Utilizing data mining to influence maintenance actions

Young, T; Fehskens, M.; Pujara, Paavan; Burger, Michael; Edwards, Gail

doi:10.1109/autest.2010.5613610

Cited by 16 publications

(7 citation statements)

References 2 publications

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“…The model studied historical failure data and recommended an appropriate policy with optimal preventive maintenance intervals. The aircraft maintenance data are analyzed in Reference [21], which discovered that the parameters link failures, diagnoses, and repair actions in order to enhance the maintenance practices. In Reference [22], the authors suggested a neural-network-based prediction model for assessing the risk priority of medical equipment.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities

2018

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A data mining approach is integrated in this work for predictive sequential maintenance along with information on spare parts based on the history of the maintenance data. For most practical problems, the simple failure of one part of a given piece of equipment induces the subsequent failure of the other parts of said equipment. For example, it is frequently observed in mining industries that, like many other industries, the maintenance of conventional equipment is carried out in sequence. Besides, depending on the state of parts of the equipment, many parts can be consumed and replaced. Consequently, with a group of spare parts consumed sequentially in various maintenance activities, it is possible to discover sequential maintenance activities. From maintenance data with predefined support or threshold values and spare parts information, this work determines the sequential patterns of maintenance activities. The proposed method predicts the occurrence of the next maintenance activity with information on the consumed spare parts. An industrial real case study is presented in this paper and it is well-noticed that our experimental results shed new light on the maintenance prediction using data mining.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities

2018

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“…In the study by Ibukun et al, the issue faced was the inadequacy knowledge of DM's terminologies (Afolabi et al, 2016). The technical aspect as encountered by Young, Fehskens, Pujara, Burger, and Edwards (2010) was the delay in the selection of the DM technique. When the selected DM techniques do not correspond with the selected data format, incompatibility will occur, causing the user to return again to the data analysis stage.…”

Section: Strengths and Limitations Of Models In The Sme Contextmentioning

confidence: 99%

Big data analytics—A review of data‐mining models for small and medium enterprises in the transportation sector

Selamat

Prakoonwit

Sahandi

et al. 2018

WIREs Data Min & Knowl

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The need for small and medium enterprises (SMEs) to adopt data analytics has reached a critical point, given the surge of data implied by the advancement of technology. Despite data mining (DM) being widely used in the transportation sector, it is staggering to note that there are minimal research case studies being done on the application of DM by SMEs, specifically in the transportation sector. From the extensive review conducted, the three most common DM models used by large enterprises in the transportation sector are identified, namely “Knowledge Discovery in Database,” “Sample, Explore, Modify, Model and Assess” (SEMMA), and “CRoss Industry Standard Process for Data Mining” (CRISP‐DM). The same finding was revealed in the SMEs' context across the various industries. It was also uncovered that among the three models, CRISP‐DM had been widely applied commercially. However, despite CRISP‐DM being the de facto DM model in practice, a study carried out to assess the strengths and weakness of the models reveals that they have several limitations with respect to SMEs. This paper concludes that there is a critical need for a novel model to be developed in order to cater to the SMEs' prerequisite, especially so in the transportation sector context. This article is categorized under: Application Areas > Business and Industry Application Areas > Industry Specific Applications

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“…Numerous studies have been carried out to develop a predictive maintenance model that can be used to diagnose equipment health in production lines. Young et al [20] evaluated the use of data mining algorithms on an F-18 Aircraft to improve maintenance procedures. The proposed Data mining model consisted of diagnoses and failures codes along with repair details to design a predictive maintenance model to optimize aircraft run time and reduce the risk of any hazardous technical errors.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

Kohli¹

2018

IJET

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Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations.

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Utilizing data mining to influence maintenance actions

Cited by 16 publications

References 2 publications

RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities

RETRACTED: Using Data Mining Methods for Predicting Sequential Maintenance Activities

Big data analytics—A review of data‐mining models for small and medium enterprises in the transportation sector

Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

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