2015
DOI: 10.1016/j.jfranklin.2014.12.002
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Quality-relevant fault detection and diagnosis for hot strip mill process with multi-specification and multi-batch measurements

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Cited by 59 publications
(13 citation statements)
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“…Based on PLS and singular value decomposition (SVD), Yin et al [33] established a quality monitoring method. In addition, Wang et al [34] and Peng et al [35] proposed principal component regression (PCR)-based quality monitoring methods in the past few years. These methods mainly focused on the subsequent decomposition process, but ignored the effect of different process variables on the quality.…”
Section: Introductionmentioning
confidence: 99%
“…Based on PLS and singular value decomposition (SVD), Yin et al [33] established a quality monitoring method. In addition, Wang et al [34] and Peng et al [35] proposed principal component regression (PCR)-based quality monitoring methods in the past few years. These methods mainly focused on the subsequent decomposition process, but ignored the effect of different process variables on the quality.…”
Section: Introductionmentioning
confidence: 99%
“…Since then, PLS based fault detection technology had been continuously improved [15]- [20]. Peng et al [21] combined PCR with Bayesian inference. Wang and Jiao et al [22] improved the kernel least square method, which could make it better in monitoring performance.…”
Section: Introductionmentioning
confidence: 99%
“…The infinite-dimensional systems [6][7][8][9] that modeled by the coupled PDEs-ODEs commonly exist in practical applications, such as crane cables [10], flexible manipulators [11][12][13][14], moving materials [15,16], flexible wings for aircraft [17] and aerial refueling systems [18]. Due to the infinite-dimensional state space, some control methods for finite-dimensional systems [19][20][21][22][23][24][25][26][27][28] cannot be directly used for control of PDEs. Exogenous disturbances result in many basic control problems to control engineers, considering that the external loads are unknown and spatiotemporally varying.…”
Section: Introductionmentioning
confidence: 99%