Maintenance cost assessment for heterogeneous multi-component systems incorporating perfect inspections and waiting time to maintenance

2021 ◽  
pp. 1748006X2110388
Author(s):  
Lucía Bautista ◽  
Inma T Castro ◽  
Luis Landesa
Keyword(s):  
Heuristic Algorithms ◽  
Cost Model ◽  
System Level ◽  
Maintenance Cost ◽  
Maintenance Policy ◽  
Industrial Systems ◽  
Inspection Strategy ◽  
Optimal Maintenance ◽  
Systems Maintenance ◽  
Safety Constraint

Most existing research about complex systems maintenance assumes they consist of the same type of components. However, systems can be assembled with heterogeneous components (for example degrading and non-degrading components) that require different maintenance actions. Since industrial systems become more and more complex, more research about the maintenance of systems with heterogeneous components is needed. For this reason, in this paper, a system consisting of two groups of components: degrading and non-degrading components is analyzed. The main novelty of this paper is the evaluation of a maintenance policy at system-level coordinating condition-based maintenance for the degrading components, delay time to the maintenance and an inspection strategy for this heterogeneous system. To that end, an analytic cost model is built using the semi-regenerative processes theory. Furthermore, a safety constraint related to the reliability of the degrading components is imposed. To find the optimal maintenance strategy, meta-heuristic algorithms are used.

Availability analysis and maintenance optimization for multiple failure mode systems considering imperfect repair

2021 ◽  
pp. 1748006X2110127
Author(s):  
Qingan Qiu ◽  
Baoliang Liu ◽  
Cong Lin ◽  
Jingjing Wang
Keyword(s):  
Failure Modes ◽  
Maintenance Cost ◽  
Imperfect Maintenance ◽  
Maintenance Policy ◽  
Cost Rate ◽  
Availability Analysis ◽  
Long Run ◽  
Optimal Maintenance ◽  
Maintenance Policies ◽  
Periodic Inspections

This paper studies the availability and optimal maintenance policies for systems subject to competing failure modes under continuous and periodic inspections. The repair time distribution and maintenance cost are both dependent on the failure modes. We investigate the instantaneous availability and the steady state availability of the system maintained through several imperfect repairs before a replacement is allowed. Analytical expressions for system availability under continuous and periodic inspections are derived respectively. The availability models are then utilized to obtain the optimal inspection and imperfect maintenance policy that minimizes the average long-run cost rate. A numerical example for Remote Power Feeding System is presented to demonstrate the application of the developed approach.

scholarly journals Evaluasi Manajemen Perawatan dengan Metode Reliability Centered Maintenance (RCM) II pada Mesin Cane Cutter 1 dan 2 di Stasiun Gilingan PG Meritjan - Kediri

Rekayasa ◽  
2017 ◽  
Vol 10 (2) ◽  
pp. 99
Author(s):  
Cahyo Purnomo Prasetyo
Keyword(s):  
Maintenance Cost ◽  
Repair Cost ◽  
Maintenance Policy ◽  
Work Safety ◽  
Maintenance Costs ◽  
Reliability Centered Maintenance ◽  
Optimal Maintenance ◽  
Critical Components ◽  
Optimal Maintenance Policy ◽  
Production Target

<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>

Reliability and maintenance modeling for competing failures with intermission considered

2019 ◽  
Vol 233 (5) ◽  
pp. 898-907
Author(s):  
Jingyi Liu ◽  
Yugang Zhang ◽  
Bifeng Song
Keyword(s):  
System Reliability ◽  
Rest Period ◽  
Maintenance Cost ◽  
Reliability Model ◽  
Maintenance Policy ◽  
General Applicability ◽  
Industrial Systems ◽  
Soft Failure ◽  
Long Run ◽  
Degradation Failure

There are many industrial systems experiencing multiple dependent competing failure processes, in detail degradation failure (soft failure) and catastrophic failure (hard failure). Earlier research studied failure behaviors and system reliability during operational period, but did not consider the intermission period. Some industrial systems are not always operating continuously while with intermissions or rest period. The degradation and random shock processes are different between operating period and intermissions, which caused it more challenging and complicated to establish reliability model. In this article, a new reliability model for multiple dependent competing failure processes is developed with intermission considered. The system reliability can be analyzed based on the proposed model more practically. Besides, a preventive replacement maintenance policy is studied by minimizing the average long-run maintenance cost with intermission periods considered. Finally, the availability and general applicability of presented model are demonstrated by a case in different parameter settings.

Online Analytics Framework of Sensor-Driven Prognosis and Opportunistic Maintenance for Mass Customization

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Tangbin Xia ◽  
Xiaolei Fang ◽  
Nagi Gebraeel ◽  
Lifeng Xi ◽  
Ershun Pan
Keyword(s):  
Real Time ◽  
Mass Customization ◽  
Maintenance Scheduling ◽  
System Level ◽  
Maintenance Cost ◽  
Time To Failure ◽  
Maintenance Policy ◽  
Opportunistic Maintenance ◽  
Product Demand ◽  
System Breakdown

In mass customization, a manufacturing line is required to be kept in reliable operation to handle product demand volatility and potential machine degradations. Recent advances in data acquisition and processing allow for effective maintenance scheduling. This paper presents a systematic framework that integrates a sensor-driven prognostic method and an opportunistic maintenance policy. The prognostic method uses degradation signals of each individual machine to predict and update its time-to-failure (TTF) distributions in real time. Then, system-level opportunistic maintenance optimizations are dynamically made according to real-time TTF distributions and variable product orders. The online analytics framework is demonstrated through the case study based on the collected reliability information from a production line of engine crankshaft. The results can effectively prove that the real-time degradation updating and the opportunistic maintenance scheduling can efficiently reduce maintenance cost, avoid system breakdown, and ensure product quality. Furthermore, this framework can be applied not only in an automobile line but also for a broader range of manufacturing lines in mass customization.

scholarly journals Optimal Maintenance Probabilities and Preventive Replacement Maintenance Policy for Photocopy Machines

2021 ◽  
Author(s):  
Nse Udoh ◽  
Akaninyene Udom ◽  
Fredrick Ohaegbunem
Keyword(s):  
Minimum Cost ◽  
Maintenance Cost ◽  
Logistic Distribution ◽  
Maintenance Policy ◽  
Preventive Replacement ◽  
Maintenance Time ◽  
Replacement Model ◽  
Cumulative Function ◽  
Optimal Maintenance ◽  
Preventive Maintenance Policy

The need for suitable replacement policies are essential to minimize down time, maintenance cost and maximize the availability and reliability of equipment. On this premise, this work models the failure rate of Photocopy machines and obtain its optimal preventive maintenance policy that would prevent damage and its attendant losses to both users and end-product consumers. The failure distribution of the machine was shown to follow the Log-Logistic distribution with shape parameter, αˆ=1.723339368 and scale parameter, βˆ=763.9219635. Optimal probabilities of the distribution were obtained and utilized in both the cumulative failure function and cumulative hazard function-based replacement models to formulate a replacement maintenance policy for the machine. The failure cumulative function-based replacement model was found to be a better model which yields optimal replacement maintenance time of 166 hours at a minimum cost of 113 Naira for maintaining the machine per cycle time with 96% availability, 94% reliability and 0.07% chance of failure occurrence in the machine.

Maintenance optimization for marine mechanical systems

2019 ◽  
Vol 234 (2) ◽  
pp. 446-462
Author(s):  
David Kimera ◽  
Fillemon Nduvu Nangolo
Keyword(s):  
Mechanical System ◽  
Expert Knowledge ◽  
Mechanical Systems ◽  
Maintenance Cost ◽  
Maintenance Policy ◽  
Maintenance Costs ◽  
Cost Rate ◽  
Optimal Maintenance ◽  
Optimal Maintenance Policy ◽  
Marine Vehicle

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.

Optimal track geometry maintenance limits using machine learning: A case study

2020 ◽  
pp. 095440972097009
Author(s):  
Ahmad Kasraei ◽  
Jabbar Ali Zakeri ◽  
Arash Bakhtiary
Keyword(s):  
Clustering Algorithm ◽  
Cost Model ◽  
Maintenance Cost ◽  
Maintenance Costs ◽  
Corrective Maintenance ◽  
Track Geometry ◽  
Penalty Costs ◽  
Optimal Maintenance ◽  
Maintenance Activities ◽  
Maintenance Model

The aim of this study has been to determine the optimal maintenance limits for one of the main railway lines in Iran in such a way that the total maintenance costs are minimized. For this purpose, a cost model has been developed by considering costs related to preventive maintenance activities, corrective maintenance activities, inspection, and a penalty costs associated with exceeding corrective maintenance limit. Standard deviation of longitudinal level was used to measure the quality of track geometry. In order to reduce the level of uncertainty in the maintenance model, K-means clustering algorithm was used to classify track sections with most similarity. Then, a linear function was used for each cluster to model the degradation of track sections. Monte Carlo technique was used to simulate track geometry behavior and determine the optimal maintenance limit which minimizes the total maintenance costs. The results of this paper show that setting an optimal limit can affect total annual maintenance cost about 27 to 57 percent.

scholarly journals Optimizing Maintenance Planning in the Production Industry Using the Markovian Approach

2012 ◽  
Vol 9 (2) ◽  
pp. 46
Author(s):  
B Kareem ◽  
HA Owolabi
Keyword(s):  
Cost Minimization ◽  
Production Equipment ◽  
Maintenance Cost ◽  
Maintenance Planning ◽  
Maintenance Policy ◽  
Transition Matrices ◽  
Operational Life ◽  
Optimal Maintenance ◽  
Markovian Approach ◽  
Optimal Maintenance Policy

Maintenance is an essential activity in every manufacturing establishment, as manufacturing effectiveness counts on the functionality of production equipment and machinery in terms of their productivity and operational life. Maintenance cost minimization can be achieved by adopting an appropriate maintenance planning policy. This paper applies the Markovian approach to maintenance planning decision, thereby generating optimal maintenance policy from the identified alternatives over a specified period of time. Markov chains, transition matrices, decision processes, and dynamic programming models were formulated for the decision problem related to maintenance operations of a cable production company. Preventive and corrective maintenance data based on workloads and costs, were collected from the company and utilized in this study. The result showed variability in the choice of optimal maintenance policy that was adopted in the case study. Post optimality analysis of the process buttressed the claim. The proposed approach is promising for solving the maintenance scheduling decision problems of the company. 

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