Using Data Mining Methods for Predicting Sequential Maintenance Activities

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...

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Bibliographic Details
Published in:Applied sciences 2018-11, Vol.8 (11), p.2184
Main Authors: Rezig, Sadok, Achour, Zied, Rezg, Nidhal
Format: Article
Language:English
Subjects:
Artificial intelligence
Cost control
Data mining
Machine learning
Maintenance management
Predictive maintenance
sequential pattern
Spare parts
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app8112184