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.
Highlights
Data mining methods are tools that combine the techniques of artificial intelligence, statistical analysis, and computer science, namely, databases and graphic visualizations, in order to extract and obtain information that is not explicitly represented in the original data that can be more profitable and interesting
The purpose of data mining is to extract the relevant information from a large amount of data and, build models of information and knowledge based on fixed criteria
Discovering itemset/frequent patterns is an important process for performing other data mining activity processes such as association rule mining, classification, prediction, clustering [7,8]
Summary
Data mining methods are tools that combine the techniques of artificial intelligence, statistical analysis, and computer science, namely, databases and graphic visualizations, in order to extract and obtain information that is not explicitly represented in the original data that can be more profitable and interesting. Data Mining can optimize quality and reduce the scrap by up to 30% [4] It ensures the sequencing of the maintenance activities with the support of information on the spare parts at the base of the history of the maintenance actions. Companies produce and store huge amounts of data of different natures, increasing the difficulty of the use and processing of the data in real time In this context, given the relevance of the data collected in industrial facilities, we seek to propose a forecast model of predictive maintenance activities using data mining techniques by means of this topic.
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