Increasing availability of industrial systems through data stream mining

Improving industrial product reliability, maintainability and thus availability is a challenging task for many industrial companies. In industry, there is a growing need to process data in real time, since the generated data volume exceeds the available storage capacity. This paper consists of a review of data stream mining and data stream management systems aimed at improving product availability. Further, a newly developed and validated grid-based classifier method is presented and compared to one-class support vector machine (OCSVM) and a polygon-based classifier. The results showed that, using 10% of the total data set to train the algorithm, all three methods achieved good (>95% correct) overall classification accuracy. In addition, all three methods can be applied on both offline and online data. The speed of the resultant function from the OCSVM method was, not surprisingly, higher than the other two methods, but in industrial applications the OCSVMs' comparatively long time needed for training is a possible challenge. The main advantage of the grid-based classification method is that it allows for calculation of the probability (%) that a data point belongs to a specific class, and the method can be easily modified to be incremental. The high classification accuracy can be utilized to detect the failures at an early stage, thereby increasing the reliability and thus the availability of the product (since availability is a function of maintainability and reliability). In addition, the consequences of equipment failures in terms of time and cost can be mitigated. (C) 2010 Elsevier Ltd. All rights reserved.