Classification of present faults in rotating machinery based on time and frequency domain feature extraction

The need for an effective and efficient maintenance process increases with the level of complexity of modern rotating machinery. This paper introduces a methodology for transforming raw vibration signals into adequate inputs for machine learning classification algorithms in order to identify present faults in rotating machinery. It complements a previous study by the same authors, which covers the processing of vibrational signals by determining the optimal sampling frequency and using appropriate filters for the raw data. The first part of this study covers feature extraction using time and frequency-domain techniques and correlation matrices are plotted to determine which extracted features are significantly connected and what is the level of their correlation. The study continues with the use of Neighborhood Component Analysis (NCA) where weight factors of the features are calculated in terms of recognizing the present rotating machinery faults. Only the ones with the highest level of importance have been used as input for the classification algorithms. The MATLAB Add-in Classification Learner has been used for training and testing various classification algorithms. K-nearest neighbors classifier (KNN), Support vector machines (SVM), and Wide Neural Network (NN) showed the highest accuracy in distinguishing ten different fault conditions. For this case study, the MaFaulda vibration dataset has been used and ten operating conditions have been considered: normal, imbalance, horizontal misalignment, vertical misalignment, and three faults in the underhang bearing and the overhang bearing.