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- Title
Tensor sequence component analysis for fault detection in dynamic process.
- Authors
Huang, Pengfei; Tao, Yang; Song, Bing; Shi, Hongbo; Tan, Shuai
- Abstract
For dynamic processes, using sequence information to augment the data can improve fault detection performance. Traditional approaches transform raw data into augmented vectors, which leads to losses in structural information in the variables and increases the data dimension. This paper proposes a novel data dimension reduction algorithm called tensor sequence component analysis (TSCA) and applies it to dynamic process fault detection. The algorithm extends each sample into a matrix comprising current and past process data, and simultaneously reduces the dimensions of time delay and the variables for feature extraction, solving the problem of the curse of dimensionality. For the dimension reduction of time delay, in order to extract similar information from the samples, each sample is reconstructed with time neighbourhoods. For the dimension reduction of the variables, considering the information of different variables variance information of the latent variables is maximized for feature extraction. Finally, a numerical example and the Tennessee Eastman process are used to demonstrate the efficacy of the proposed method.
- Subjects
SEQUENCE analysis; LATENT variables; FEATURE extraction; VECTOR data; DATA reduction; TIME delay systems
- Publication
Canadian Journal of Chemical Engineering, 2020, Vol 98, Issue 1, p225
- ISSN
0008-4034
- Publication type
Article
- DOI
10.1002/cjce.23576