Fault Detection Based on Variational Autoencoders for Complex Nonlinear Processes | IEEE Conference Publication | IEEE Xplore

Fault Detection Based on Variational Autoencoders for Complex Nonlinear Processes


Abstract:

Deep learning models have been proved to outperform shallow methods for industrial process fault detection because of their high capacity for complex nonlinearity. Howeve...Show More

Abstract:

Deep learning models have been proved to outperform shallow methods for industrial process fault detection because of their high capacity for complex nonlinearity. However, typical deep models applied to monitoring processes are conducted in a deterministic manner. They are unable to provide a confidence level for each decision. Also, most deep learning methods often need to integrate prior conditions, such as orthogonal latent variables, constraints, and some given distributions. The consequences of these issues cause lots of trials and errors as conventional deep models are built based on experiences. In this paper, a variational auto-encoder is used to set up a framework to tackle these problems. The learned latent variables, which would be orthogonal to each other, are constrained under the specified and optimized objective. Simultaneously, considering uncertainty in data, probability density estimates of latent variables and residuals instead of point estimates are given to design distribution based monitoring indices. A numerical example validates the effectiveness of the proposed method.
Date of Conference: 09-12 June 2019
Date Added to IEEE Xplore: 18 July 2019
ISBN Information:
Conference Location: Kitakyushu, Japan

References

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