A confirmation technique for predictive maintenance using the Rough Set Theory

This paper presents a technique to improve the accuracy of the predictions obtained using the Rough Set Theory (RST) in non-deterministic cases (rough cases). The RST is here applied to the data collected by the Intelligent Field Devices for identifying predictive diagnostic algorithms for machinery, plants, subsystems, or components. The data analysis starts from a historical data set recorded from the field instruments, and its final result is a set of “if–then” rules identifying predictive maintenance functions. These functions may be used to predict if a component is going to fail or not in the next future. The prediction is obtained by applying the rules extracted with the RST algorithm on the real-time values transmitted by the field device. It may happen that some diagnoses are uncertain, in the sense that it is not possible to take a certain decision (device sound or close to fail) with a given set of data. In this paper, a new algorithm for increasing the confidence in these uncertain cases is presented. To show an example, the proposed confirmation algorithm is applied to the predictive algorithms obtained for an intelligent pressure transmitter.