  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 Network With Attention (TDANet), designed to
improve fault diagnosis performance in noise environments. This model
transforms one-dimensional signals into two-dimensional tensors based on their
periodic properties, employing multi-scale 2D convolution kernels to extract
signal information both within and across periods. This method enables
effective identification of signal characteristics that vary over multiple time
scales. The TDANet incorporates a Temporal Variable Denoise (TVD) module with
residual connections and a Multi-head Attention Fusion (MAF) module, enhancing
the saliency of information within noisy data and maintaining effective fault
diagnosis performance. Evaluation on two datasets, CWRU (single sensor) and
Real aircraft sensor fault (multiple sensors), demonstrates that the TDANet
model significantly outperforms existing deep learning approaches in terms of
diagnostic accuracy under noisy environments.
