Group-sparsity-enforcing fault discrimination and estimation with dynamic process data
| Autor: | Hao Ye, Liang Zhao, Xiaolin Huang, Chao Shang, Dexian Huang |
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| Rok vydání: | 2021 |
| Předmět: |
Optimization problem
Computer science Process (computing) Root cause Fault (power engineering) computer.software_genre Industrial and Manufacturing Engineering Computer Science Applications Variable (computer science) Discriminative model Discriminant Control and Systems Engineering Modeling and Simulation Data mining computer Interpretability |
| Zdroj: | Journal of Process Control. 105:236-249 |
| ISSN: | 0959-1524 |
| Popis: | Fisher discriminative analysis (FDA) has been recognized a prototypical approach to fault classification and diagnosis. To enhance model performance with time-series data used, it is customary to encompass lag measurements into the model. This not only increases model complexity prohibitively but also reduces the interpretability of fault diagnosis strategies. To address this issue, we propose a novel group-sparsity-enforcing FDA model, which utilizes reweighted group Lasso penalty to prune out irrelevant variables at the variable level so as to improve interpretability of discriminant directions. A tailored algorithm based on alternating direction method of multipliers is developed to solve the non-smooth and non-convex optimization problem. In addition, to identify root cause variables and unveil the fault evolution over time, a sparse fault estimation approach based on reweighted group Lasso is developed. This eventually allows to develop a holistic online scheme yielding informative diagnostic verdicts with faulty variable information used. Experimental results demonstrate that, the proposed model significantly improves the discriminant capability between normal and faulty data, and yields more interpretable discriminative information than conventional methods using dynamic process data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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