Industrial Fault Detection Based on Time-Frequency Distillation Autoencoder

Data-driven feature extraction is a crucial research area in control loop performance assessment (CLPA). Deep learning is a widely used technique for building feature learning models based on neural networks (NNs). However, most NN-based CLPA methods require a large amount of labeled data and do not fully leverage the potential of frequency features. We propose a novel model called time-frequency distillation autoencoder (TFDAE) to address these limitations. The TFDAE consists of a frequency distillation encoder and a representation extraction decoder. The encoder leverages self-supervised contrastive learning to learn time features that guide the distillation of key frequency information. Additionally, a multi-kernel pooling block is incorporated in the encoder, enabling multi-scale information refinement for time feature extraction. The decoder uses the distilled information to extract informative representations and reconstruct the original input series. Taking valve stiction detection in CLPA as the evaluation task, we developed a stiction detection method based on TFDAE. Finally, We evaluate our model on the benchmark dataset: International Stiction Data Base (ISDB), and the experimental results show that TFDAE outperforms traditional knowledge-based and recent NN-based methods.