MRS-kNN fault detection method for multirate sampling process based variable grouping threshold |
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| Affiliation: | 1. Department of Automation, Tsinghua University, Beijing, 100084, China;2. School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, United Kingdom;1. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA;2. Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA;3. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA;4. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, USA |
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| Abstract: | For the multirate sampling process, some traditional multivariate statistical process monitoring methods cannot perform well because the lengths of all samples are not consistent. To handle this problem, a multirate sampling k-nearest neighbor fault detection method is proposed in this paper. The training sample set is divided into different groups according to the length of the sample to ensure that the sample length of each group is uniform. For all the groups, we can get a variable threshold corresponding to samples of different lengths. Also, this model can be developed into one that is suitable for fault detection of various sampling rate processes. Finally, the effectiveness of the proposed method is demonstrated by the simulation experiments on a numerical example and an industrial process. |
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