scholarly journals Reliability-Based Opportunistic Maintenance Modeling for Multi-component Systems with Economic Dependence under Base Warranty

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Rongcai Wang ◽  
Zhonghua Cheng ◽  
Enzhi Dong ◽  
Chiming Guo ◽  
Liqing Rong
Keyword(s):  
Production System ◽  
Cost Optimization ◽  
Economic Losses ◽  
Maintenance Cost ◽  
Economic Dependence ◽  
Opportunistic Maintenance ◽  
Component Systems ◽  
Important Solution ◽  
Optimal Maintenance ◽  
Warranty Cost

Maintenance usually plays a key role in controlling a multi-component production system within normal operations. Furthermore, the failure of components in the production system will also cause large economic losses for users due to the shutdown. Meanwhile, manufacturers of the production system will be confronted with the challenges of the warranty cost. Therefore, it is of great significance to optimize the maintenance strategy to reduce the downtime and warranty cost of the system. Opportunistic maintenance (OM) is a quite important solution to reduce the maintenance cost and improve the system performance. This paper studies the OM problem for multi-component systems with economic dependence under base warranty (BW). The irregular imperfect preventive maintenance (PM) is performed to reduce the failure rate of components at a certain PM reliability threshold. Moreover, the OM optimization model is developed to minimize the maintenance cost under the optimal OM reliability threshold of each component. A simulated annealing (SA) algorithm is proposed to determine the optimal maintenance cost of the system and the optimal OM threshold under BW. Finally, a numerical example of a belt conveyor drive device in a port is introduced to demonstrate the feasibility and advantages of the proposed model in maintenance cost optimization.

scholarly journals Defining Optimal Maintenance Scope for Multiple k-out-of-n Load-Sharing Production Systems Connected in Series Based on RUL Predictions

2020 ◽  
Vol 7 (3) ◽  
Author(s):  
Leonardo R. Rodrigues
Keyword(s):  
Production System ◽  
Production Cost ◽  
Health Management ◽  
Load Sharing ◽  
Remaining Useful Life ◽  
Production Unit ◽  
Maintenance Cost ◽  
Total Cost ◽  
In Series ◽  
Optimal Maintenance

This paper presents a method to define the optimal maintenance scope of a production system consisting of multiple k-out-of-n systems connected in series. Maintenance recommendations are based on Remaining Useful Life (RUL) predictions obtained from a Prognostics and Health Management (PHM) system for each production unit within the production system. Defining the techniques applied in order to estimate the degradation level of production units is out of the scope of this paper. It is assumed here that a PHM system is available and provides the degradation level and RUL estimates for each production unit. The goal is to find the maintenance scope that minimizes the expected total cost per cycle until the next maintenance activity. A k-out-of-n load-sharing system is assumed, which means that the failure of a production unit results in a higher load (and consequently a higher degradation rate) on the surviving production units. The total cost comprises the production cost and the maintenance cost. Production cost of each k-out-of-n system is also affected by the number of surviving production units. A preventive maintenance cost is incurred to maintain a degraded but still functional production unit. A corrective maintenance cost is incurredto maintain a failed production unit. An Ant Colony Optimization (ACO) approach is adopted, which allows the proposed method to deal with large instances of the problem. A numerical example is presented to illustrate the application of the proposed method.

A Study on Imperfect Production System Under Maintenance Strategies and Warranty

2018 ◽  
pp. 371-387
Author(s):  
Chandra K. Jaggi ◽  
Prerna Gautam ◽  
Aditi Khanna
Keyword(s):  
Sensitivity Analysis ◽  
Production System ◽  
Preventive Maintenance ◽  
World Wide ◽  
Maintenance Cost ◽  
Total Cost ◽  
Imperfect Production ◽  
Holding Cost ◽  
Warranty Cost ◽  
Model Features

In every production system, malfunctioning or breakdown during run time can incur heavy loss to the organization, to overcome such a situation it is crucial to use maintenance actions which can be either corrective or preventive depending upon the condition of the system. Also, warranty policy is extensively used world-wide to increase customer confidence in the product and to uplift sales. On account of this, the present chapter presents a problem of a manufacturer dealing with an imperfect production system considering maintenance actions and warranty policy by trading off the rework cost, holding cost, warranty cost and corrective/preventive maintenance cost so as to minimize the manufacturer's total cost. Numerical analysis and sensitivity analysis is performed to showcase model features.

Bottleneck-based opportunistic maintenance model for series production systems

2015 ◽  
Vol 21 (1) ◽  
pp. 70-88 ◽  
Author(s):  
Binghai Zhou ◽  
Jiadi Yu ◽  
Jianyi Shao ◽  
Damien Trentesaux
Keyword(s):  
Production System ◽  
Production Systems ◽  
System Capacity ◽  
System Throughput ◽  
Maintenance Cost ◽  
Series Production ◽  
Opportunistic Maintenance ◽  
Content Type ◽  
Maintenance Activities ◽  
Bottleneck Machine

Purpose – The purpose of this paper is to develop a bottleneck-based opportunistic maintenance (OM) model for the series production systems with the integration of the imperfect effect into maintenance activities. Design/methodology/approach – On the analysis of availability and maintenance cost, preventive maintenance (PM) models subjected to imperfect maintenance for different equipment types are built. And then, a cost-saving function of OM is established to find out an optimal OM strategy, depending on whether the front-bottleneck machines adopt OM strategy or not. A numerical example is given to show how the proposed bottleneck-based OM model proceeded. Findings – The simulation results indicate that the proposed model is better than the methods to maintain the machines separately and the policy to maintain all machines when bottleneck machine reaches its reliability threshold. Furthermore, the relationship between OM strategy and corresponding parameters is identified through sensitivity analysis. Practical implications – In practical situations, the bottleneck machine always determines the throughput of the whole series production system. Whenever a PM activity is carried out on the bottleneck machine, there will be an opportunity to maintenance other machines. Under such circumstances, findings of this paper can be utilized for the determination of optimal OM policy with the objective of minimizing total maintenance cost of the system. Originality/value – This paper presents a bottleneck-based OM optimization model with the integration of the imperfect effect as a new method to schedule maintenance activities for a series production system with buffers. Furthermore, to the best of the knowledge, this paper presents the first attempt to considering the bottleneck constraint on system capacity and diverse types of machines as a means to minimize the maintenance cost and ensure the system throughput.

Multi-component maintenance grouping optimization based on stochastic dependency

2020 ◽  
pp. 1748006X2094751
Author(s):  
Vimal Vijayan ◽  
Sanjay K Chaturvedi
Keyword(s):  
Diesel Engine ◽  
Cost Optimization ◽  
Maintenance Cost ◽  
Substantial Cost ◽  
Maintenance Time ◽  
Optimal Maintenance ◽  
Air Intake ◽  
Maintenance Activities ◽  
Preparatory Work

Maintenance activities often require an identical preparatory work. Therefore, a joint execution of such maintenance activities may save a substantial cost. In this work, we consider the problem of optimizing the total maintenance cost of a multi-component repairable system by grouping of components and carrying out maintenance activities on group(s) of components of a complex system. More specifically, we propose a maintenance grouping cost optimization model based on the stochastic dependency as well as economic dependency among components in a system. The stochastic dependency modeling is done using Bayesian network by considering the failure probability of components as a measure of failure interactions among components. Penalty functions are formulated due to the shift of individual optimal maintenance time of components to find the optimum joint maintenance interval and associated cost benefits. Finally, a case study on a diesel engine of a diesel power plant involving 10 components (components of diesel engine, air intake system, and turbocharger) is presented to illustrate the proposed approach.

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 Power Electric Transformer Fault Diagnosis Based on Infrared Thermal Images Using Wasserstein Generative Adversarial Networks and Deep Learning Classifier

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1161
Author(s):  
Kuo-Hao Fanchiang ◽  
Yen-Chih Huang ◽  
Cheng-Chien Kuo
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Monitoring System ◽  
Absolute Difference ◽  
Generative Adversarial Networks ◽  
Economic Losses ◽  
Maintenance Cost ◽  
Full Time ◽  
Noise Interference ◽  
In Series

The safety of electric power networks depends on the health of the transformer. However, once a variety of transformer failure occurs, it will not only reduce the reliability of the power system but also cause major accidents and huge economic losses. Until now, many diagnosis methods have been proposed to monitor the operation of the transformer. Most of these methods cannot be detected and diagnosed online and are prone to noise interference and high maintenance cost that will cause obstacles to the real-time monitoring system of the transformer. This paper presents a full-time online fault monitoring system for cast-resin transformer and proposes an overheating fault diagnosis method based on infrared thermography (IRT) images. First, the normal and fault IRT images of the cast-resin transformer are collected by the proposed thermal camera monitoring system. Next is the model training for the Wasserstein Autoencoder Reconstruction (WAR) model and the Differential Image Classification (DIC) model. The differential image can be acquired by the calculation of pixel-wise absolute difference between real images and regenerated images. Finally, in the test phase, the well-trained WAR and DIC models are connected in series to form a module for fault diagnosis. Compared with the existing deep learning algorithms, the experimental results demonstrate the great advantages of the proposed model, which can obtain the comprehensive performance with lightweight, small storage size, rapid inference time and adequate diagnostic accuracy.

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