2010
DOI: 10.1007/s10845-010-0394-3
|View full text |Cite
|
Sign up to set email alerts
|

An independent component analysis-based disturbance separation scheme for statistical process monitoring

Abstract: In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
5

Citation Types

0
5
0

Year Published

2011
2011
2018
2018

Publication Types

Select...
8
1

Relationship

3
6

Authors

Journals

citations
Cited by 15 publications
(5 citation statements)
references
References 49 publications
0
5
0
Order By: Relevance
“…However, in fact, not all QCs are equally important as some are redundant or noisy indicators of a product's overall quality level [1]. Therefore, selecting possible key quality characteristics (KQCs) becomes an important task before implementing quality control or improvement tools, such as statistical process control (SPC) [2] and design of experiments (DOE) [3]. In addition, the selected KQCs could be used to establish a concise and efficient learning algorithm for the purpose of predicting quality level of products [4].…”
Section: Introductionmentioning
confidence: 99%
“…However, in fact, not all QCs are equally important as some are redundant or noisy indicators of a product's overall quality level [1]. Therefore, selecting possible key quality characteristics (KQCs) becomes an important task before implementing quality control or improvement tools, such as statistical process control (SPC) [2] and design of experiments (DOE) [3]. In addition, the selected KQCs could be used to establish a concise and efficient learning algorithm for the purpose of predicting quality level of products [4].…”
Section: Introductionmentioning
confidence: 99%
“…Lu et al [10] integrated ICA, engineering process control and BPN to recognize shift and trend patterns in correlated process. Lu [11] proposed an ICA-based monitoring scheme to identify shift pattern in an autocorrelated process. Lu et al [12] combined ICA and SVM for diagnosing mixture CCPs which are mixed by the normal and other abnormal basic patterns.…”
Section: Introductionmentioning
confidence: 99%
“…Lu [5] developed an ICA-based disturbance separation scheme to diagnose shift patterns with different levels of shift magnitudes in a correlated process. Lu et al [6] proposed a two-stage forecasting model by integrating linear ICA and support vector regression (SVR) for financial time series.…”
Section: Introductionmentioning
confidence: 99%
“…Lu et al [ 4 ] proposed a hybrid scheme which integrates ICA, engineering process control (EPC), and backpropagation neural network (BPN) to recognize shift and trend patterns in correlated processes. Lu [ 5 ] developed an ICA-based disturbance separation scheme to diagnose shift patterns with different levels of shift magnitudes in a correlated process. Lu et al [ 6 ] proposed a two-stage forecasting model by integrating linear ICA and support vector regression (SVR) for financial time series.…”
Section: Introductionmentioning
confidence: 99%