文献类型：期刊文献

英文题名：Adaptive denoising autoencoder for robust fault detection

作者：Li, Zixuan[1];Zhao, Haitao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Automat Dept, Shanghai 200237, Peoples R China

年份：2024

卷号：188

起止页码：860

外文期刊名：PROCESS SAFETY AND ENVIRONMENTAL PROTECTION

收录：;EI(收录号：20242416234351);WOS:【SCI-EXPANDED(收录号:WOS:001252462300001)】；

基金：Acknowledgements This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 62173143.

语种：英文

外文关键词：Process monitoring; Robust fault detection; Denoising autoencoder; Diffusion model

摘要：In real chemical processes, the collected data is often subject to interference from ambient environmental noise, resulting in a decline in the detection performance. Although denoising autoencoder can realize robust fault detection to a certain extent, its performance is limited by the addition of artificial noise, which refers to Gaussian noise or the dropout noise. Moreover, the artificial noise added is often far from the distribution of real data, making it difficult to suppress the noise in real data. To address this issue, an adaptive denoising autoencoder (ADAE) based on the diffusion model is proposed. ADAE adopts a chain -like structure, concatenating traditional DAEs by replacing the manually injected noise in each DAE block with noise generated by diffusion model at various diffusion steps. In order to better capture the key features of the original process data, a novel noise prediction network is proposed within the diffusion model, utilizing dilated and gated convolutions to capture the dynamic information of the process data. Ultimately, adaptation is achieved through a chain of DAEs, suppressing noise similar to the distribution of the original data. The superiority of ADAE in noise suppression is then verified through numerical examples and the Tennessee Eastman process (TEP), comparing it with other methods.