scholarly journals Integrating Modelling of Maintenance Policies within a Stochastic Hybrid Automaton Framework of Dynamic Reliability

2021 ◽  
Vol 11 (5) ◽  
pp. 2300
Author(s):  
Simone Arena ◽  
Irene Roda ◽  
Ferdinando Chiacchio
Keyword(s):  
Model Systems ◽  
Mitigation Measures ◽  
Repairable Systems ◽  
Maintenance Policy ◽  
Hybrid Automaton ◽  
Dynamic Reliability ◽  
Software Model ◽  
Maintenance Policies ◽  
Mathematical Techniques ◽  
Continuous And Discrete Variables

The dependability assessment is a crucial activity for determining the availability, safety and maintainability of a system and establishing the best mitigation measures to prevent serious flaws and process interruptions. One of the most promising methodologies for the analysis of complex systems is Dynamic Reliability (also known as DPRA) with models that define explicitly the interactions between components and variables. Among the mathematical techniques of DPRA, Stochastic Hybrid Automaton (SHA) has been used to model systems characterized by continuous and discrete variables. Recently, a DPRA-oriented SHA modelling formalism, known as Stochastic Hybrid Fault Tree Automaton (SHyFTA), has been formalized together with a software library (SHyFTOO) that simplifies the resolution of complex models. At the state of the art, SHyFTOO allows analyzing the dependability of multistate repairable systems characterized by a reactive maintenance policy. Exploiting the flexibility of SHyFTA, this paper aims to extend the tools’ functionalities to other well-known maintenance policies. To achieve this goal, the main features of the preventive, risk-based and condition-based maintenance policies will be analyzed and used to design a software model to integrate into the SHyFTOO. Finally, a case study to test and compare the results of the different maintenance policies will be illustrated.

Progressive maintenance policy for multiple repairable systems with imperfect maintenance

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Garima Sharma ◽  
Rajiv Nandan Rai
Keyword(s):  
Power Plants ◽  
Maintenance Cost ◽  
Mean Residual Life ◽  
Time Interval ◽  
Imperfect Maintenance ◽  
Repairable Systems ◽  
Maintenance Policy ◽  
Data Set ◽  
Content Type ◽  
Maintenance Policies

PurposeDegradation of repairable components may not be similar after each maintenance activity; thus, the classic (traditional-time based) maintenance policies, which consider preventive maintenance (PM), age-based maintenance and overhauls to be done at fixed time interval, may fail to monitor the exact condition of the component. Thus, a progressive maintenance policy (PMP) may be more appropriate for the industries that deal with large, complex and critical repairable systems (RS) such as aerospace industries, nuclear power plants, etc.Design/methodology/approachA progressive maintenance policy is developed, in which hard life, PM scheduled time and overhaul period of the system are revised after each service activity by adjusting PM interval and mean residual life (MRL) such that the risk of failure is not increased.FindingsA comparative study is then carried out between the classic PM policy and developed PMP, and the improvement in availability, mean time between failures and reduction in maintenance cost is registered.Originality/valueThe proposed PMP takes care of the equipment degradation more efficiently than any other existing maintenance policies and is also flexible in its application as the policy can be continuously amended as per the failure profile of the equipment. Similar maintenance policies assuming lifetime distributions are available in the literature, but to ascertain that the proposed PMP is more suitable and applicable to the industries, this paper uses Kijima-based imperfect maintenance models. The proposed PMP is demonstrated through a real-time data set example.

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.

Real-Time Maintenance Policy in Manufacturing Systems With Intermediate Buffers

2015 ◽  
Author(s):  
Xi Gu ◽  
Xiaoning Jin ◽  
Jun Ni
Keyword(s):  
Real Time ◽  
Manufacturing Systems ◽  
Maintenance Policy ◽  
Discrete Time Markov Chain ◽  
Markov Decision ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Time Buffer ◽  
Buffer Capacities ◽  
One Machine

Real-time maintenance decision making in large manufacturing system is complex because it requires the integration of different information, including the degradation states of machines, as well as inventories in the intermediate buffers. In this paper, by using a discrete time Markov chain (DTMC) model, we consider the real-time maintenance policies in manufacturing systems consisting of multiple machines and intermediate buffers. The optimal policy is investigated by using a Markov Decision Process (MDP) approach. This policy is compared with a baseline policy, where the maintenance decision on one machine only depends on its degradation state. The result shows how the structures of the policies are affected by the buffer capacities and real-time buffer levels.

Big Data Analytics for Predictive Maintenance Strategies

2017 ◽  
pp. 50-74 ◽  
Author(s):  
C. K. M. Lee ◽  
Yi Cao ◽  
Kam Hung Ng
Keyword(s):  
Big Data ◽  
Big Data Analytics ◽  
Cost Effective ◽  
Health Condition ◽  
Predictive Maintenance ◽  
Maintenance Policy ◽  
Failure Patterns ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Faulty Component

Maintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.

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.

On preventive maintenance policies: a selection framework

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Imad Alsyouf ◽  
Sadeque Hamdan ◽  
Mohammad Shamsuzzaman ◽  
Salah Haridy ◽  
Iyad Alawaysheh
Keyword(s):  
Preventive Maintenance ◽  
Fuzzy Ahp ◽  
Critical Component ◽  
Multi Objective Optimization ◽  
Maintenance Policy ◽  
Coolant Pump ◽  
Content Type ◽  
Multi Objective ◽  
Maintenance Policies ◽  
Preventive Maintenance Policy

PurposeThis paper develops a framework for selecting the most efficient and effective preventive maintenance policy using multiple-criteria decision making and multi-objective optimization.Design/methodology/approachThe critical component is identified with a list of maintenance policies, and then its failure data are collected and the optimization objective functions are defined. Fuzzy AHP is used to prioritize each objective based on the experts' questionnaire. Weighted comprehensive criterion method is used to solve the multi-objective models for each policy. Finally, the effectiveness and efficiency are calculated to select the best maintenance policy.FindingsFor a fleet of buses in hot climate environment where coolant pump is identified as the most critical component, it was found that block-GAN policy is the most efficient and effective one with a 10.24% of cost saving and 0.34 expected number of failures per cycle compared to age policy and block-BAO policy.Research limitations/implicationsOnly three maintenance policies are compared and studied. Other maintenance policies can also be considered in future.Practical implicationsThe proposed methodology is implemented in UAE for selecting a maintenance scheme for a critical component in a fleet of buses. It can be validated later in other Gulf countries.Originality/valueThis research lays a solid foundation for selecting the most efficient and effective preventive maintenance policy for different applications and sectors using MCDM and multi-objective optimization to improve reliability and avoid economic loss.

Coloured stochastic Petri nets modelling for the reliability and maintenance analysis of multi-state multi-unit systems

2014 ◽  
Vol 25 (4) ◽  
pp. 476-490 ◽  
Author(s):  
Zhouhang Wang ◽  
Maen Atli ◽  
H. Kondo Adjallah
Keyword(s):  
Petri Nets ◽  
Complex Structure ◽  
Real Life ◽  
Simulation Models ◽  
Decision Makers ◽  
Stochastic Petri Nets ◽  
Repairable Systems ◽  
Content Type ◽  
Markov Graphs ◽  
Maintenance Policies

Purpose – The purpose of this paper is to introduce a method for modelling the multi-state repairable systems subject to stochastic degradation processes by using the coloured stochastic Petri nets (CSPN). The method is a compact and flexible Petri nets model for multi-state repairable systems and offers an alternative to the combinatory of Markov graphs. Design/methodology/approach – The method is grounded on specific theorems used to design an algorithm for systematic construction of multi-state repairable systems models, whatever is their size. Findings – Stop and constraint functions were derived from these theorems and allow to considering k-out-of-n structure systems and to identifying the minimal cut sets, useful to monitoring the states evolution of the system. Research limitations/implications – The properties of this model will be studied, and new investigations will help to demonstrate the feasibility of the approach in real world, and more complex structure will be considered. Practical implications – The simulation models based on CSPN can be used as a tool by maintenance decision makers, for prediction of the effectiveness of maintenance strategies. Originality/value – The proposed approach and model provide an efficient tool for advanced investigations on the development and implementation of maintenance policies and strategies in real life.

Maintenance Decision Model for a Two-Machine Production Line With Multistage Degradation Machines

2018 ◽  
Author(s):  
Yunyi Kang ◽  
Feng Ju
Keyword(s):  
Decision Model ◽  
Production Systems ◽  
Maintenance Policy ◽  
Multi Stage ◽  
Markov Decision ◽  
Optimal Maintenance ◽  
Markov Decision Model ◽  
Maintenance Decision ◽  
Maintenance Policies ◽  
Optimal Maintenance Policy

In this work, we develop preventative maintenance policies on two-machine-and-one-buffer production systems with machines subject to multi-stage degradation. Condition-based maintenance policies are generated for both machines, with consideration on both the machine degradation stages and the buffer level. Moreover, the policies are flexible, allowing a machine to be recovered to any better operating state, while merely recovering to the best operating state is possible in many previous work. A Markov decision model is formulated to find the optimal maintenance policy and computational experiments show that the policies improve the performance of a system in finite production runs.

scholarly journals Energy Consumption-Based Maintenance Policy Optimization

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5674
Author(s):  
Ágota Bányai
Keyword(s):  
Energy Consumption ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Functional Model ◽  
Optimization Method ◽  
Maintenance Policy ◽  
Failure Data ◽  
Maintenance Policies ◽  
Value Determination ◽  
Policy Optimization

The optimal predictive, preventive, corrective and opportunistic maintenance policies play an important role in the success of sustainable maintenance operations. This study discusses a new energy efficiency-related maintenance policy optimization method, which is based on failure data and status information from both the physical system and the digital twin-based discrete event simulation. The study presents the functional model, the mathematical model and the solution algorithm. The maintenance optimization method proposed in this paper is made up of four main phases: computation of energy consumption based on the levelized cost of energy, computation of GHG emission, computation of value determination equations and application of the Howard’s policy iteration techniques. The approach was tested with a scenario analysis, where different electricity generation sources were taken into consideration. The computational results validated the optimization method and show that optimized maintenance policies can lead to an average of 38% cost reduction regarding energy consumption related costs. Practical implications of the proposed model and method regard the possibility of finding optimal maintenance policies that can affect the energy consumption and emissions from the operation and maintenance of manufacturing systems.

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