General overview of maintenance strategies – concepts and approaches

Gackowiec, Paulina

doi:10.2478/mape-2019-0013

Cited by 29 publications

(14 citation statements)

References 35 publications

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“…Data mining is a data preparation employed by industrial companies to transform a raw dataset into valuable information [16]. By using an application to eye for patterns in large dataset, companies can discover more about their equipment health to develop more effective maintenance strategy, increase productivity and reduce costs [17]. It is based on operational data collection, warehousing, and processing power.…”

Section: Training Algorithmsmentioning

confidence: 99%

Simulation for predictive maintenance using weighted training algorithms in machine learning

Jittawiriyanukoon¹,

Srisarkun²

2022

IJECE

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<span>In the production, the efficient employment of machines is realized as a source of industry competition and strategic planning. In the manufacturing industries, data silos are harvested, which is needful to be monitored and deployed as an operational tool, which will associate with a right decision-making for minimizing maintenance cost. However, it is complex to prioritize and decide between several results. This article utilizes a synthetic data from a factory, mines the data to filter for an insight and performs machine learning (ML) tool in artiﬁcial intelligence (AI) to strategize a decision support and schedule a plan for maintenance. Data includes machinery, category, machinery, usage statistics, acquisition, owner’s unit, location, classiﬁcation, and downtime. An open-source ML software tool is used to replace the short of maintenance planning and schedule. Upon data mining three promising training algorithms for the insightful data are employed as a result their accuracy figures are obtained. Then the accuracy as weighted factors to forecast the priority in maintenance schedule is proposed. The analysis helps monitor the anticipation of new machines in order to minimize mean time between failures (MTBF), promote the continuous manufacturing and achieve production’s safety.</span>

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Section: Training Algorithmsmentioning

confidence: 99%

Simulation for predictive maintenance using weighted training algorithms in machine learning

Jittawiriyanukoon¹,

Srisarkun²

2022

IJECE

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“…Ding and Kamaruddin (2015) classified maintenance policies on the basis of (1) strategies that aim to repair and restore asset to original function state, (2) strategies that prevent failures and retain functioning of system and (3) an advanced improvement-based maintenance strategy that engages operators and system redesigning too. Gackowiec (2019) has drawn an overview of evolution of maintenance strategy classifications. After studying the prominent classifications of maintenance strategies, a generic classification for these is presented in this study in Figure 3.…”

Section: Maintenance Strategy Paradigmsmentioning

confidence: 99%

Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches

Patil

Soni

Prakash

et al. 2021

IJQRM

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PurposeIn today's competitive industries, the selection of best suitable maintenance strategy is dependent on large number of quantitative and qualitative factors, and it becomes an extensively difficult problem for maintenance engineers. Over the years, a diverse range of solution methodologies have been developed for solving this multi-criteria decision-making (MCDM) problem. In this paper, the authors have presented a comprehensive review of latest maintenance strategy paradigms and solution approaches proposed for the selection of an appropriate strategy in various industries. It would provide a systematic mapping of developments in this field and identify some research gaps to explore further studies.Design/methodology/approachA systematic state-of-the-art comprehensive literature review on maintenance strategy paradigms and selection approaches is presented in this study. In this study, 87 research articles published in peer-reviewed journals, since year 2012, are reviewed.FindingsFor the selection of a suitable maintenance strategy, a variety of criteria are considered to better evaluate the alternatives. In this study, contemporary strategies are discussed, and their applications in different industries are also depicted. Moreover, through the analysis of extant literature, critical criteria are selected and classified in six major categories (namely, economic, technical, safety, environmental, feasibility and social) and further sub-categorized in quantitative and qualitative classes. These clusters of criteria can be helpful as an initial set of criteria for survey and then case- or industry-specific criteria can be shortlisted for further alternative evaluation.Practical implicationsFrom the perspective of maintenance managers, maintenance management can be a very difficult task, considering the numerous factors affecting the decision-making process. In order to help in the decision-making process, this study presents the contemporary maintenance strategies in a systematic manner. In a previous study (Kothamasu et al., 2006), these strategies were classified into repair and prevent classes only. With the developments of autonomous maintenance and design out maintenance (DOM), it was fair to include continuous improvement class. It will help managers and practitioners to identify, according to organization policy, appropriate maintenance strategy alternatives for the asset. A benchmark set of state-of-the-art maintenance strategies are laid out with their applications. The industrial case studies discussed in this study summarizes the optimal maintenance strategies for respective industries. Also, most critical criteria are identified from the existing studies for various industries that can help maintenance practitioners in acknowledging the critical factors and making appropriate decisions. Evaluation parameters for the maintenance strategy selection (MSS) generally conflict with each other, and considering the difficulty of quantifying the qualitative measures, it is a challenging task to determine the optimal trade-off. In order to overcome these challenges, popular MCDM approaches, demonstrating effective results across different industries are discussed with their limitations and applications. Decision-makers can refer this study to identify best suitable decision-making technique for the MSS problem in the industry of their choice. Maintenance managers and engineers can refer the case studies illustrated in Tables 1 and 2 to analyse the MSS techniques proposed by previous studies with industry-specific applications.Social implicationsThis study is an attempt to provide a reference point for research scholars interested in the field of maintenance management and/or development of maintenance strategy framework. This study provides a critical state-of-the-art review of efforts made in the field of MSS. The prominent maintenance strategies being implemented in contemporary industries are discussed with respective case studies. Interested researchers and academicians can familiarize themselves with these strategies and their distinct features in this study. In order to guide future studies and provide a reference point for academicians, MSS critical criteria used in extant literature are identified and classified into a comprehensive benchmark framework. Moreover, the industrial case studies are discussed with the most critical criteria of MSS for different industries and which strategy is most suitable for the respective industries based on these criteria. Table 1 presents different MCDM techniques and their hybrid applications for solving MSS problem that can help researchers in identifying research gaps. Future research can be directed at addressing the limitation of MCDM approach employed in existing studies and comparing the differences in results obtained by the proposed approach. Different industrial case studies with considered maintenance strategy alternatives are presented in Table 2, which can help researchers in identifying the industries that have not been studied yet. Moreover, not all of the existing studies are carried out by considering all the presented benchmark strategies, which can be addressed in future studies by interested researchers. More detailed discussion on research gaps is presented in the following section.Originality/valueFrom the analysis of the extant literature, the authors could observe that the decision-making process adopted in numerous studies was limited to the classical maintenance strategies and not inclusive of aggressive maintenance strategy alternatives. To overcome these limitations and help maintenance managers in the decision-making, this study depicts the contemporary maintenance strategies, critical evaluation criteria and MCDM frameworks (employed to solve the MSS problem with industrial case studies) in a structured manner.

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“…Maintenance is defined as an act of keeping machines or tools to make them stay in a normal condition and able to function properly (Gackowiec, 2019). According to ISO 14224:2016, maintenance has been defined as the combination of technical and associated administrative actions intended to retain an item or system or restore it to a state where it can perform its required function.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The planned maintenance consists of preventive maintenance and corrective maintenance. There are two types of activities in preventive maintenance, namely maintenance in the form of heating machines or tools (running maintenance) (Gackowiec, 2019) and maintenance that must stop the activities of all machines in a moment related to the addition of components or shutdown maintenance (Garg & Gupta, 1987). Furthermore, corrective maintenance is an activity that can be divided into two.…”

Section: Figure 1 An Overview Of the Crucial Maintenancementioning

confidence: 99%

The benefit of running maintenance habituation before learning of machining process begins

Sukardi

Setiadi

Yudantoko³

2021

jpv

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The study was aimed at investigating the advantages of the habituation of running maintenance and supervision over the practice learning process and the learning achievement of the students of Mechanical Engineering Study Program, Faculty of Engineering, Universitas Negeri Yogyakarta. It was participatory classroom action research. The subjects were 80 third-semester students grouped into the group C class. The data on the habituation of running maintenance were collected using a questionnaire; those on the students’ learning achievement were collected through documentation and analyzed using a descriptive technique; those on the supervision intensity and machine feasibility were gathered through observations and analyzed using the descriptive qualitative technique. The findings showed that: 1.) The implementation of running maintenance used the systems of coaching, mentoring, and supervision with an optimal intensity of lecturers in teaching-learning machining practices; 2.) The habituation of running maintenance was done through standard procedures, and the implemented action took three cycles; 3.) The achievement of the habituation of running maintenance on students during the implementation of teaching-learning practices was characterized by the rapid job sheet completion that must be taken in one semester; and 4.) With the habituation of running maintenance, the feasibility of the existing machines in the workshop could be maintained.

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General overview of maintenance strategies – concepts and approaches

Cited by 29 publications

References 35 publications

Simulation for predictive maintenance using weighted training algorithms in machine learning

Simulation for predictive maintenance using weighted training algorithms in machine learning

Maintenance strategy selection: a comprehensive review of current paradigms and solution approaches

The benefit of running maintenance habituation before learning of machining process begins

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