The purpose of this paper is the simultaneous determination of optimal replacement threshold and inspection scheme for a system within condition-based maintenance (CBM) framework.
Abstract
Purpose
The purpose of this paper is the simultaneous determination of optimal replacement threshold and inspection scheme for a system within condition-based maintenance (CBM) framework.
Design/methodology/approach
A proportional hazards model (PHM) is used for risk of failure and a Markovian process to model the system covariates. Total expected long-run cost (including replacement, inspection and downtime costs) is formulated in terms of replacement threshold and inspection scheme. Through an iterative procedure, for all different values of replacement thresholds, their associated optimal inspection scheme is determined using an effective search algorithm. By evaluating the corresponding costs, the optimal replacement threshold and its associated optimal inspection scheme are, then, identified.
Findings
The mathematical formulation, that takes into account all different costs, required for the simultaneous determination of optimal replacement threshold and optimal inspection scheme for an item subjected to CBM using PHM is provided. The proposed approach is compared against classical age policy and one state-of-the-art policy through a numerical example. The results show that the proposed approach outperforms other comparing policies.
Practical implications
In practical situations where CBM is implemented, inspections and downtime often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection scheme so that the CBM cost is minimized.
Originality/value
In most of the reported researches, it is often assumed that inspections have no cost and/or that the time for replacements (either preventive or at failure) is negligible. In the contrary, in this paper the author takes all cost factors including inspection costs, replacement time(s) and their associated downtime costs into account in the simultaneous determination of optimal replacement threshold and optimal inspection scheme.
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Zengqiang Jiang, Dragan Banjevic, Mingcheng E., Andrew Jardine and Qi Li
The purpose of this paper is to develop an approach for estimating the remaining useful life (RUL) of metropolitan train wheels considering measurement error.
Abstract
Purpose
The purpose of this paper is to develop an approach for estimating the remaining useful life (RUL) of metropolitan train wheels considering measurement error.
Design/methodology/approach
The paper proposes a wear model of a metropolitan train wheel based on a discrete state space model; the model considers the wheel’s stochastic degradation and measurement error simultaneously. The paper estimates the RUL on the basis of the estimated degradation state. Finally, it presents a case study to verify the proposed approach. The results indicate that the proposed method is superior to methods that do not consider measurement error and can improve the accuracy of the estimated RUL.
Findings
RUL estimation is a key issue in condition-based maintenance and prognostics and health management. With the rapid development of advanced sensor technologies and data acquisition facilities for the maintenance of metropolitan train wheels, condition monitoring (CM) is becoming more accurate and more affordable, creating the possibility of estimating the RUL of wheels using CM data. However, the measurements of the wheels, especially the wayside measurements, are not yet precise enough. On the other hand, few existing studies of the RUL estimation of train wheels consider measurement error.
Practical implications
The approach described in this paper will make the RUL estimation of metropolitan train wheels easier and more precise.
Originality/value
Hundreds of million yuan are wasted every year due to over re-profiling of rail wheels in China. The ability to precisely estimate RUL will reduce the number of re-profiling activities and achieve significant economic benefits. More generally, the paper could enrich the body of knowledge of RUL estimation for a slowly degrading system considering measurement error.
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Hamid Reza Golmakani and Morteza Pouresmaeeli
The purpose of this paper is to determine optimal replacement threshold and optimal inspection interval for an item subjected to condition-based maintenance (CBM). The primarily…
Abstract
Purpose
The purpose of this paper is to determine optimal replacement threshold and optimal inspection interval for an item subjected to condition-based maintenance (CBM). The primarily assumption is that the item's failure replacement cost depends on the item's degradation state at which failure occurs and/or the time the item fails. The cost of inspection is also taken into account.
Design/methodology/approach
The control limit replacement policy framework, already reported by some research referred to in this paper, is first extended to include the non-decreasing failure replacement cost assumption. Then, for alternative inspection intervals, replacement thresholds together with their associated total cost including the inspection cost are computed. By comparing the total costs, the optimal inspection interval and its corresponding optimal replacement threshold are simultaneously identified.
Findings
The mathematical formulation required for the determination of optimal replacement threshold and optimal inspection interval for an item subjected to CBM under the assumption of non-decreasing failure cost is provided.
Practical implications
In some practical situations where CBM is implemented, the failure replacement cost may depend on the time the failure happens and/or may depend on the system's degradation state. In addition, inspections often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection interval for the underlying system.
Originality/value
Using the approach proposed in this paper, one could obtain the optimal replacement threshold and the optimal inspection interval for a system subjected to CBM.
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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, D. Banjevic and V. Makis
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…
Abstract
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.
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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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A.K.S. Jardine, D. Banjevic, M. Wiseman, S. Buck and T. Joseph
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…
Abstract
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.
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A.K.S. Jardine, T. Joseph and D. Banjevic
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…
Abstract
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.
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Hanna Lo, Alireza Ghasemi, Claver Diallo and John Newhook
Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared…
Abstract
Purpose
Condition-based maintenance (CBM) has become a central maintenance approach because it performs more efficient diagnoses and prognoses based on equipment health condition compared to time-based methods. CBM models greatly inform maintenance decisions. This research examines three CBM fault prognostics models: logical analysis of data (LAD), artificial neural networks (ANNs) and proportional hazard models (PHM). A methodology, which involves data pre-processing, formulating the models and analyzing model outputs, is developed to apply and compare these models. The methodology is applied on NASA’s Turbofan Engine Degradation data set and the structural health monitoring (SHM) data set from a Nova Scotia Bridge. Results are evaluated using three metrics: error, half-life error and a cost score. This paper concludes that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably, and its predictions show much larger variance than the predictions from the other three methods. Based on these conclusions, the purpose of this paper is to provide recommendations on the appropriate situations in which to apply these three prognostics models.
Design/methodology/approach
LAD, ANNs and PHM methods are adopted to perform prognostics and to calculate the mean residual life (MRL) of eqipment using NASA’s Turbofan Engine Degradation data set and the SHM data set from a Nova Scotia Bridge. Statistical testing was used to evaluate the statistical differences between the approaches based on these metrics. By considering the differences in these metrics between the models, it was possible to draw conclusions about how the models perform in specific cases.
Findings
Results were evaluated using three metrics: error, half-life error and a cost score. It was concluded that the LAD and feedforward ANN models compares favorably to the PHM model. However, the feedback ANN does not compare favorably and its predictions show much larger variance than the predictions from the other three methods. Overall the models predict failure after it has already occurred (negative error) when the residual life is large and vice versa.
Practical implications
It was concluded that a good CBM prognostics model for practical implications can be determined based on three main considerations: accuracy, run time and data type. When accuracy is a main concern, as in the case where impacts of failure are large, LAD and feedforward neural network are preferred. The preference changes when run time is considered. If data can be easily collected and updating the model is performed often, the ANNs and LAD are preferred. On the other hand, if CM data are not easily obtainable and existing data are not representative of the population’s behavior, data type comes into play. In this case, PHM is preferred.
Originality/value
Previous research in the literature performed reviews of multiple independent studies on CBM techniques performed on different data sets. They concluded that it is typically harder to implement artificial intelligence models, because of difficulties in data procurement, but these approaches offer improved performance as compared to more traditional model-based and statistical approaches. In this research, the authors further investigate and compare the performance and results from two major artificial intelligence models, namely, ANNs and LAD, and one pioneer statistical model, PHM over the same two real life prognostics data sets. Such in-depth comparison and review of major CBM techniques was missing in current literature of CBM field.
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Ronghua Cai, Jiamei Yang, Xuemin Xu and Aiping Jiang
The purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers…
Abstract
Purpose
The purpose of this paper is to propose an improved multi-objective optimization model for the condition-based maintenance (CBM) of single-component systems which considers periodic imperfect maintenance and ecological factors.
Design/methodology/approach
Based on the application of non-periodic preventive CBM, two recursion models are built for the system: hazard rate and the environmental degradation factor. This paper also established an optimal multi-objective model with a normalization process. The multiple-attribute value theory is used to obtain the optimal preventive maintenance (PM) interval. The simulation and sensitivity analyses are applied to obtain further rules.
Findings
An increase in the number of the occurrences could shorten the duration of a maintenance cycle. The maintenance techniques and maintenance efficiency could be improved by increasing system availability, reducing cost rate and improving degraded condition.
Practical implications
In reality, a variety of environmental situations may occur subsequent to the operations of an advanced manufacturing system. This model could be applied in real cases to help the manufacturers better discover the optimal maintenance cycle with minimized cost and degraded condition of the environment, helping the corporations better fulfill their CSR as well.
Originality/value
Previous research on single-component condition-based predictive maintenance usually focused on the maintenance costs and availability of a system, while ignoring the possible pollution from system operations. This paper proposed a modified multi-objective optimization model considering environment influence which could more comprehensively analyze the factors affecting PM interval.