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Article
Publication date: 20 December 2018

Sara Antomarioni, Maurizio Bevilacqua, Domenico Potena and Claudia Diamantini

The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The…

Abstract

Purpose

The purpose of this paper is developing a data-driven maintenance policy through the analysis of vast amount of data and its application to an oil refinery plant. The maintenance policy, analyzing data regarding sub-plant stoppages and components breakdowns within a defined time interval, supports the decision maker in determining whether it is better to perform predictive maintenance or corrective interventions on the basis of probability measurements.

Design/methodology/approach

The formalism applied to pursue this aim is association rules mining since it allows to discover the existence of relationships between sub-plant stoppages and components breakdowns.

Findings

The application of the maintenance policy to a three-year case highlighted that the extracted rules depend on both the kind of stoppage and the timeframe considered, hence different maintenance strategies are suggested.

Originality/value

This paper demonstrates that data mining (DM) tools, like association rules (AR), can provide a valuable support to maintenance processes. In particular, the described policy can be generalized and applied both to other refineries and to other continuous production systems.

Details

International Journal of Quality & Reliability Management, vol. 36 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

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Article
Publication date: 3 October 2008

Heiko Gebauer, Felix Pützr, Thomas Fischer, Chunzhi Wang and Jie Lin

The purpose of this paper is to explore maintenance strategies for manufacturing equipment in Chinese firms.

Abstract

Purpose

The purpose of this paper is to explore maintenance strategies for manufacturing equipment in Chinese firms.

Design/methodology/approach

Data were collected from Chinese companies using a questionnaire administered during face‐to‐face interviews and two established methodologies in strategic management research, exploratory factor analysis and cluster analysis, were used to analyze the data.

Findings

The results suggest that despite increasing competitive capabilities of Chinese firms, their maintenance strategies are often restricted to corrective maintenance. Only very few Chinese firms have already implemented predictive maintenance approach, total productive maintenance programs or the strategic outsourcing of maintenance activities.

Research limitations/implications

The research limitations stem from typical issues related to the use of exploratory factor analysis and cluster analysis (for example reliance on the subjective judgment of the researcher or the provision of clusters although no meaningful groups are embedded in the sample).

Practical implications

The findings highlight potential strategies for Chinese firms to improve their maintenance management.

Originality/value

This paper deals with a neglected area of operations management by exploring the maintenance approaches in fast growing Chinese manufacturing industries.

Details

Management Research News, vol. 31 no. 12
Type: Research Article
ISSN: 0140-9174

Keywords

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Article
Publication date: 11 January 2011

Edwin Vijay Kumar and S.K. Chaturvedi

This paper aims to prioritize preventive maintenance actions on process equipment by evaluating the risk associated with failure modes using predictive maintenance data…

Abstract

Purpose

This paper aims to prioritize preventive maintenance actions on process equipment by evaluating the risk associated with failure modes using predictive maintenance data instead of maintenance history alone.

Design/methodology/approach

In process plants, maintenance task identification is based on the failure mode and effect analysis (FMEA). To eliminate or mitigate risk caused by failure modes, maintenance tasks need to be prioritized. Risk priority number (RPN) can be used to rank the risk. RPN is estimated invariably using maintenance history. However, maintenance history has deficiencies, like limited data, inconsistency etc. To overcome these deficiencies, the proposed approach uses the predictive maintenance data clubbed with expert domain knowledge. Unlike the traditional single step approach, RPN is estimated in two steps, i.e. Step 1 estimates the “Possibility of failure mode detection” and Step 2 estimates RPN using output of step 1. Fuzzy sets and approximate reasoning are used to handle the uncertainty/imprecision in data and subjectivity/vagueness of expert domain knowledge. Fuzzy inference system is developed using MATLAB® 6.5.

Findings

The proposed approach is applied to a large gearbox in an integrated steel plant. The gearbox is covered under a predictive maintenance program. RPN for each of the failure modes is estimated with the proposed approach and compared with the maintenance task schedule. The illustrative case study results show that the proposed approach helps in detection of failure modes more scientifically and prevents “Over maintenance” to ensure reliability.

Originality/value

This approach gives an opportunity to integrate the predictive maintenance data and subjective/qualitative domain expertise to evaluate the possibility of failure mode detection (POD) quantitatively, which is otherwise purely estimated using subjective judgments. The approach is generic and can be applied to a variety of process equipment to ensure reliability through prioritized maintenance scheduling.

Details

International Journal of Quality & Reliability Management, vol. 28 no. 1
Type: Research Article
ISSN: 0265-671X

Keywords

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Article
Publication date: 19 February 2020

Shashidhar Kaparthi and Daniel Bumblauskas

The after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article…

Abstract

Purpose

The after-sale service industry is estimated to contribute over 8 percent to the US GDP. For use in this considerably large service management industry, this article provides verification in the application of decision tree-based machine learning algorithms for optimal maintenance decision-making. The motivation for this research arose from discussions held with a large agricultural equipment manufacturing company interested in increasing the uptime of their expensive machinery and in helping their dealer network.

Design/methodology/approach

We propose a general strategy for the design of predictive maintenance systems using machine learning techniques. Then, we present a case study where multiple machine learning algorithms are applied to a particular example situation for an illustration of the proposed strategy and evaluation of its performance.

Findings

We found progressive improvements using such machine learning techniques in terms of accuracy in predictions of failure, demonstrating that the proposed strategy is successful.

Research limitations/implications

This approach is scalable to a wide variety of applications to aid in failure prediction. These approaches are generalizable to many systems irrespective of the underlying physics. Even though we focus on decision tree-based machine learning techniques in this study, the general design strategy proposed can be used with all other supervised learning techniques like neural networks, boosting algorithms, support vector machines, and statistical methods.

Practical implications

This approach is applicable to many different types of systems that require maintenance and repair decision-making. A case is provided for a cloud data storage provider. The methods described in the case can be used in any number of systems and industrial applications, making this a very scalable case for industry practitioners. This scalability is possible as the machine learning techniques learn the correspondence between machine conditions and outcome state irrespective of the underlying physics governing the systems.

Social implications

Sustainable systems and operations require allocating and utilizing resources efficiently and effectively. This approach can help asset managers decide how to sustainably allocate resources by increasing uptime and utilization for expensive equipment.

Originality/value

This is a novel application and case study for decision tree-based machine learning that will aid researchers in developing tools and techniques in this area as well as those working in the artificial intelligence and service management space.

Details

International Journal of Quality & Reliability Management, vol. 37 no. 4
Type: Research Article
ISSN: 0265-671X

Keywords

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Article
Publication date: 1 June 1991

Michael Thompson and Yunus Kathawala

This article evaluates maintenance management in an electric utility setting. It begins with a historical review before discussing present pressures on management and the…

Abstract

This article evaluates maintenance management in an electric utility setting. It begins with a historical review before discussing present pressures on management and the new importance of maintenance costs. It discusses remedial, preventive and predictive maintenance, and the implications of each one of these maintenance functions.

Details

International Journal of Quality & Reliability Management, vol. 8 no. 6
Type: Research Article
ISSN: 0265-671X

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Article
Publication date: 23 March 2012

Selim Zaim, Ali Turkyılmaz, Mehmet F. Acar, Umar Al‐Turki and Omer F. Demirel

The purpose of this paper is to demonstrate the use of two general purpose decision‐making techniques in selecting the most appropriate maintenance strategy for…

Abstract

Purpose

The purpose of this paper is to demonstrate the use of two general purpose decision‐making techniques in selecting the most appropriate maintenance strategy for organizations with critical production requirements.

Design/methodology/approach

The Analytical Hierarchical Process (AHP) and the Analytical Network Process (ANP) are used for the selection of the most appropriate maintenance strategy in a local newspaper printing facility in Turkey.

Findings

The two methods were shown to be effective in choosing a strategy for maintaining the printing machines. The two methods resulted in almost the same results. Both methods take into account the specific requirements of the organization through its own available expertise.

Practical implications

The techniques demonstrated in this paper can be used by all types of organizations for selecting and adopting maintenance strategies that have higher impact on maintenance performance and hence overall business productivity. The two methods are explained in a step‐by‐step approach for easier adaptation by practitioners in all types of organizations.

Originality/value

The value of the paper is in applying AHP and ANP decision‐making methodologies in maintenance strategy selection. These two methods are not very common in the area of maintenance, and hence add to the pool of techniques utilized in selecting maintenance strategies.

Details

Journal of Quality in Maintenance Engineering, vol. 18 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

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Article
Publication date: 9 July 2020

James Wakiru, Liliane Pintelon, Peter Muchiri and Peter Chemweno

The purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded…

Abstract

Purpose

The purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set.

Design/methodology/approach

The DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models.

Findings

The results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs.

Practical implications

The proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.

Originality/value

Advances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS.

Details

Journal of Quality in Maintenance Engineering, vol. 27 no. 2
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 17 July 2020

Frank Koenig, Pauline Anne Found, Maneesh Kumar and Nicholas Rich

The aim of this paper is to develop a contribution to knowledge that adds to the empirical evidence of predictive condition-based maintenance by demonstrating how the…

Abstract

Purpose

The aim of this paper is to develop a contribution to knowledge that adds to the empirical evidence of predictive condition-based maintenance by demonstrating how the availability and reliability of current assets can be improved without costly capital investment, resulting in overall system performance improvements

Design/methodology/approach

The empirical, experimental approach, technical action research (TAR), was designed to study a major Middle Eastern airport baggage handling operation. A predictive condition-based maintenance prototype station was installed to monitor the condition of a highly complex system of static and moving assets.

Findings

The research provides evidence that the performance frontier for airport baggage handling systems can be improved using automated dynamic monitoring of the vibration and digital image data on baggage trays as they pass a service station. The introduction of low-end innovation, which combines advanced technology and low-cost hardware, reduced asset failures in this complex, high-speed operating environment.

Originality/value

The originality derives from the application of existing hardware with the combination of edge and cloud computing software through architectural innovation, resulting in adaptations to an existing baggage handling system within the context of a time-critical logistics system.

Details

Journal of Manufacturing Technology Management, vol. 32 no. 3
Type: Research Article
ISSN: 1741-038X

Keywords

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Article
Publication date: 15 September 2020

Wieger Tiddens, Jan Braaksma and Tiedo Tinga

Asset owners and maintainers need to make timely and well-informed maintenance decisions based on the actual or predicted condition of their physical assets. However, only…

Abstract

Purpose

Asset owners and maintainers need to make timely and well-informed maintenance decisions based on the actual or predicted condition of their physical assets. However, only few companies have succeeded to implement predictive maintenance (PdM) effectively. Therefore, this paper aims to identify why only few companies were able to successfully implement PdM.

Design/methodology/approach

A multiple-case study including 13 cases in various industries in The Netherlands was conducted. This paper examined the choices made in practice to achieve PdM and possible dependencies between and motivations for these choices.

Findings

An implementation process for PdM appeared to comprise four elements: a trigger, data collection, maintenance technique (MT) selection and decision-making. For each of these elements, several options were available. By identifying the choices made by companies in practice and mapping them on the proposed elements, logical combinations appeared. These combinations can provide insight into the PdM implementation process and may also lead to guidance on this topic. Further, while successful companies typically combined various techniques, the mostly applied techniques were still those based on previous experiences.

Research limitations/implications

This research calls for better methods or procedures to guide the selection and use of suitable types of PdM, directed by the firm's ambition level and the available data.

Originality/value

While it is important for firms to make suitable choices during implementation, the literature often focusses only on developing additional techniques for PdM. This paper provides new insights into the application and selection of techniques for PdM in practice and helps practitioners reduce the often applied trial-and-error process.

Details

Journal of Quality in Maintenance Engineering, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-2511

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Article
Publication date: 4 July 2020

Miguel Angel Navas, Carlos Sancho and Jose Carpio

The purpose of this paper is to present a new disruptive maintenance model based on new technologies.

Abstract

Purpose

The purpose of this paper is to present a new disruptive maintenance model based on new technologies.

Design/methodology/approach

The approach is carrying out through the impact of the Industry 4.0, Internet of things, big data, virtual reality and additive manufacturing on maintenance.

Findings

The findings are that new technologies are an evolutionary challenge that is immediately affecting maintenance engineering. It presents a unique opportunity to make a disruptive evolution of maintenance.

Research limitations/implications

The correct development of Maintenance 4.0 relates to the correct implementation of Industry 4.0.

Practical implications

Maintenance 4.0 will greatly improve the main operating indicators: safety, reliability, availability and cost.

Social implications

Maintenance 4.0 will contribute to a circular and sustainable economy.

Originality/value

For the first time, a complete new Maintenance Engineering 4.0 model is proposed. The application of the new technologies appears in each specific maintenance process of the product life cycle.

Details

International Journal of Quality & Reliability Management, vol. 37 no. 6/7
Type: Research Article
ISSN: 0265-671X

Keywords

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