This paper builds on work presented in the first paper, Part 1  and is of equal significance. The paper proposes a novel compensation method to preserve the integrity of step-fault signatures prevalent in various processes that can be masked during the removal of both auto- and cross correlation. Using industrial data, the paper demonstrates the benefit of the proposed method, which is applicable to chemical, electrical, and mechanical process monitoring. This paper, (and Part 1 ), has led to further work supported by EPSRC grant GR/S84354/01 involving kernel PCA methods.
ASJC Scopus subject areas
- Chemical Engineering (miscellaneous)
- Environmental Science(all)
- Polymers and Plastics
Lieftucht, D., Kruger, U., Xie, L., Littler, T., Chen, Q., & Wang, S. Q. (2006). Statistical Monitoring of Dynamic Multivariate Processes: Part II: Identifying Fault Magnitude and Signature. Industrial and Engineering Chemistry Research, 45 (5)(5), 1677-1688. https://doi.org/10.1021/ie060017b