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Appl. Sci. 2018, 8(11), 2184; https://doi.org/10.3390/app8112184

Using Data Mining Methods for Predicting Sequential Maintenance Activities

Laboratoire de Génie Informatique, de Production et de Maintenance, UFR MIM, Université de Lorraine, 57000 Metz, France
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Received: 11 October 2018 / Revised: 29 October 2018 / Accepted: 31 October 2018 / Published: 7 November 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

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. View Full-Text
Keywords: data mining; predictive maintenance; sequential pattern; spare parts data mining; predictive maintenance; sequential pattern; spare parts
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Rezig, S.; Achour, Z.; Rezg, N. Using Data Mining Methods for Predicting Sequential Maintenance Activities. Appl. Sci. 2018, 8, 2184.

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