Classification of bearing faults through time-frequency analysis and image processing

Rossetti, Damiano, Zhang, Yu, Squartini, Stefano and Collura, Stefano (2016) Classification of bearing faults through time-frequency analysis and image processing. In: 17th International Conference on Mechatronics – Mechatronika 2016, 7-9 December 2016, Prague, Czech Republic.

ROSSETTI_ID86_ME2016.pdf - Whole Document

Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive


The present work proposes a new technique for bearing fault classification that combines time-frequency analysis with image processing. This technique uses vibration signals from bearing housings to detect bearing conditions and classify the faults. By means of Empirical Mode Decomposition (EMD), each vibration signal is decomposed into Intrinsic Mode Functions (IMFs). Principal Components Analysis (PCA) is then performed on the matrix of the decomposed IMFs and the important principal components are chosen. The spectrogram is obtained for each component by means of the Short Time Fourier Transform (STFT) to obtain an image that represents the time-frequency relationship of the main components of the analyzed signal. Furthermore, Image Moments are extracted from the spectrogram images of principal components in order to obtain an array of features for each signal that can be handled by the classification algorithm. 8 images are selected for each signal and 17 moments for each image, so an array of 136 features is associated with every signal. Finally, the classification is performed using a standard machine learning technique, i.e. Support Vector Machine (SVM), in the proposed technique. The dataset used in this work include data collected for various rotating speeds and loads, in order to obtain a set of different operating conditions, by a Roller Bearing Faults Simulator. The results have shown that the developed technique provides classification effectively, with a single classifier, of bearing faults characterized by different rotating speeds and different loads.

Keywords:Empirical Mode Decomposition;, Principal Component Analysis;, Spectral Analysis;, Image Processing, Machine Learning
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
H Engineering > H320 Mechanisms and Machines
Divisions:College of Science > School of Engineering
ID Code:25219
Deposited On:22 Nov 2016 15:12

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