Vibration Signal Analysis and Fault Detection of Mechanical System Based on Deep Learning

This study introduces a Hybrid Deep Convolutional Neural Network (HDCNN) for advanced vibration signal analysis and fault detection in mechanical systems. HDCNN combines One Dimensional Convolutional Neural Network (1DCNN), Two-Dimensional Convolutional Neural Network (2DCNN), and Deep Neural Networks (DNN) to enhance fault diagnostic efficiency. The 1DCNN is used for regression tasks like analyzing machining surface roughness, while 2DCNN handles classification tasks such as tool wear and bearing fault diagnosis using time-frequency images from short time Fourier transform (STFT). DNN integrates features from 1DCNN and 2DCNN for effective regression and fault classification. Experimental results show that HDCNN outperforms existing techniques like CNN, DNN, FANN, PSONN, and GANN in diagnostic accuracy, convergence speed, and stability. This research highlights HDCNN as a powerful tool for predictive maintenance and real-time fault identification in mechanical systems, showcasing the advantages of deep learning in mechanical fault diagnostics.