Andrew K. S. Jardine

Measuring maintenance performance : a holistic approach

Performance measures should be linked to an organization’s strategy in order to provide useful information for making effective decisions and shaping desirable employee behaviour. The pitfalls relating to the indiscriminate use of common maintenance performance indicators are discussed in this paper. It also reviews four approaches to maintenance performance measures. The value‐based performance measure evaluates the impact of maintenance activities on the future value of the organization. The Balanced Scorecard (BSC) provides a framework for translating strategy into operational measures that collectively capture the critical requirements for sustaining the organization’s success. System audits are the tool for measuring organizational culture, which in turn determines the appropriate approach to the organization of maintenance functions. The operational efficiency of an organization’s maintenance function can be benchmarked with those of its counterparts in other organizations by using Data Envelopment Analysis (DEA). Among these approaches, the one which builds on the BSC embraces the design principles of a good performance measurement system. To smooth the adoption of the BSC approach to managing maintenance operations, a related research agenda is proposed in the concluding section.

Optimal Replacement In The Proportional Hazards Model

AbstractIn this paper, we examine a replacement problem for a system subject to stochastic deterioration. Upon failure the system must be replaced by a new one and a failure cost is incurred. If the systemis replaced before failure a smaller cost is incurred. The failure of the system depends both on its age and also on values of a diagnostic stochastic process observable at discrete points of time. Cox’s proportional hazards model is used to describe the failure rate of the system. We consider the problem of specifying a replacement rule which minimizes the long-run expected average cost per unit time. The form of the optimal replacement policy is found and an algorithm based on a recursive computational procedure is presented which can be used to obtain the optimal policy and the optimal expected average cost.

A Control-Limit Policy And Software For Condition-Based Maintenance Optimization

Abstract The focus of the paper is the optimization of condition-based maintenance decisions within the contexts of physical asset management. In particular, the analysis of a preventive replacement policy of the control-limit type for a deteriorating system subject to inspections at discrete points of time is presented. Cox’s PHM with a Weibull baseline hazard function and time dependent stochastic covariates is used to describe the failure rate of the system. The methods of estimating model parameters and the calculation of the optimal policy are given. The structure of the decision-making software EXAKT is presented. Experience with collecting, preprocessing and using real oil and vibration data is reported.

Optimal component replacement decisions using vibration monitoring and the proportional-hazards model

This paper describes a case study in which the Weibull proportional-hazards model is used to determine the optimal replacement policy for a critical item which is subject to vibration monitoring. Such an approach has been used to date in the context of monitoring through oil debris analysis, and this approach is extended in this paper to the vibration monitoring context. The Weibull proportional-hazards model is reviewed along with the software EXAKT used for optimization. In particular the case considers condition-based maintenance for circulating pumps in a coal wash plant that is part of the SASOL petrochemical company. The condition-based maintenance policy recommended in this study is based on histories collected over a period of 2 years, and is compared with current practice. The policy is validated using data that arose from subsequent operation of the plant.

A practical procedure for the selection of time-to-failure models based on the assessment of trends in maintenance data

Abstract Many times, reliability studies rely on false premises such as independent and identically distributed time between failures assumption (renewal process). This can lead to erroneous model selection for the time to failure of a particular component or system, which can in turn lead to wrong conclusions and decisions. A strong statistical focus, a lack of a systematic approach and sometimes inadequate theoretical background seem to have made it difficult for maintenance analysts to adopt the necessary stage of data testing before the selection of a suitable model. In this paper, a framework for model selection to represent the failure process for a component or system is presented, based on a review of available trend tests. The paper focuses only on single-time-variable models and is primarily directed to analysts responsible for reliability analyses in an industrial maintenance environment. The model selection framework is directed towards the discrimination between the use of statistical distributions to represent the time to failure (“renewal approach”); and the use of stochastic point processes (“repairable systems approach”), when there may be the presence of system ageing or reliability growth. An illustrative example based on failure data from a fleet of backhoes is included.

Optimizing condition‐based maintenance decisions for equipment subject to vibration monitoring

The paper reports the development of an optimal maintenance program based on vibration monitoring of critical bearings on machinery in the food processing industry. Statistical analysis of vibration data is undertaken using the software package EXAKT to establish the key vibration signals that are necessary for risk estimation. Once the risk curve is identified using a proportional hazards model, cost data are then blended with risk to identify the optimal maintenance program. The structure of the decision making software EXAKT is also presented. Concludes that perhaps the most important benefit of the study was the realization by maintenance management that it is possible to identify key measurements for examination at the time of vibration monitoring – thus possibly saving on inspection costs.

Optimizing a mine haul truck wheel motors’ condition monitoring program Use of proportional hazards modeling

Discusses work completed at Cardinal River Coals in Canada to improve the existing oil analysis condition monitoring program being undertaken for wheel motors. Oil analysis results from a fleet of 55 haul truck wheel motors were analyzed along with their respective failures and repairs over a nine‐year period. Detailed data cleaning procedures were applied to prepare data for modeling. In addition, definitions of failure and suspension were clarified depending on equipment condition at replacement. Using the proportional hazards model approach, the key condition variables relating to failures were found from among the 19 elements monitored, plus sediment and viscosity. Those key variables were then incorporated into a decision model that provided an unambiguous and optimal recommendation on whether to continue operating a wheel motor or to remove it for overhaul on the basis of data obtained from an oil sample. Wheel motor failure implied extensive planetary gear or sun gear damage necessitating the replacement of one or more major internal components in a general overhaul. The decision model, when triggered by incoming data, provided both a recommendation based on an optimal decision policy as well as an estimate of the unit’s remaining useful life. By optimizing the times of repair as a function both of age and condition data a 20‐30 percent potential savings in overhaul costs over existing practice was identified.

Periodic inspection optimization model for a complex repairable system

This paper proposes a model to find the optimal periodic inspection interval on a finite time horizon for a complex repairable system. In general, it may be assumed that components of the system are subject to soft or hard failures, with minimal repairs. Hard failures are either self-announcing or the system stops when they take place and they are fixed instantaneously. Soft failures are unrevealed and can be detected only at scheduled inspections but they do not stop the system from functioning. In this paper we consider a simple policy where soft failures are detected and fixed only at planned inspections, but not at moments of hard failures. One version of the model takes into account the elapsed times from soft failures to their detection. The other version of the model considers a threshold for the total number of soft failures. A combined model is also proposed to incorporate both threshold and elapsed times. A recursive procedure is developed to calculate probabilities of failures in every interval, and expected downtimes. Numerical examples of calculation of optimal inspection frequencies are given. The data used in the examples are adapted from a hospitals maintenance data for a general infusion pump.

Optimal replacement policy and the structure of software for condition‐based maintenance

States that the concept of condition‐based maintenance (CBM) has been widely accepted in practice since it enables maintenance decisions to be made based on the current state of equipment. Existing CBM methods, however, mainly rely on the inspector’s experience to interpret data on the state of equipment, and this interpretation is not always reliable. Aims to present a preventive maintenance policy based on inspections and a proportional hazards modelling approach with time‐dependent covariates to analyse failure‐time data statistically. Presents the structure of the software, currently under develop‐ ment and supported by the CBM Project Consortium.

A note on optimal replacement policy under general repair

Abstract A replacement model with general repair which brings the state of the system to a certain better state is considered. The form of the optimal replacement policy is found and the expected average cost per unit time is derived which can be minimized to obtain the optimal replacement time. Numerical examples are provided.