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- W2945598078 abstract "Abstract Nowadays, there are many fault detection approaches, which are taken into consideration in two categories including the data-driven and the model-based fault detection approaches. One of the well-known data-based fault detection techniques is the support vector machine that is proved to be a powerful approach in the classification. This one has weakness in dealing with a variety of complicated data. To address this concern, based on the investigation presented, an integration of the two approaches including the fuzzy and the multi-label SVM is proposed. In a word, the performance-based approach can classify the noisy and the multi-label data. This one is carried out for the dew point process with the cooling cycle in the real-world plant data in correspondence with the simulated plant via the HYSYS software environment. The proposed performance-based approach is about 10% percent more accurate than the conventional multi-label SVM in identifying the faults of the process." @default.
- W2945598078 created "2019-05-29" @default.
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- W2945598078 date "2019-10-01" @default.
- W2945598078 modified "2023-09-26" @default.
- W2945598078 title "Performance-based fault detection approach for the dew point process through a fuzzy multi-label support vector machine" @default.
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- W2945598078 doi "https://doi.org/10.1016/j.measurement.2019.05.036" @default.
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