Dragan Banjevic

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.

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 multi-component and multi-failure mode inspection model based on the delay time concept

The delay time concept and the techniques developed for modelling and optimising plant inspection practices have been reported in many papers and case studies. For a system comprised of many components and subject to many different failure modes, one of the most convenient ways to model the inspection and failure processes is to use a stochastic point process for defect arrivals and a common delay time distribution for the duration between defect the arrival and failure of all defects. This is an approximation, but has been proven to be valid when the number of components is large. However, for a system with just a few key components and subject to few major failure modes, the approximation may be poor. In this paper, a model is developed to address this situation, where each component and failure mode is modelled individually and then pooled together to form the system inspection model. Since inspections are usually scheduled for the whole system rather than individual components, we then formulate the inspection model when the time to the next inspection from the point of a component failure renewal is random. This imposes some complication to the model, and an asymptotic solution was found. Simulation algorithms have also been proposed as a comparison to the analytical results. A numerical example is presented to demonstrate the model.

Using principal components in a proportional hazards model with applications in condition-based maintenance

This paper proposes the application of a principal components proportional hazards regression model in condition-based maintenance (CBM) optimization. The Cox proportional hazards model with time-dependent covariates is considered. Principal component analysis (PCA) can be applied to covariates (measurements) to reduce the number of variables included in the model, as well as to eliminate possible collinearity between the covariates. The main issues and problems in using the proposed methodology are discussed. PCA is applied to a simulated CBM data set and two real data sets obtained from industry: oil analysis data and vibration data. Reasonable results are obtained.

Optimization Models for Critical Spare Parts Inventories — A Reliability Approach

In industries characterized by heavy utilization of equipment and machinery, such as mining, oil & gas, utilities, transportation, adequate stockholding of critical spare parts becomes essential. Insufficient stocks affect overall performance of physical assets, as lack of spares may result in gross penalties, lower availability or increased operational risks. On the other hand, oversized inventories lead to inefficient use of capital and may imply severe expenditures. This paper presents various approaches for the determination of the optimal stock size, when the stock is composed of (i) non-repairable or (ii) repairable parts. The paper is focused on spares for relatively expensive, highly reliable components, rather than on fast-moving spare parts. Optimization criteria considered are minimization of costs, maximization of equipment availability, and the achievement of a desired stock reliability (probability that a spare part request will not be rejected because of the lack of spares in stock). For stock reliability, instantaneous and interval reliability calculations are considered. In addition, models directed to the estimation of the remaining life of a given stock of spare parts (at a certain stock reliability level) are introduced. The paper describes several models subject to practical industrial application, and presents case studies from utilities and mining to illustrate their use.

A decision optimization model for condition‐based maintenance

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.