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Chen, Junghui
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Computer Science & IT

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Journal : International Journal of Advances in Intelligent Informatics

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Fault diagnosis-based SDG transfer for zero-sample fault symptom Yu, Mengqin; Lee, Yi Shan; Chen, Junghui
International Journal of Advances in Intelligent Informatics Vol 9, No 3 (2023): November 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i3.1434

Abstract

The traditional fault diagnosis models cannot achieve good fault diagnosis accuracy when a new unseen fault class appears in the test set, but there is no training sample of this fault in the training set. Therefore, studying the unseen cause-effect problem of fault symptoms is extremely challenging. As various faults often occur in a chemical plant, it is necessary to perform fault causal-effect diagnosis to find the root cause of the fault. However, only some fault causal-effect data are always available to construct a reliable causal-effect diagnosis model. Another worst thing is that measurement noise often contaminates the collected data. The above problems are very common in industrial operations. However, past-developed data-driven approaches rarely include causal-effect relationships between variables, particularly in the zero-shot of causal-effect relationships. This would cause incorrect inference of seen faults and make it impossible to predict unseen faults. This study effectively combines zero-shot learning, conditional variational autoencoders (CVAE), and the signed directed graph (SDG) to solve the above problems. Specifically, the learning approach that determines the cause-effect of all the faults using SDG with physics knowledge to obtain the fault description. SDG is used to determine the attributes of the seen and unseen faults. Instead of the seen fault label space, attributes can easily create an unseen fault space from a seen fault space. After having the corresponding attribute spaces of the failure cause, some failure causes are learned in advance by a CVAE model from the available fault data. The advantage of the CVAE is that process variables are mapped into the latent space for dimension reduction and measurement noise deduction; the latent data can more accurately represent the actual behavior of the process. Then, with the extended space spanned by unseen attributes, the migration capabilities can predict the unseen causes of failure and infer the causes of the unseen failures. Finally, the feasibility of the proposed method is verified by the data collected from chemical reaction processes.
A novel multi-step prediction model for process monitoring Lee, Yi Shan; Ooi, Sai Kit; Chen, Junghui
International Journal of Advances in Intelligent Informatics Vol 10, No 2 (2024): May 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v10i2.1528

Abstract

In the competitive market, process monitoring can ensure the quality of products, but strong nonlinearities, slow dynamics, and uncertainties characterize the complexities of the large-scale chemical plant. When the fault occurs, it will not influence the process instantaneously but will react after a few time points. After all the products affected by the faults are inspected, it is too late to fix the process. Conventional approaches neither do nor care about early detection before any disturbance significantly affects the process. To estimate disturbances propagated through the process, a multi-step prediction model is essential. The purpose of early process monitoring is to detect any problem with the currently running process as early as possible. In this paper, a multi-step prediction system is proposed. The system is a dynamic model that can capture the dynamic relationship of past process input variables and future process output variables. It provides a lower dimension and a lower noise-contaminated space for data analysis. Particularly, the past input and output process data can be mapped from the observation space into the latent space to acquire their intrinsic properties. The latent variables preserve the dynamic information for future multi-step prediction so that early warning can be achieved. An industrial example of the PVC dying process is presented to show the multistep predictive ability of the proposed method.
Co-Authors Lee, Yi Shan Ooi, Sai Kit Yu, Mengqin