Modeling Failure Process and Quantifying the Effects of Multiple Types of Preventive Maintenance for a Repairable System

Said, Uthman; Taghipour, Sharareh

doi:10.1002/qre.2088

Cited by 12 publications

(4 citation statements)

References 31 publications

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“…Other models were designed, generally with an estimation procedure, for example by Dauxois and Maalouf [6], Dauxois et al [7], Dijoux et al [8], Doyen et al [9], and Peng et al [10]. Some models also include preventive maintenances, as those exposed by Peng et al [11], Said and Taghipour [12] and Salles et al [13]. Preventive maintenances are carried out without the occurence of a failure.…”

Section: Contextmentioning

confidence: 99%

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Brenière

Doyen

Bérenguer

2020

Reliability Engineering & System Safety

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A repairable system faces some failures and imperfect maintenances throughout its lifetime. If several identical and independent systems are considered together, some differences may arise between the systems, such as the geographical location or the maintenance team for example, which are constant information, or the weather conditions, which vary with time. This observed heterogeneity will influence more or less the failure process. In this paper, we include these data in a generalized virtual age model with the use of covariates. Then we estimate simultaneously the effect of the maintenances, that of the covariates, and the intrinsic wear of the systems. We also propose two simulation methods as well as a numerical estimation procedure. Then we assess the quality of the estimation of the parameters with a thorough simulation study.

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Section: Contextmentioning

confidence: 99%

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Brenière

Doyen

Bérenguer

2020

Reliability Engineering & System Safety

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“…Doyen (Nasr, Gasmi, & Sayadi, 2013). Said and Taghipour further expanded this by considering three maintenance types for PM events and minimal repair for CM events (Said & Taghipour, 2016). They derive the likelihood function for estimating the parameters of the failure process and the effects of preventive maintenance, as well as provide the conditional reliability and the expected number of failures between two consecutive PM types (Said & Taghipour, 2016).…”

Section: Science and Technologymentioning

confidence: 99%

“…Said and Taghipour further expanded this by considering three maintenance types for PM events and minimal repair for CM events (Said & Taghipour, 2016). They derive the likelihood function for estimating the parameters of the failure process and the effects of preventive maintenance, as well as provide the conditional reliability and the expected number of failures between two consecutive PM types (Said & Taghipour, 2016). Other methods included using feed-forward artificial neural networks (ANN) on condition monitoring data with asset targets' being asset survival probabilities estimated by Kaplan-Meier (KM) and degradation failure probability density function (PDF) estimator (Heng, et al, 2009).…”

Section: Science and Technologymentioning

confidence: 99%

An algorithm for estimating the effect of maintenance on aggregated covariates with application to railway switch point machines

Babishin¹,

Taghipour²

2019

Eksploatacja i Niezawodność – Maintenance and Reliability

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We propose an algorithm for estimating the effectiveness of maintenance on both age and health of a system. One of the main contributions is the concept of virtual health of the device. It is assumed that failures follow a nonhomogeneous Poisson process (NHPP) and covariates follow the proportional hazards model (PHM). In particular, the effect of maintenance on device's age is estimated using the Weibull hazard function, while the effect on device's health and covariates associated with condition-based monitoring (CBM) is estimated using the Cox hazard function. We show that the maintenance effect on the health indicator (HI) and the virtual HI can be expressed in terms of the Kalman filter concepts. The HI is calculated from Mahalanobis distance between the current and the baseline condition monitoring data. The effect of maintenance on both age and health is also estimated. The algorithm is applied to the case of railway point machines. Preventive and corrective types of maintenance are modelled as different maintenance effect parameters. Using condition monitoring data, the HI is calculated as a scaled Mahalanobis distance. We derive reliability and likelihood functions and find the least squares estimates (LSE) of all relevant parameters, maintenance effect estimates on time and HI, as well as the remaining useful life (RUL).

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“…Yildirim [20] proposed an integrated framework for wind farm maintenance that combined predictive analytics methodology with an optimization model considering economic dependence and stochastic dependence. Shahraki [21] proposed a selective maintenance optimization method for complex systems composed of stochastically dependent components, which captures the two-way interaction between components through system performance rates. Although many models of maintenance dependence have been proposed, almost all existing studies only focus on one or two types of maintenance dependence, which cannot perfectly reflect the impact of all three types of dependence.…”

Section: Introductionmentioning

confidence: 99%

Group maintenance strategy of CNC machine tools considering three kinds of maintenance dependence and its optimization

Sun

Chen

et al. 2021

Int J Adv Manuf Technol

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Unreasonable maintenance strategies will increase maintenance costs and reduce the efficiency of CNC (Computer Numerical Control) machine tools. Therefore, not only the degradation state of components but also their coupling effect should be considered to obtain a scientific and reasonable system-level maintenance strategy because of the dependence among different components of CNC machine tools. This study proposes a group maintenance strategy of CNC machine tools considering economic, structural, and stochastic dependence among critical components and optimizes the group maintenance strategy. The model of group maintenance of CNC machine tools is composed of four sub-models: sub-model of component degression, sub-model of group maintenance decisions, sub-model of imperfect maintenance, sub-model of maintenance cost. Utilizing the model of group maintenance of CNC machine tools, the time, objective, and measures of maintenance can be decided according to the degression state and failures. And then, the cost of each maintenance can be calculated. In the group maintenance model, economic dependence and structural dependence among components are quantified by cost, while stochastic dependence is quantified by failure intensity. On that basis, the Monte Carlo method is used to simulate the machine tool operation process and the long-term maintenance cost of CNC machine tools corresponding to a certain failure intensity threshold is calculated. Finally, Genetic Algorithm is used to optimize the failure intensity thresholds of preventive and group maintenance. A numerical example verifies the effectiveness of the proposed optimization method for group maintenance strategy.

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Modeling Failure Process and Quantifying the Effects of Multiple Types of Preventive Maintenance for a Repairable System

Cited by 12 publications

References 31 publications

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

An algorithm for estimating the effect of maintenance on aggregated covariates with application to railway switch point machines

Group maintenance strategy of CNC machine tools considering three kinds of maintenance dependence and its optimization

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