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- W290462016 abstract "In statistical fault isolation, contribution plots are most commonly used tools, whose results are often influenced by the smearing effect. To solve this problem, reconstruction-based methods were developed. Unfortunately, the conventional reconstruction-based methods rely on the knowledge of fault directions or abundant historical fault data which are seldom available in industrial applications. The branch and bound (B&B) algorithm can be adopted to relieve such limitation. However, the computational burden of B&B is usually heavy, especially for a large number of variables. In this paper, a fault isolation method based on variable selection is proposed to overcome these shortcomings of the existing methods. The fault isolation problem is transformed into a quadratic programming problem with a sparsity constraint, which has a unified form for different monitoring statistics and can be solved efficiently using the least absolute shrinkage and selection operator (LASSO). The effectiveness of this method is illustrated by case studies." @default.
- W290462016 created "2016-06-24" @default.
- W290462016 creator A5000492991 @default.
- W290462016 creator A5038811086 @default.
- W290462016 date "2015-08-01" @default.
- W290462016 modified "2023-09-27" @default.
- W290462016 title "Variable selection method for fault isolation using least absolute shrinkage and selection operator (LASSO)" @default.
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- W290462016 doi "https://doi.org/10.1016/j.chemolab.2015.05.019" @default.
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