K-means clustering pre-analysis for fault diagnosis in an aluminium smelting process

Developing a fault detection and diagnosis system of complex processes usually involve large volumes of highly correlated data. In the complex aluminium smelting process, there are difficulties in isolating historical data into different classes of faults for developing a fault diagnostic model. This paper presents a new application of using a data mining tool, k-means clustering in order to determine precisely how data corresponds to different classes of faults in the aluminium smelting process. The results of applying the clustering technique on real data sets show that the boundary of each class of faults can be identified. This means the faulty data can be isolated accurately to enable for the development of a fault diagnostic model that can diagnose faults effectively.