Temperature Prediction Model for Roller Kiln by ALD-Based Double Locally Weighted Kernel Principal Component Regression

Chen, Ning; Dai, Jiayang; Yuan, Xiaofeng; Gui, Weihua; Ren, Wei; Koivo, H.N.

doi:10.1109/tim.2018.2810678

Cited by 72 publications

(26 citation statements)

References 30 publications

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“…For dealing with the soft sensor modeling of multiphase batch process, Jin et al [20] developed a JITL KPLS method, where a hybrid similarity including the sample similarity and phase similarity is used to select the relevant training samples and then the local KPLS soft sensor is built for each query sample. To consider both the modeling accuracy and the efficiency, Chen et al [21] proposed a JITL method with selective updating based on approximated linearity dependence (ALD) and applied it to the soft sensor of roller kiln temperature. The DAR method firstly identifies the process modes by applying the data clustering technologies, and then builds multiple local soft sensors corresponding to the different clusters.…”

Section: Introductionmentioning

confidence: 99%

Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

et al. 2020

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To cope with the soft sensor modeling of unobserved multimode nonlinear processes, this paper proposes a modified kernel partial least squares (KPLS) by integrating latent factor clustering (LFC), called LFC-KPLS. In the proposed method, the process data are first divided into several batches orderly, and then projected onto the latent space by using the nonlinear functional expansion technology. In the latent space, partial least squares method is applied to compute the regression coefficients between the input variables and output variable of each batch. These regression coefficients, called the latent factors, can describe the functional relationships in the unobserved multimode data. Therefore, the latent factors are used for mode clustering so that the process data with similar functional relations can be clustered in one mode together. For each mode, the nonlinear soft sensor is established based on KPLS. To assign the mode of the online query sample, a mode identification strategy based on Bayesian inference is designed for the soft sensor online prediction. Finally, two cases studies are adopted to validate the proposed method.

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Section: Introductionmentioning

confidence: 99%

Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

et al. 2020

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“…The hardware sensors used in industries often face issues pertinent to time‐consuming maintenance, aged deterioration, the need for calibration, insufficient accuracy, and any other operational difficulties . For the above reasons, inferential sensors have been acknowledged as better alternatives and low‐cost technological tools to complement and/or replace hardware sensors . The inferential sensor, also named as a soft sensor, is a predictive model used to estimate hard‐to‐measure variables where its input variables are easy‐to‐measure variables.…”

Section: Introductionmentioning

confidence: 99%

“…[1][2][3] For the above reasons, inferential sensors have been acknowledged as better alternatives and low-cost technological tools to complement and/or replace hardware sensors. [4][5][6] The inferential sensor, also named as a soft sensor, is a predictive model used to estimate hard-tomeasure variables where its input variables are easy-tomeasure variables. Since the soft sensor is data-driven, the implementation of distributed control systems and availability of abundant historical plant data have further promoted research activities and industrial applications in this area.…”

Section: Introductionmentioning

confidence: 99%

Missing data treatment for locally weighted partial least square‐based modelling: A comparative study

Yeo

Saptoro

Perumal

2020

Asia-Pacific J Chem Eng

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Adaptive soft sensors including widely used locally weighted partial least square (LW‐PLS) have been established for online prediction, fault detection, and process monitoring. Nevertheless, majority of these existing adaptive soft sensors have zero tolerance to missing data, and the presence of missing data is inevitable due to sensor failures, routine maintenance, changes in sensor equipment over time, merging data from different system, and so forth. In the literature, limited studies could be found on the effects of missing data and the existing missing data imputation methods on the predictive performances of adaptive soft sensors. This work reports combined use of different missing data imputation techniques on LW‐PLS and the nonadaptive soft sensor, the partial least square (PLS). Well known trimmed score regression (TSR) and singular value decomposition (SVD) were employed in this study, and thus, the newly integrated TSR‐LW‐PLS and SVD‐LW‐PLS algorithms were proposed. Meanwhile, both existing TSR‐PLS and SVD‐PLS were used, and their results compared with the novel TSR‐LW‐PLS and SVD‐LW‐PLS algorithms. These algorithms were tested and evaluated using two examples having different percentages of missing data ranging from 5% to 40%. Results showed that TSR‐LW‐PLS model was superior compared with SVD‐LW‐PLS, TSR‐PLS, and SVD‐PLS as indicated by more than 100% improvements in prediction accuracy. It was also evident that TSR‐LW‐PLS is able to cope well with up to 20% of missing data.

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“…Considering problems brought by high‐dimensional data, for instance, computational difficulties, data redundancy, and efficiency degradation, scholars proposed a variety of dimensionality reduction techniques, such as multidimensional scaling analysis [10], principal component analysis (PCA) [11], kernel PCA (KPCA) [12], local linear embedding [13] etc. In the literature [14], for temperature prediction of roller kiln system for lithium‐ion battery, an approximate linearity dependence (ALD)‐based double locally weighted KPCA regression was proposed, which carried out the sample and variable weighted learning at the same time to solve the process time‐varying and the strong non‐linearity problems. For process non‐linearity, which occurs in the manufacture of ternary cathode material, is another problem to be considered in the TCM‐CPS.…”

Section: Introductionmentioning

confidence: 99%

Data fusion based on‐line product quality evaluation of ternary cathode material cyber‐physical systems

Chen

Dai

Tian

et al. 2019

IET cyber-phys. syst.

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Temperature Prediction Model for Roller Kiln by ALD-Based Double Locally Weighted Kernel Principal Component Regression

Cited by 72 publications

References 30 publications

Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

Soft Sensor Modeling for Unobserved Multimode Nonlinear Processes Based on Modified Kernel Partial Least Squares With Latent Factor Clustering

Missing data treatment for locally weighted partial least square‐based modelling: A comparative study

Data fusion based on‐line product quality evaluation of ternary cathode material cyber‐physical systems

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