Mining association rules from time series to explain failures in a hot-dip galvanizing steel line

This paper presents an experience based on the use of association rules from multiple time series captured from industrial processes. The main goal is to seek useful knowledge for explaining failures in these processes. An overall method is developed to obtain association rules that represent the repeated relationships between pre-defined episodes in multiple time series, using a time window and a time lag. First, the process involves working in an iterative and interactive manner with several pre-processing and segmentation algorithms for each kind of time series in order to obtain significant events. In the next step, a search is made for sequences of events called episodes that are repeated among the various time series according to a pre-set consequent, a pre-established time window and a time lag. Extraction is then made of the association rules for those episodes that appear many times and have a high rate of hits. Finally, a case study is described regarding the application of this methodology to a historical database of 150 variables from an industrial process for galvanizing steel coils.

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