A Fault Diagnosis Approach Based on 2D-Vibration Imaging for Bearing Faults

Background The widely used rolling element bearings in rotating machines undergo progressive degradation with continuous operation. To identify bearing faults, complex time-frequency based signal processing techniques and high-end deep neural network algorithms have been used to perform fault classification, which is time-consuming. Method In this paper, the focus was given to replace the complex time-frequency domain signal processing techniques by incorporating a simple time-domain based methodology. Initially, the vibration signature of different bearing faults was acquired at three different speeds and was directly converted into images by 2D-Vibration Imaging (2D-VI) technique using an overlapping-based moving window. The extracted images were fed into Convolutional Neural Network (CNN) for automatic feature extraction, followed by classification using Support Vector Machine (SVM). Results and validation Separately, time-frequency spectrums were also extracted to compare the effectiveness of the proposed methodology. Furthermore, the proposed methodology was validated on the bearing dataset of combined faults and Case Western Reserve University (CWRU). Conclusion The experimental results showed that the proposed methodology has the potential to replace the conventional approach by consuming less computational time without affecting classification accuracy.

Automated summary

This paper proposes a time-domain based approach for identifying rolling element bearing faults. By converting vibration signatures into images and using a Convolutional Neural Network for feature extraction and Support Vector Machine for classification, faults can be accurately identified in a shorter amount of time.