Preprocessing of alarm data for data mining

Many industries, including process industry, face an increased number of alarms every day. This is due to advanced computer-based monitoring and control technologies that are widely available in all industrial plants. On the other hand, data mining, as a method of finding patterns in data, has been widely used to discover patterns and relationships in alarm data, in hopes of reducing the volume of alarms and operators' workload. One of the first steps in data mining is to prepare and cleanup raw data for better mining, also known as preprocessing. In this paper, we focus on preprocessing of alarm data and investigate the steps required for data preparation. Two steps - namely, removing chattering alarms and reconstruction of missing alarms - are more challenging. For chattering alarms, many algorithms are proposed with a discussion on the time frame that should be selected for removing chattering alarms. As for the reconstruction of missing alarms, two methods are presented, using information from the same alarm tag or other related alarms. A case study shows the efficiency of the proposed methods.