Adaptive Gaussian Mixture Model-Based Relevant Sample Selection for JITL Soft Sensor Development

Fan, Miao; Ge, Zhiqiang; Song, Zhihuan

doi:10.1021/ie5029864

Cited by 47 publications

(17 citation statements)

References 26 publications

(50 reference statements)

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“…As a result, it is challenging to build an appropriate global model suitable for products under diverse working conditions. As an alternative, an adaptive on-line local modeling strategy, just-in-time learning, is proposed [16]. JITL based models are built on-line.…”

Section: Volume 4 2016mentioning

confidence: 99%

Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

Dong

Tian

Peng³

2020

IEEE Access

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Mechanical properties are important indexes to evaluate the quality of hot rolling strips. It is a research hotspot in the field of hot rolling that realizing timely and accurate soft sensing of mechanical properties. Traditional soft sensing methods have poor performance in the application of strong nonlinearity and multiple working conditions. Moreover, the utilization rate of data is relatively low, which limit the improvement of prediction accuracy. To solve the problems above, a just-in-time learning (JITL) based multi-block weighted semisupervised Gaussian mixture regression (JMWSSGMR) soft sensor is proposed in the paper. There are two stages in the soft sensor: off-line variable blocking and on-line local modeling. In the off-line phase, process variables are divided into different sub-blocks by partial least square (PLS) according to distinct principal component directions. In each sub-block, original variables with high contribution rate are retained. In the on-line phase, optimized Mahalanobis distance is constructed to select the most similar historical samples to the query sample. Next, various real-time semisupervised sub-models are built to estimate the output of the query sample. Finally, predicted values of sub-models are fused and ultimate prediction of mechanical properties is obtained. Case studies are carried out on a numerical example and a hot rolling process. The feasibility and effectiveness of proposed soft sensor are verified by the predicted results.

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Section: Volume 4 2016mentioning

confidence: 99%

Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

Dong

Tian

Peng³

2020

IEEE Access

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“…In order to make a distinction between the two process variations, a Mahalanobis-distance-based similarity (MD) is used as an index in this paper [33]. For a testing sample x test ∈ R D , the local MD between x test and the center µ x k of the k th Gaussian component G k (…”

Section: B Classification Of Process Variation and Selection Of Updamentioning

confidence: 99%

Soft Sensor Model Development for Cobalt Oxalate Synthesis Process Based on Adaptive Gaussian Mixture Regression

et al. 2019

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Average particle size is a critical quality variable for optimal operation of the cobalt oxalate synthesis process. However, the measurement for this variable is often achieved from offline laboratory assaying procedure with low sampling rate and low reliability. Therefore, a data-driven soft sensor model based on adaptive Gaussian mixture regression (AGMR) is presented in this paper. Firstly, a GMR based soft sensor model is developed for predicting the average particle size. Secondly, the prediction uncertainty obtained from the GMR model is used to assess the performance of the current soft sensor model. Thirdly, a dual updating algorithm based on the model performance assessment is constructed to track the timevarying behavior of the synthesis process. In the updating method, bias updating and moving window model updating methods are performed in turns based on the results of model performance assessment. The dual updating mechanism can avoid blind updating. Finally, a numerical example and a real industrial cobalt oxalate synthesis process application are used to demonstrate the effectiveness of the proposed method. INDEX TERMS Cobalt oxalate synthesis process, Gaussian mixture regression, soft sensor, model performance assessment, model updating.

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“…A good soft sensor may be successfully developed and implemented; however, its predictive performance tends to deteriorate over the time. This is due to the changes in the state of plants and process characteristic including catalyst deactivation and sensor and process drift due to equipment aging . Consequently, the traditional, nonadaptive PLS‐based soft sensors should be modified to be adaptive in dealing with new characteristics of the process and its related data.…”

Section: Introductionmentioning

confidence: 99%

“…This is due to the changes in the state of plants and process characteristic including catalyst deactivation and sensor and process drift due to equipment aging. 7,[15][16][17] Consequently, the traditional, nonadaptive PLS-based soft sensors should be modified to be adaptive in dealing with new characteristics of the process and its related data. The recursive PLS-based adaptive method had been proposed by Dayal and MacGregor 18 , Helland et al 19 , and Qin 20 .…”

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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Adaptive Gaussian Mixture Model-Based Relevant Sample Selection for JITL Soft Sensor Development

Cited by 47 publications

References 26 publications

Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

Just-in-Time Learning-Based Soft Sensor for Mechanical Properties of Strip Steel via Multi-Block Weighted Semisupervised Models

Soft Sensor Model Development for Cobalt Oxalate Synthesis Process Based on Adaptive Gaussian Mixture Regression

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

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