A modularized framework for predictive maintenance scheduling

2011 ◽  
Vol 226 (4) ◽  
pp. 380-391 ◽  
Author(s):  
Ming-Yi You ◽  
Guang Meng
Keyword(s):  
Preventive Maintenance ◽  
Industrial Applications ◽  
Maintenance Scheduling ◽  
Predictive Maintenance ◽  
Scheduling Model ◽  
Component Systems ◽  
Maintenance Policies ◽  
The Individual ◽  
Maintenance Schedules ◽  
Dynamic Updating

This paper presents a modularized, easy-to-implement framework for predictive maintenance scheduling. With a modularization treatment of a maintenance scheduling model, a predictive maintenance scheduling model can be established by integrating components’ real-time, sensory-updated prognostics information with a classical preventive maintenance/condition-based maintenance scheduling model. With the framework, a predictive maintenance scheduling model for multi-component systems is established to illustrate the framework’s use; such a predictive maintenance scheduling model for multi-component systems has not been reported previously in the literature. A numerical example is provided to investigate the individual-orientation and dynamic updating characteristics of the optimal preventive maintenance schedules of the established predictive maintenance scheduling model and to evaluate the performance of these preventive maintenance schedules. It is hoped that the presented framework will facilitate the implementation of predictive maintenance policies in various industrial applications.

Selection of Maintenance Strategies in an Operating Theatre

2021 ◽  
pp. 1-35
Author(s):  
María Carmen Carnero ◽  
Andrés Gómez
Keyword(s):  
Preventive Maintenance ◽  
Predictive Maintenance ◽  
Operating Theatre ◽  
Sterile Water ◽  
Repairable Systems ◽  
Corrective Maintenance ◽  
Water Power ◽  
Suitable Combination ◽  
Multicriteria Model ◽  
Maintenance Policies

The aim of this chapter is to select the most suitable combination of maintenance policies in the different systems that make up an operating theatre: air conditioning, sterile water, power supply, medicinal gases, and operating theatre lighting. To do so, a multicriteria model will be developed using the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) approach considering multiple decision centres. The model uses functional, safety, and technical-economic criteria, amongst which is availability. Mean availability for repairable systems has been measured to assess this criterion, using Markov chains from the data obtained over three years from the subsystems of a hospital operating theatre. The alternatives considered are corrective maintenance; preventive maintenance together with corrective maintenance by means of daily, weekly, monthly, and yearly programmes; periodical predictive maintenance together with corrective maintenance; and corrective together with preventive and predictive maintenance.

scholarly journals Automobile Predictive Maintenance using Deep Learning

2021 ◽  
Vol 11 (2) ◽  
pp. 0-0
Keyword(s):  
Preventive Maintenance ◽  
Binary Classification ◽  
Decision Rules ◽  
Complete Information ◽  
Predictive Maintenance ◽  
Maintenance Management ◽  
Classification Model ◽  
Management Policy ◽  
Maintenance Cycle ◽  
Maintenance Schedules

There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

scholarly journals Preventive Maintenance Scheduling for Multicogeneration Plants with Production Constraints Using Genetic Algorithms

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Khaled Alhamad ◽  
Mohsen Alardhi ◽  
Abdulla Almazrouee
Keyword(s):  
Energy Management ◽  
Preventive Maintenance ◽  
Time Horizon ◽  
Maintenance Scheduling ◽  
The Other ◽  
Scheduling Problem ◽  
Decision Tool ◽  
Production Constraints ◽  
Effective Decision ◽  
Maintenance Schedules

This paper describes a method developed to schedule the preventive maintenance tasks of the generation and desalination units in separate and linked cogeneration plants provided that all the necessary maintenance and production constraints are satisfied. The proposed methodology is used to generate two preventing maintenance schedules, one for electricity and the other for distiller. Two types of crossover operators were adopted, 2-point and 4-point. The objective function of the model is to maximize the available number of operational units in each plant. The results obtained were satisfying the problem parameters. However, 4-point slightly produce better solution than 2-point ones for both electricity and water distiller. The performance as well as the effectiveness of the genetic algorithm in solving preventive maintenance scheduling is applied and tested on a real system of 21 units for electricity and 21 units for water. The results presented here show a great potential for utility applications for effective energy management over a time horizon of 52 weeks. The model presented is an effective decision tool that optimizes the solution of the maintenance scheduling problem for cogeneration plants under maintenance and production constraints.

Optimum Consecutive Preventive Maintenance Scheduling Model Considering Reliability

2019 ◽  
Vol 24 (4) ◽  
pp. 490-495
Author(s):  
Gehui Liu ◽  
Xiangyu Long ◽  
Shuo Tong ◽  
Rui Zhang ◽  
Shaokuan Chen
Keyword(s):  
Preventive Maintenance ◽  
Maintenance Scheduling ◽  
Scheduling Model

scholarly journals A Preventive Maintenance Scheduling Model of the Cluster Tool

IE interfaces ◽  
2012 ◽  
Vol 25 (1) ◽  
pp. 127-133 ◽  
Author(s):  
Hyun Lee ◽  
You-Jin Park ◽  
Sun Hur
Keyword(s):  
Preventive Maintenance ◽  
Maintenance Scheduling ◽  
Scheduling Model ◽  
Cluster Tool

An Optimal Preventive Maintenance Model for a Structure

2012 ◽  
Vol 496 ◽  
pp. 484-487
Author(s):  
Xiao Li Zou
Keyword(s):  
Fatigue Crack ◽  
Preventive Maintenance ◽  
Minimum Cost ◽  
Crack Size ◽  
Maintenance Scheduling ◽  
Optimal Time ◽  
Cost Rate ◽  
Scheduling Model ◽  
Maintenance Model ◽  
Rate Criterion

An optimal preventive maintenance scheduling model for deteriorating structures is presented. The random initial damage and the cumulative damages are quantitatively measured with the statistical distribution of a dominant fatigue crack size in the structure. The preventive maintenance for the structure in service is assumed to be possible only at a series of discrete times. By taking into account the costs of structure failure and preventive maintenance, a minimum cost rate criterion is established to determinate the optimal time for preventive maintenance. Finally, an illustrative example is given

scholarly journals Preventive maintenance scheduling of multi-component systems with interval costs

2014 ◽  
Vol 76 ◽  
pp. 390-400 ◽  
Author(s):  
Emil Gustavsson ◽  
Michael Patriksson ◽  
Ann-Brith Strömberg ◽  
Adam Wojciechowski ◽  
Magnus Önnheim
Keyword(s):  
Preventive Maintenance ◽  
Maintenance Scheduling ◽  
Component Systems
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