Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 64, Issue 6, Pages 4866-4875Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2017.2668987
Keywords
Feature extraction; locality preserving projection (LPP); non-Gaussian process; performance monitoring; sparse coding
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Funding
- National Science Foundation of China [61590923, 61422303, 21376077]
- Shu Guang Project by the Shanghai Municipal Education Commission
- Shanghai Education Development Foundation
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This study focuses on the performance monitoring of a non-Gaussian process with multiple operation conditions. By utilizing the Bayesian inference technique, the proposed method, locality preserving sparse modeling, can automatically identify the current operation condition. Then, the feature of the data structure is extracted by locality preserving projections (LPP) and modeled by the sparse modeling technique. This hybrid framework of sparse modeling and LPP provides a robust and accurate paradigm for process data clustering and monitoring. The validity and effectiveness of this approach are verified by applying it to both a synthetic numerical example and the Tennessee Eastman process benchmark process.
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