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About 10,000 papers for “model-based maintenance”

Optimal Preventive Maintenance Tactics Model Based on Condition-based Maintenance

Hao Chen, Zhou Zheng, Zheng Xie · SHILAP Revista de lepidopterología · 2018

Condition-based maintenance(CBM) is based on specific system operation condition to evaluate the real status of monitored equipment. CBM overcome the blindness of maintenance, and is able to effectively reduce accidental failures caused by insufficient maintenance and waste of resources by the excess of maintenance. A multi-unit system non-periodic preventive maintenance plan optimization method is proposed, which is based on the condition. The concept of “opportunity maintenance threshold” is introduced, and the expression of different maintenance mode transition probability is optimized. Multiple maintenance works are combined in accordance with the opportunity maintenance threshold. The expression of different maintenance mode transition probability and the objective function on the basis of Markov Decision Process Theory are proposed. Through the example analysis, the proposed optimization model can reduce the preventive maintenance activities, save maintenance time and funds, and provide reference for system preventive maintenance strategy decision.

Cited by 0Open access

Model-Based Testing and Maintenance

Donghua Deng, Phillip C.‐Y. Sheu, T. Wang et al. · 2005

This paper presents a semantic software development model (SSDM) for object-oriented software. It organizes all the information generated during the software development lifecycle including requirements, design, implementation, testing, and maintenance. Based on SSDM, software testing and maintenance can be carried out in a more systematic, efficient and complete manner, and can be enhanced by a set of proactive rules defined.

Cited by 20

Machine Learning Based Predictive Maintenance Model

Ashok K. Pundir, Pratik Maheshwari, PK Prajapati · 2022

The production systems are affected by degradations and failures caused by operational and environmental conditions.Indeed, unplanned, unscheduled maintenance (UUM) practices lead to lower productivity, lost production, expensive labour (call-outs, overtime) and damaged equipments.However, predictive maintenance (PdM) is a condition-based maintenance strategy (CBM) that has become popular among practitioners in recent years that carries out maintenance action when needed, avoiding unnecessary preventive measures or failures.Machine learning (ML) has become an advanced diagnostic approach in maintenance strategy to mitigate those challenges.Despite that, the ML-based PdM strategies are infancy stage due to the immaturity of the Industrial Internet of things (IIoT) which is responsible for data collection and execution.Implementing ML-based PdM is a difficult and expensive process, especially for those companies which often lack the necessary skills and financial and labour resources.Thus, a cost-oriented analysis is required to define when ML-based PdM.The proposed study develops the ML algorithm and provides an intelligent PdM model with a framework for practitioners and researchers considering aircraft industries.The contribution of the paper has twofold.The first is to developing an ML-based predictive maintenance model.The analysis shows that random forest outperformed other models scored 28.63 cycles to predicts Time to failure (TTF) within the average error range of ±28 cycles.While, the research finding helps to monitor that how to execute data wrangling and prepare pandas data frames to define TTF of the airlines engines.

Cited by 9Open access

Prediction Cost Maintenance Model of Office Building Based on Condition-Based Maintenance

Cheong Peng Au-Yong, Azlan Shah Ali, Faizah Ahmad · Eksploatacja I Niezawodnosc-maintenance and Reliability · 2017

Building maintenance costs are continuously increasing as a result of poor maintenance. Consequently, there is an urgent need to develop solutions to reduce the maintenance costs. Various studies demonstrated that the characteristics of condition-based maintenance are directly related to the cost performance. Thus, this paper seeks to establish the relationships between the characteristics of condition-based maintenance and the cost performance. The researcher then developed a regression model for maintenance planning and prediction. The study adopted a mix method approach that includes questionnaire survey, interview, and case study. The findings highlighted the reliability of maintenance data and information as the most significant characteristic of condition-based maintenance. Consequently, the study concluded that the planning and the application of the condition-based maintenance strategy should consider its significant characteristics and make reference to the resulting prediction model. Furthermore, the study recommended measures to improve the significant characteristics and the cost performance in practice.

Cited by 21Open access

Prediction Cost Maintenance Model of Office Building Based on Condition-Based Maintenance

Au-Yong, Cheong Peng, Azlan Shah Ali, Faizah Ahmad · Figshare · 2017

Building maintenance costs are continuously increasing as a result of poor maintenance. Consequently, there is an urgent need to develop solutions to reduce the maintenance costs. Various studies demonstrated that the characteristics of condition-based maintenance are directly related to the cost performance. Thus, this paper seeks to establish the relationships between the characteristics of condition-based maintenance and the cost performance. The researcher then developed a regression model for maintenance planning and prediction. The study adopted a mix method approach that includes questionnaire survey, interview, and case study. The findings highlighted the reliability of maintenance data and information as the most significant characteristic of condition-based maintenance. Consequently, the study concluded that the planning and the application of the condition-based maintenance strategy should consider its significant characteristics and make reference to the resulting prediction model. Furthermore, the study recommended measures to improve the significant characteristics and the cost performance in practice.

Cited by 0Open access

Predictable Maintenance: A Bayesian Network-based Model

Mohammad Partovi, Mohsen Amra, Mohammadjavad Pahlevanzadeh et al. · International Journal of Reliability Risk and Safety Theory and Application · 2023

Industries' increasing progress and complexity has made maintenance and repair tasks very challenging, complex, and time-consuming. Maintenance is one of the important sectors in several industries, and improvement in this sector can have excellent results. This paper develops a new maintenance prediction model based on Bayesian networks (BN) capabilities. The models include several variables that experts determine and their influence on each other's-called conditional probability tables-which are learned from historical data. The model is implemented in an automobile repair department case study to show its performance. The model is evaluated through a sensitivity analysis, and the results show the proficiency of the proposal mode.

Cited by 0Open access

Multi-event Maintenance Decision-making Model Based on Opportunistic Maintenance Policy

Mengying Zhang, Gong Wang, Chuan Lv · 2015

As one core of modern maintenance theory, the maintenance decision-making theory has become a key research direction with its high precision and systematization. The maintenance decision-making aims to provide a reliable theoretical basis for the exclusion of faults to ensure normal circulation, adjust maintenance plan and treat of benefits, efficiency and economy according to the actual situation. For multi-component complex systems, fault events emerge one after another in a certain time period, a substantial portion of which can be reserved for quite a while and repair then. If the maintenance events are arranged together in a certain opportunity in accordance with opportunistic maintenance policy, maintenance plan can be adjusted and maintenance downtime can be reduced significantly. However, the most part of existing studies focus on pre-established maintenance policies, such as the combination of different preventive maintenance, or combination of preventive maintenance and corrective maintenance. And systematical dynamic maintenance decision-making during real-time operation is rare. Aiming at shortest maintenance downtime, the paper proposes a multi-event opportunistic maintenance decision-making model restricted to maintenance resources and mission requirements, which satisfy dynamic maintenance decision-making demands for complex system. In addition, it will greatly reduce maintenance costs and downtime, while improve system availability.

Cited by 0Open access

INTEGRATED PRODUCTION MAINTENANCE MODEL UNDER IMPERFECT AGE-BASED MAINTENANCE POLICY AND NON-NEGLIGIBLE MAINTENANCE TIMES

Sami El Ferik, Mohamed Ben‐Daya · Asia Pacific Journal of Operational Research · 2010

This paper deals with an integrated production maintenance model where preventive maintenance (PM) activities are carried out at the end of each production run or at failure, whichever of them occurs first. PM activities take a fixed amount of time. However, due to the random nature of failures, the stock built during the production period may deplete before the next production run starts, leading to some loss in demand. After N production runs, the process is replaced (or undergoes a major repair), which brings it to the "as-good-as-new" condition. A mathematical model is derived for this problem and a model analysis conducted to characterize optimal solutions. Numerical examples are used to illustrate the proposed model and conduct a sensitivity analysis with respect to key model parameters.

Cited by 9

A condition-based maintenance approach to an optimal maintenance strategy considering equipment imperfect maintenance model

Yeyong Li, Xueshan Han, Bo Xu et al. · 2015

Power grid tries to maximize its profit by reducing generation cost, supply costs and operation costs while sustaining its required reliability. Developing maintenance strategies is one of the most effective methods. The condition-based maintenance approach is a key method in providing optimal maintenance strategies in theory. It considers the tradeoffs between the system maintenance risk and the potential system failure risk of system reliability losses. Since power grid contains a group of different kinds of equipments and the reliability of the electric equipments varies with time. One type of electrical equipment has different maintenance models because of their different failure modes and deterioration processes. A union method for equipment maintenance model and system condition-based maintenance model is proposed. The modified imperfect maintenance model-Kijima II model is selected for equipment maintenance model comparing with minimal repair and perfect maintenance through optimizing imperfect maintenance effect factor. In addition, a genetic algorithm is used to find the optimal maintenance strategies from possible maintenance scenarios. These optimal maintenance strategies have been tested on a HVDC link which is simplified into an equivalent serial and parallel system and an IEEE-RTS79 system. The results demonstrate the feasibility and validity of the proposed model.

Cited by 8

Integrated model for condition-based maintenance, predictive maintenance and statistical process control

Mohamed Othman Latrous · theses.fr (ABES) · 2021

Integrated solutions are evolving to cope with the digitalization of manufacturing processes. One area of improvement is developing new strategies joining maintenance and quality control. A popular hypothesis used in these integrated models is considering that quality control comes down to monitoring a single process or product characteristic. However, for complex systems, this assumption is seldom satisfied as the number of characteristics grows and they can have potential correlations. In fact, a multivariate control chart is more suitable to guarantee better performances of drift detection. Following this perspective, the main objective of this thesis consists in developing new maintenance decision rules based on produced items’ quality data. Multivariate control charts are used to provide detection and identification of potential process shifts and adaptive maintenance actions are triggered, targeting system components responsible for the deterioration of the produced items’ quality. First, a preliminary study is led to highlight the impact of multivariate control charts on the decision making process. Next, integrated maintenance decision rules for condition based and predictive maintenance are proposed using multimodal control charts. The developed decision rules are applied in the context of a simulated manufacturing process exhibiting a quality defect appearing in different dynamics (fast and slow drifts). The new strategies proposed in this thesis have been validated through the development of analytical models expressing the profit of the manufacturing system. This profit was maximized based on decision variables (sampling period and critical intervention threshold) and the existence of optimal solutions has been demonstrated through simulation. The robustness of the developed models has been proven by sensitivity studies, followed by comparative studies in order to highlight the impact of the proposed strategies.

Cited by 0Open access

Surrogate-based flowsheet model maintenance for Digital Twins

Balázs Palotai, Gábor Kis, János Abonyi et al. · Digital Chemical Engineering · 2025

Digital Twins (DTs) are transforming industrial processes by providing virtual models that mirror physical systems, enabling real-time monitoring and optimization. A major challenge in DTs in process industry, is maintaining the accuracy of flowsheet simulation models due to changes like equipment degradation and operational shifts. This study proposes a novel surrogate-based approach for the automated calibration of these models, which reduces reliance on manual adjustments and adapts to changes in the physical system. This study leverages surrogate models and particle swarm optimization to incorporate modeling considerations and measurement uncertainties, thereby automating model calibration and reducing manual interventions. In a refinery case study, our approach reduced calibration time for the sour water stripper Hysys model by 80% while maintaining the desired accuracy. These results highlight the method’s potential to enhance flowsheet model accuracy in digital twin systems and to support more robust and adaptable DT applications. • Surrogate-based calibration for flowsheet models in digital twins. • Reduced manual intervention through automated calibration. • Taguchi method boosts dynamic calibration and model adaptability. • The case study demonstrates that partial inverse models are fast, but surrogates offer greater flexibility.

Cited by 3Open access

Ontology and Meta-Model Based Maintenance Process Modeling

Zhang Yan, Dong Zhou, Ziyue Guo et al. · 2025

To address the issues of fragmented knowledge systems and the lack of standardized representation and application in current maintenance process modeling, this paper proposes a maintenance process modeling method based on ontology and meta-model. Firstly, by constructing a Maintenance Process Concept Ontology (MPCO), it systematically integrates knowledge concepts and semantic relationships within maintenance process, resolving problems of dispersed knowledge and inconsistent semantic expression. Secondly, based on MPCO, elements and relationships of the maintenance process meta-model are defined to establish a highlevel reference framework. Further, the SysML stereotype mechanism is extended to develop domain-specific stereotypes for maintenance processes, enabling SysML-based maintenance process modeling. Finally, through ontology reasoning technology, reasoning rules are formulated to verify and supplement potential logical relationships in model instances, thereby enhancing model rationality.

Cited by 0