An Efficient Sequential Embedding ConvNet for Rotating Machinery Intelligent Fault Diagnosis

Effective monitoring of critical components of rotating machinery can bring considerable economic benefits and ensure personal safety. Effective information in mechanical vibration signals is, however, usually submerged by noise interference and nonstationary operating conditions. This causes constructed time-series vibration samples to produce a temporal covariate shift (TCS) phenomenon, resulting in differences in the distribution of the training and testing sets. By focusing on this issue, this study proposes an efficient sequential embedding ConvNet (SECN), which can be mainly divided into three layers. Sequential embedding (SE) is first proposed to reduce the distribution differences between samples by trainable linear dimensional boosting, where SE aims to realize the secondary representation of samples information distribution via the expansion of a low-dimensional space to a high-dimensional space. Then a single layer of multiscale separable convolution is introduced to extract information over different perceptual fields. Finally, the fully connected layer is synthesized by global average pooling to complete the classification task. Experiments and comparisons on three datasets verified that SECN has obvious advantages in terms of accuracy, noise immunity, and sample number dependence. The key principles of the proposed SE are explored by ablation and quantitative experiments. The SE applied in the proposed SECN model shows great potential as a basic framework for intelligent fault diagnosis models.