scholarly journals Optimizing preventive maintenance policy: A data-driven application for a light rail braking system

Author(s):  
Francesco Corman ◽  
Sander Kraijema ◽  
Milinko Godjevac ◽  
Gabriel Lodewijks

This article presents a case study determining the optimal preventive maintenance policy for a light rail rolling stock system in terms of reliability, availability, and maintenance costs. The maintenance policy defines one of the three predefined preventive maintenance actions at fixed time-based intervals for each of the subsystems of the braking system. Based on work, maintenance, and failure data, we model the reliability degradation of the system and its subsystems under the current maintenance policy by a Weibull distribution. We then analytically determine the relation between reliability, availability, and maintenance costs. We validate the model against recorded reliability and availability and get further insights by a dedicated sensitivity analysis. The model is then used in a sequential optimization framework determining preventive maintenance intervals to improve on the key performance indicators. We show the potential of data-driven modelling to determine optimal maintenance policy: same system availability and reliability can be achieved with 30% maintenance cost reduction, by prolonging the intervals and re-grouping maintenance actions.

2018 ◽  
Vol 204 ◽  
pp. 02016
Author(s):  
Moh. Jufriyanto ◽  
Nani Kurniati ◽  
Ade Supriatna

The needs of the consumers about the functionality of a product and increase maintenance costs of equipment caused the prices of products and treatments to be expensive. Therefore, the company considers the lease rather than buy it. Leasing provides interesting strategy when dealing with expensive equipment. Policy maintenance that is done to the product that has decreased performance. Minimum repair done to fix failed equipment in order to return to operational condition, while imperfect preventive maintenance to improve the operational conditions of the equipment to avoid failure. Time duration for a minimum repair neglected. The lessor will charge a penalty (penalty cost) if the lease equipment failure. Mathematical model built for the minimization cost of maintenance policy. In the final part, the numerical experiment are given to show the maintenance policy taking into account the rate of usage (usage rate) by knowing the minimization the resulting costs.


Author(s):  
Takao Ota ◽  
Hiroyuki Kawamura ◽  
Yoshiharu Matsumi ◽  
Junji Koyanagi ◽  
Takashi Satow

The infrastructures are required to keep a certain level of performance during the duration of service. Because the performance of the infrastructures including harbor and coastal structures deteriorates due to aging and damage that is caused by the action of external forces, it is necessary to perform appropriate maintenance. Satow et al. (2009) proposed a mathematical model for the preventive maintenance of wave dissipating blocks based on the method of the reliability engineering. They also derived the expected maintenance cost over the in service period and the optimal preventive maintenance policy. In this study, the optimal threshold for preventive maintenance to minimize the expected maintenance cost is determined for the wave dissipating blocks covering caisson breakwater by using the above model.


Author(s):  
Inderjeet Singh ◽  
Elmira Popova ◽  
Ernie Kee

We design an optimal preventive maintenance policy for a system of N items that minimizes the total expected maintenance cost. We assume that the budget for preventive maintenance is limited and constrained. The problem has a finite time horizon and we consider constant inter-preventive maintenance times for every item. The resulting nonlinear optimization problem is reformulated as a binary integer program and computation results are presented on a real data set from South Texas Project Nuclear Operating Company in Bay City, Texas, USA.


Rekayasa ◽  
2017 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Cahyo Purnomo Prasetyo

<p>Penelitian ini bertujuan untuk menentukan kebijakan perawatan optimal yang dapat mengurangi biaya perbaikan (repair cost) dan biaya konsekwensi operasional (operational consequence cost). Metode yang diterapkan pada penelitian ini adalah Reliability Centered Maintenance (RCM) II. Penelitian ini difokuskan pada mesin Cane Cutter 1 dan 2 dengan pertimbangan beberapa aspek yaitu : pengaruh kegagalan terhadap pencapaian target produksi, resiko keselamatan kerja dan biaya perawatan yang akan ditimbulkan. Dari hasil penelitian dapat diketahui bahwa komponen kritis pada mesin Cane Cutter 1 dan 2 adalah : Pisau dan Baut Pisau. Perawatan yang dilakukan untuk mengantisipasi dan mengatasi kegagalan yang terjadi pada komponen mesin tersebut adalah proactive task yang meliputi : scheduled restoration task dan scheduled discard task. Rata-rata penurunan biaya perawatan total yang didapatkan dengan mengurangkan ‘biaya total pada interval perawatan awal’ dan ‘biaya total pada interval perawatan optimal’ adalah 14,82 %.</p><p>Kata Kunci: cane cutter, downtime, pabrik gula.</p><p><strong> </strong></p><p><strong>ABSTRACT</strong></p><p><em>This research aims to determine the optimal maintenance policy which could reduce repair cost and operational consequence cost. The methods which applied in this research is Reliability Centered Maintenance (RCM) II. This research focuses on Cane Cutter 1 and 2 machines by considering several aspects, such as and effect of any failure on production target achievement, work safety risk and maintenance cost which might be caused by the critical condition. The result showed that some critical components at the Cane Cutter 1 and 2 machines were : Blade and Blade Bolt. The maintenance which could be done to anticipate and deal with any failure occurring in the machine components was called proactive task comprising : scheduled restoration task and scheduled discard task. The average reduction in total maintenance costs which was obtained by subtracting ‘total costs at initial maintenance interval’ and ‘total costs at optimal maintenance interval’ amounted to 14,82 %.</em></p><p><em>Keywords: cane cutter, downtime, sugar factory</em></p>


Author(s):  
David Kimera ◽  
Fillemon Nduvu Nangolo

This article proposes a stochastic technique for determining the optimal maintenance policy for marine mechanical systems. The optimal maintenance policy output includes the average maintenance cost rate, maintenance interval and the performance thresholds for the three marine mechanical system classifications. The purpose of this study is to optimize maintenance, maintenance interval and performance thresholds based on maintenance and reliability data of the marine mechanical systems. Performance threshold and maintenance interval are used as the decision variables to determine the optimal maintenance policy. A stochastic model based on probability analysis is developed to trigger the maintenance action for mechanical systems. The model is later coded in MATLAB. Maintenance and failure data for a marine vehicle were statistically fitted using ReliaSoft, from which a three-parameter Weibull distribution best fitted all the mechanical system classifications. Model inputs were based on both the maintenance data and expert knowledge of the maintenance crew. Based on a 20-year marine vehicle life span, the optimal maintenance costs for plant and machinery are relatively the same. The model predicted annual total maintenance cost of US$183,029.24 is 11.11% more than the maintenance cost derived from experts’ threshold of US$164,726. Marine vehicle machinery presents a higher maintenance interval of 3.23 years compared to 2.92 years for marine vehicle plants. It was observed that for the performance thresholds greater than 84.54%, there is an insignificant difference between the plant and machinery maintenance costs. Sensitivity analysis results suggest there is little justification that changing maintenance costs will have an impact on the performance threshold [Formula: see text] and maintenance interval [Formula: see text]. A maintenance interval of 3 years results in a lower total annual maintenance cost deviation of 2.66% from the optimal total annual maintenance cost.


2021 ◽  
Vol 18 (1) ◽  
pp. 43-50
Author(s):  
M.C. Nwachukwu ◽  
J.C. Agunwamba ◽  
B.C. Okoro ◽  
C.N. Mama

A study optimising maintenance cost of water borehole schemes in South Eastern states of Nigeria (Abia, Anambra, Ebonyi, Enugu and Imo States) was carried out. Data was collected from 260 boreholes spread across all local government areas in the states. Optimisation results showed that for boreholes (submersible pumps) pumping once per day, the optimal preventive maintenance frequency and resulting savings in cost are 2 and ₦521,076 for Abia; 2 and ₦783,963 for Anambra; 2 and ₦458,242 for Ebonyi; 2 and ₦740,964 for Enugu; 2 and ₦605,187 Imo. For boreholes pumping twice per day, the optimal preventive maintenance frequency and resulting savings in cost are 5 and ₦1,896,301 for Abia; 4 and ₦3,692,655 for Anambra; 5 and ₦786,913 for Ebonyi; 4 and ₦4,187,161 for Enugu; 4 and ₦2,477,609 for Imo; and for boreholes pumping thrice per day; 8 and ₦2,798,330 for Abia; 7 and ₦8,372,862 for Anambra; 7 and ₦6,485,293 for Ebonyi; 10 and ₦4,014,240 for Enugu; 10 and ₦6,021,503 for Imo; with no downtime as opposed to the wasteful current practice of no preventive maintenance with downtime of up to 12 months or more. As a recommendation for a borehole scheme, there should be a check on the type of submersible pump and generator capacity as the choice made directly affects the total operational cost.


Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6082
Author(s):  
Vincent F. Yu ◽  
Thi Huynh Anh Le ◽  
Tai-Sheng Su ◽  
Shih-Wei Lin

Employing maintenance threshold plays a critical step in determining an optimal maintenance policy for an offshore wind system to reduce maintenance costs while increasing system reliability. Considering the limited works on this topic, we propose a two-stage procedure to determine the optimal maintenance thresholds for multiple components of an offshore wind power system in order to minimize maintenance costs while achieving the highest possible system reliability. First, using genetic algorithms, a dynamic strategy is developed to determine the maintenance thresholds of individual components where the cost of maintenance and the rate of failure are critical. Then, fuzzy multi-objective programming is applied to find the system’s optimal maintenance threshold considering all components. A variety of factors including weather conditions, system reliability, power generation losses, and electricity market price are carefully considered to enhance the system’s reliability and reduce the costs of maintenance. When maintenance threshold results are compared, component-wise versus system-wise, an average system savings of 1.19% for maintenance cost is obtained while the system reliability is increased by 1.62% on average.


2017 ◽  
Vol 9 (1) ◽  
pp. 32-48 ◽  
Author(s):  
Rima Oudjedi Damerdji ◽  
Myriam Noureddine

The definition of an appropriated maintenance policy appears essential to avoid the system failures and ensure its optimal operation, while taking into account the criteria of availability and costs. This article deals with a maintenance decision-making for a system subject to two competing maintenance actions, corrective and preventive maintenance. To define this situation of dependent competing risks, the Alert Delay model seems well suited because it involves the notion of a delivered alert before system failure in order to perform preventive maintenance. This paper proposes an approach including both an extension of the Alert Delay model where the considered system follows an exponential distribution, and the total maintenance cost assessment of the system. These two concepts provide an aid decision-making to select the optimal maintenance policy based on the minimal cost. The proposed approach is validated in a computer system localized in a real industrial enterprise.


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