Adaptive denoising autoencoder for robust fault detection

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...

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Vydané v:Process safety and environmental protection Ročník 188; s. 860 - 876
Hlavní autori: Li, Zixuan, Zhao, Haitao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.08.2024
Predmet:
Denoising autoencoder
Diffusion model
Process monitoring
Robust fault detection
Process monitoring
Robust fault detection
Denoising autoencoder
Diffusion model
ISSN:0957-5820, 1744-3598
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Shrnutí: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.
ISSN:0957-5820
1744-3598
DOI:10.1016/j.psep.2024.05.129