TDANet: A Novel Temporal Denoise Convolutional Neural Network With Attention for Fault Diagnosis

arXiv:2403.19943v1 Announce Type: new
Abstract: Fault diagnosis plays a crucial role in maintaining the operational integrity of mechanical systems, preventing significant losses due to unexpected failures. As intelligent manufacturing and data-driven approaches evolve, Deep Learning (DL) has emerged as a pivotal technique in fault diagnosis research, recognized for its ability to autonomously extract complex features. However, the practical application of current fault diagnosis methods is challenged by the complexity of industrial environments. This paper proposed the Temporal Denoise Convolutional Neural …

abstract arxiv attention convolutional neural network cs.ai cs.lg data data-driven deep learning diagnosis eess.sp integrity intelligent losses manufacturing network neural network novel pivotal research role systems temporal type