Fault diagnosis method of rolling bearing based on improved deep convolutional neural network

Rolling bearings occupy a pivotal position as one of the most crucial and frequently used components in machinery and equipment. Traditionally, diagnosing faults in these bearings involved the reduction of the original signal's dimension and the extraction of a limited set of characteristic values from the vibration signal. However, this conventional approach often overlooked vital fault information embedded within the original signal. To overcome this limitation, this paper introduces a groundbreaking approach that seamlessly integrates the Improved Deep Convolutional Neural Network (IDCNN) with the Continuous Wavelet Transform (CWT) method. This innovative hybrid technique facilitates a comprehensive multiscale analysis of vibration signals, generating wavelet time-frequency domain graphs that effectively highlight fault features. Additionally, a three-channel multi-scale convolutional network is utilized to learn from these diverse fault features, enabling precise fault identification. To validate the superiority of this proposed model, experiments were conducted using the bearing dataset from Case Western Reserve University. The findings indicate that, in comparison to traditional neural networks and deep learning technologies, the model proposed in this paper demonstrates superior performance in extracting bearing fault features and diagnosing faults, ultimately achieving an average recognition accuracy of 99.50%.