Industrial Fault Detection and Classification with the Optimal Windows Size Approach

Various important issues in industrial production processes such as product quality, process safety and supply continuity are diretly related to machine faults that occur in production and distribution stages. In addition to economic losses, machine faults also result in industrial accidents. Early diagnosis of possible faults would cut down possible losses. To date, various solutions on fault detection has been proposed. Existing solutions either detect faults after they occur or misdiagnose them due to complexity caused by operating over multiple measurements. In this study, to the best our knowledge, we propose a supervised model that optimally determines the window size for both fault detection and classification problems. Additionally, in order to determine the features that are more heavily related with the problem, we apply the binary version (BCS) of the nature-inspired Cuckoo Search Algorithm (CSA) for feature selection. Our results indicate that determining the window size appropriately has a significant impact on accuracy and feature selection increases the F-score roughly around 13%.