Abstract
The common occurrence of slow drifts in industrial processes ask for the need of adaptive monitoring. In this chapter, a recursive slow feature analysis algorithm for adaptive process monitoring is developed to handle time-varying processes. An algebraic property of slow feature analysis is first revealed. We then show that such a property may be violated in the presence of online updating, and we give an effective remedy. A novel algorithm based on rank-one modification and orthogonal iteration procedure is developed to recursively adjust the solution to the generalized eigenvalue problem, model parameters, and associated monitoring statistics conveniently. In addition, an improved stopping principle for model updating is proposed based on statistics related to process dynamics, which provides an intelligent maintenance mechanism of monitoring systems. The efficacy of the proposed schema is finally evaluated on a industrial crude heating furnace system.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Only when L consecutive samples of \(S^2\) keep normal could the model updating restart.
References
Kadlec P, Grbić R, Gabrys B (2011) Review of adaptation mechanisms for data-driven soft sensors. Comput Chem Eng 35:1–24
Rännar S, MacGregor JF, Wold S (1998) Adaptive batch monitoring using hierarchical PCA. Chemom Intell Lab Syst 41:73–81
Wold S (1994) Exponentially weighted moving principal components analysis and projections to latent structures. Chemom Intell Lab Syst 23:149–161
Wang X, Kruger U, Irwin GW (2005) Process monitoring approach using fast moving window PCA. Ind Eng Chem Res 44:5691–5702
Jeng JC (2010) Adaptive process monitoring using efficient recursive PCA and moving window PCA algorithms. J Taiwan Inst Chem E 41:475–481
Qin SJ (1998) Recursive PLS algorithms for adaptive data modeling. Comput Chem Eng 22:503–514
Li W, Yue HH, Valle-Cervantes S et al (2000) Recursive PCA for adaptive process monitoring. J Process Control 10:471–486
Elshenawy LM, Yin S, Naik AS et al (2009) Efficient recursive principal component analysis algorithms for process monitoring. Ind Eng Chem Res 49:252–259
Kompella VR, Luciw M, Schmidhuber J (2012) Incremental slow feature analysis: adaptive low-complexity slow feature updating from high-dimensional input streams. Neural Comput 24:2994–3024
Turner R, Sahani M (2007) A maximum-likelihood interpretation for slow feature analysis. Neural Comput 19:1022–1038
Shang C, Huang B, Yang F et al (2015) Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling. AIChE J 61:4126–4139
Qin SJ (2003) Statistical process monitoring: basics and beyond. J Chemometr 17:480–502
Doukopoulos XG, Moustakides GV (2008) Fast and stable subspace tracking. IEEE Trans Signal Process 56:1452–1465
Jackson JE, Mudholkar GS (1979) Control procedures for residuals associated with principal component analysis. Technometrics 21:341–349
Wardell DG, Moskowitz H, Plante RD (1994) Run-length distributions of special-cause control charts for correlated processes. Technometrics 36:3–17
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Shang, C. (2018). Recursive SFA Algorithm and Adaptive Monitoring System Design. In: Dynamic Modeling of Complex Industrial Processes: Data-driven Methods and Application Research. Springer Theses. Springer, Singapore. https://doi.org/10.1007/978-981-10-6677-1_4
Download citation
DOI: https://doi.org/10.1007/978-981-10-6677-1_4
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-6676-4
Online ISBN: 978-981-10-6677-1
eBook Packages: EngineeringEngineering (R0)