach is employed to determine the next monitoring time at the current monitoring point given that the item is not scheduled for a preventive replacement before that time. This implies that the dynamic model overrides the static model over the failure delay time since more frequent monitoring might be needed to keep the item in close attention before an appropriate replacement is made prior to failure. Two key problems are addressed in the paper. The first is which criterion function we should use in determining the monitoring check interval, and the second is the optimization process for both models, which can be solved neither analytically nor numerically since they depend on two unknown quantities, namely, the available condition information and a decision of the time to replace the item over the failure delay time. For the first problem, we propose five appealingly good criterion functions, and test them using simulations to see which one performs best. The second problem was solved using a hybrid of simulation and analytical solution procedures. We finally present a numerical example to demonstrate the modelling methodology.
    Journal of the Operational Research Society 01/2003; · 0.99 Impact Factor
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    ABSTRACT: The context of planned preventive maintenance lends itself readily to probabilistic modelling. Indeed, many of the published theoretical models to be found in the literature adopt a Markov approach, where states are usually ‘operating’, ‘operating at one of several levels of deterioration’, and ‘failed’. However, most of these models assume the required Markovian property and do not address the issue of testing the assumption, or the related task of estimating parameters. It is possible that data are inadequate to test the assumption, or that the Markov property is believed to be not strictly valid, but acceptable as an approximation. In this paper we consider within a specific inspection–maintenance context the robustness of a Markov‐based model when the Markov assumption is not valid. This is achieved by comparing the output of an exact delay time model of an inspection–maintenance problem with that of a semi‐Markov approximation. The importance of establishing the vadility of the Markov property in the modelling application is highlighted. If the plant behaviour is seen to be nearly Markov, in the case considered the semi‐Markov model gives a good approximation to the exact model. Conversley if the Markov assumption is not a good approximation, the semi‐Markov model can lead to inappropriate advice.
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    ABSTRACT: This paper considers a stochastic dynamic system subject to random deterioration, with regular condition monitoring and preventive maintenance. A model is presented to advise at a monitoring check what maintenance action to take based upon the condition monitoring and preventive maintenance information obtained to date. A general assumption adopted in the paper is that the performance of the system concerned can not be described directly by the monitored information, but is correlated with it stochastically. The model is relevant to a large class of condition monitoring techniques currently employed in industry including vibration and oil analysis. The model is constructed under fairly general conditions and includes two novel developments. Firstly, the concept of the conditional residual time is used to measure the condition of the monitored system at the time of a monitoring check, and secondly, contrary to previous practice, the monitored observation is now assumed to be a function of the system condition. Relationships between the observed history of condition monitoring, preventive maintenance actions, and the condition of the system are established. Methods for estimating model parameters are discussed. Since the model presented is generally beyond the scope for an analytical solution, a numerical approximation method is also proposed. Finally, a case example is presented to illustrate the modelling concepts in the case of non-maintained plant.
    Journal of the Operational Research Society 01/2000; 51(2):145-155. · 0.99 Impact Factor
  • W Wang
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    ABSTRACT: Although much has been published concerning the optimality of a single-type inspection process, relatively little attention has been paid in the literature of maintenance modelling to the practice of multiple nested inspections. In this paper a novel model addressing multiple nested inspections of production plant at di!erent intervals is established based upon the delay time concept. A branch-and-bound algorithm is developed for "nding the optimal intervals for all the inspections which minimizes the long-term expected total cost per unit time. The modelling can be readily extended to model downtime or reliability. Necessary mathematics is presented and proofs are given. A numerical example is also given to illustrate the modelling and solution processes.
    Computers & Operations Research 01/2000; · 1.91 Impact Factor
  • A.H Christer, C Lee, W Wang
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    ABSTRACT: This paper describes a modeling study of preventive maintenance (PM) policy of production plant in a local company with a view to improving current practice. The model developed is based upon the delay time concept where because of an absence of PM data, the process parameters and the delay time distribution were estimated from failure data only using the method of maximum likelihood. Particular attention is paid to the problem arising during the parameter estimating process because of the inadequate recording of PM data and the implied correlation between model parameters. An objective estimation process has been adopted here as far as possible. The case of data deficiency explored in the study is important because it is a relatively general situation in practice. An inspection model is finally proposed to identify the best inspection policy based upon the estimated model parameters and the delay time distribution. It is concluded that the company has other problems to attend to before the inspection problem is finally solved, and a structured review of maintenance engineering practice is recommended.
    International Journal of Production Economics. 01/2000;
  • W. Wang, A. H. Christer
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    ABSTRACT: In this paper a model of a safety inspection process is proposed for the expected consequence of inspections over a finite time horizon. A single dominant failure mode is modelled, which has considerable safety or risk consequences assumed measurable either in cost terms or in terms of the probability of failure over the time horizon. The model established extends earlier work assuming an infinite time horizon, and uses the concept of delay time and asymptotic results from the theory of renewal and renewal reward processes. The paper establishes a pragmatic procedure for formulating objective functions which may be optimized to determine the optimal inspection intervals. Merits of both the exact and asymptotic formulations of these objective functions for possible use in the inspection optimization process are considered. Although the procedure for developing an objective function over a finite time zone inspection process assumes perfect inspection, it can be generalized to the imperfect inspection case. Because of the intractability of the mathematics, it is suggested that when optimizing an inspection process over a finite time zone, an asymptotic formulation of the objective function should be optimized, and this solution then checked and if necessary refined, using simulation calculation. A numerical example illustrates the performance of the basic periodic inspection policy over different time horizons using the asymptotic solution. The results are compared with simulations performed to estimate the exact expected cost measure. It is shown that the simpler asymptotic solution is satisfactory in the case considered, especially when the time horizon is relatively long. © 1997 John Wiley & Sons, Ltd.
    Quality and Reliability Engineering 12/1998; 13(4):217 - 224. · 0.68 Impact Factor
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    ABSTRACT: This paper presents a maintenance-modelling case study of a plant manufacturing brake linings. A delay–time model is developed and applied to model and optimize preventive maintenance (PM). A key subsystem in the plant is used to illustrate the modelling process and management reaction. Defects identified at PM may not all be removed. This incomplete response to PM is a feature which has not been modelled before. The parameter values of the delay-time process are estimated from objective data from maintenance records of failures, using the method of maximum likelihood. This is aided by a theorem extending results on the NHPP arival rate of failures in a perfect-inspection case to the non-perfect-inspection case. Problems of parameter estimation given inadequate data collected at PMs are discussed, and the necessity to augment objective data with subjective assessments highlighted. Based upon the estimated model parameters and delay-time distribution, an inspection model is constructed to describe the relationship between the total unit downtime and the PM interval. The response of management is discussed.
  • W. Wang
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    ABSTRACT: In this paper, the problems in the current method for obtaining a subjective estimate of the delay time distribution used in maintenance modelling are discussed and a brief literature survey on assessing subjective probability and expert judgment in decision making is presented. Based upon the results of the survey and experience in conducting a subjective estimating procedure for the delay time distribution, a revised procedure and method for obtaining the subjective delay time estimate is proposed, and the models for combining experts' opinions and updating the delay time distribution are also discussed. Case examples are given in the paper to demonstrate the methodology.
    European Journal of Operational Research 02/1997; · 2.04 Impact Factor
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    ABSTRACT: The paper develops a replacement action decision aid for a key furnace component subject to condition monitoring. A state space model is used to predict the erosion condition of the inductors in an induction furnace in which a measure of the conductance ratio (CR) is used to indirectly assess the relative condition of the inductors, and to guide replacement decisions. This study seeks to improve on this decision process by establishing the relationship between CR and the erosion condition of the inductors. To establish such a relationship, a state space model has been established and the system parameters estimated from CR data. A replacement cost model to balance at any time costly replacements with possible catastrophic failure is also proposed based upon the predicted probability of inductor erosion conditional upon all available information. The well known Kalman filter is employed to derive the predicted and updated probability of inductor erosion level conditional upon CR data to date. This is the first time the condition monitoring decision process has been modelled for real plant based upon filtering theory. The model fits the data well, gives a sensible answer to the actual problem, and is transferable to other condition monitoring contexts. Possible extensions are discussed in the paper.
    European Journal of Operational Research 01/1997; · 2.04 Impact Factor
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    ABSTRACT: In this paper we present a study carried out for a copper products manufacturing company, developing and applying the delay-time modelling technique to model and thus optimize preventive maintenance (PM) of the plant. A key machine in the plant is used to illustrate the modelling process and management reaction. The parameter values of the process by which faults arise and of the delay-time distribution are estimated from maintenance record data of failures and faults found at PM, using the method of maximum likelihood. A test of the model fit to data is carried out. Based upon the estimated model parameters and the failure delay time, an inspection model is proposed to describe the relationship between the total downtime and the PM interval.
  • A.H. Christer, W. Wang
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    ABSTRACT: This paper addresses the problem of condition monitoring of a component which has available a measure of condition called wear. Wear accumulates over time and monitoring inspections are performed at chosen times to monitor and measure the cumulative wear. If past measurements of wear are available up to the present, and the component is still active, the decision problem is to choose an appropriate time for the next inspection based upon the condition information obtained to date. A simple model which minimizes the expected cost per unit time over the time interval between the current inspection and the next inspection time is derived, and numerical examples are given to demonstrate the solution method.
    European Journal of Operational Research 01/1995; · 2.04 Impact Factor