Recently, almost all modern manufacturing operations rely on automatic tools. These automatic tools react with materials used and physical events. Physical events are determined by data patterns that definable by domain experts. However, when problems that have not predefined occurred, it will cause critical errors especially in manufacturing plants. Furthermore, with machine complexity and new technology, this leads to a huge amount of data. This problem calls for mining extraction of useful knowledge or patterns. Time series in data mining is about solving these problems by applying various approaches that suitable with manufacturing data. Until recently, the detection of time series data has received much less attention because of time series database are usually very large, high dimensionality and the concepts of similarity can be subjective. The similarity may depend on the user, the domain, and the task on hand. As a result, a suitable time series mining is needed for finding any interesting and useful data patterns in manufacturing operation. The intent of this study is to present a broad classification of various time series problem in manufacturing plants. Also, the theoretical developments of these problems are reviewed and to analyse the current available approaches used in time series mining. This study highlights problems in manufacturing areas and also to survey the potential of the time series concepts in manufacturing.
Ruhaizan Ismail, University Kebangsaan Malaysia, Malaysia
Zalinda Othman, University Kebangsaan Malaysia, Malaysia
Azuraliza Abu Bakar, University Kebangsaan Malaysia, Malaysia
Stream: New Realities through Artificial Intelligence
This paper is part of the ACTIS2014 Conference Proceedings (View)
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