Enhanced fault feature extraction and bearing fault diagnosis using shearlet transform and deep learning

Accurate bearing fault diagnosis is essential for ensuring the health and longevity of mechanical systems. Traditional methods often struggle with the dynamic operating conditions of machinery, including variations in speed, load, and noise. This paper proposes a novel deep learning-based approach for robust bearing fault diagnosis. The method utilizes a combination of Shearlet Transform, Autoencoder, and Softmax Classifier. Vibration signals from healthy and faulty bearings are transformed into 2D image representations, capturing intricate details of the underlying mechanical state. Shearlet Transform is then employed to enhance these images, specifically targeting and amplifying subtle fault signatures, leading to improved diagnostic accuracy. The enhanced images are subsequently fed to an autoencoder, where the encoder compresses the data into a lower-dimensional feature space. These compressed features are then used to train and optimize a Softmax Classifier for effective fault classification. The proposed methodology is evaluated under diverse speed and load conditions, mimicking real-world operating scenarios. The achieved high classification accuracy across various operating points demonstrates the robustness and effectiveness of the proposed approach.