Abstract

The feature extraction and selection are the most important part of any fault detection and diagnosis system. Especially when it comes to the Machine Learning (ML), improper feature extraction and selection can cause poor classification accuracy. ML’s key challenge is the tedious process of manually extracting the features which require expert knowledge, and it is time-consuming. ML-based classifier might be less accurate than Deep Learning (DL) without proper discriminant feature extraction and selection. However, if the extraction and selection of the features are performed correctly with the knowledge about the type of input data being utilized, greater classification accuracy can be achieved.
In this work, we adopt an approach where we extract two types of features from the decomposed wavelets of the original current signals recorded from a robot test bench. The first type of features is solely based on the wavelet characteristics and it focuses on the wavelet domain whereas the second type of features is extracted based on the statistical analysis. The reason to extract different types of features is that in typical analysis methodologies either the first type of feature is utilized or the second type. Also, it is dependent on the type of fault being diagnosed. In the case of Rotate Vector (RV) reducer fault detection and diagnosis, due to the high sensitivity of the fault, the typical feature extraction methodologies fail to provide higher classification accuracy. Therefore, we developed a method where we extract features based on both of the above-mentioned types and implement deterministic feature selection to choose the most prominent features among them. Doing so provides the power to utilize the properties of wavelet and statistical domain simultaneously in an efficacious way. The proposed approach showed satisfying results regarding the fault detection and diagnosis for RV reducer with a classification accuracy of 96.7%.