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Fault detection based on improved local entropy locality preserving projections in multimodal processes
Author(s) -
Guo Jinyu,
Wang Xin,
Li Yuan
Publication year - 2019
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.3116
Subject(s) - locality , fault detection and isolation , computer science , entropy (arrow of time) , artificial intelligence , gaussian , pattern recognition (psychology) , data mining , gaussian process , philosophy , linguistics , physics , quantum mechanics , actuator
Recently, local modelling methods have drawn increasing attention for fault detection in industrial processes. However, current local modelling adopts the basic multivariate statistical process monitoring method without deep mining the internal statistical information of the process data. Locality preserving projection (LPP) is a typical local fault detection method. However, industrial processes typically have complex multimodal characteristics. In these situations, the traditional LPP method performs poorly due to its assumption that the process data are Gaussian and unimodal. To improve fault detection performance of LPP in multimodal industrial processes and mine the internal system information, this paper proposes a new fault detection method based on improved local entropy LPPs (ILELPPs). ILELPP first eliminates the multimodal and non‐Gaussian characteristics of the original data using the improved local entropy method. Then, LPP is applied to the preprocessed data, neglecting the influences of multimodal and non‐Gaussian characteristics. A multimodal numerical example and an industrial application in a semiconductor manufacturing process are used to verify the effectiveness of the proposed method. The simulation results demonstrate that the proposed method has better fault detection performance than the LPP, global entropy LPP, and local entropy LPP methods.