Review of soft sensor methods for regression applications

Souza, Francisco; Araújo, Rui; Mendes, Jérôme

doi:10.1016/j.chemolab.2015.12.011

Cited by 265 publications

(111 citation statements)

References 102 publications

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“…Soft sensors for regression tasks have found wide utility in process engineering and process analytical chemistry [1,2,3]. A soft sensor is effectively a calibration used on time-series data.…”

Section: Introductionmentioning

confidence: 99%

Small moving window calibration models for soft sensing processes with limited history

Kneale

Brown

2018

Chemometrics and Intelligent Laboratory Systems

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Five simple soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were moving window partial least squares regression (and a recursive variant), moving window random forest regression, the mean moving window of y, and a novel random forest partial least squares regression ensemble (RF-PLS), all of which can be used with small sample sizes so that they can be rapidly placed online. It was found that, on two of the datasets studied, small window sizes led to the lowest prediction errors for all of the moving window methods studied. On the majority of datasets studied, the RF-PLS calibration method offered the lowest onestep-ahead prediction errors compared to those of the other methods, and it demonstrated greater predictive stability at larger time delays than moving window PLS alone. It was found that both the random forest and RF-PLS methods most adequately modeled the datasets that did not feature purely monotonic increases in property values, but that both methods performed more poorly than moving window PLS models on one dataset with purely monotonic property values. Other data dependent findings are presented and discussed.

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Section: Introductionmentioning

confidence: 99%

Small moving window calibration models for soft sensing processes with limited history

Kneale

Brown

2018

Chemometrics and Intelligent Laboratory Systems

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“…Virtual sensors were originally introduced in process industry to predict hard-to-measure variables, which may be caused by a lack of sensors or the high cost of sensors, using easy-to-measure variables [1,2]. Over the decades, it became a hot research subject and has been proven to be a powerful tool in many industrial processes.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

Sun

Zhou

et al. 2017

Sensors

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Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

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“…[1][2][3][4] Melt index of polypropylene is applied widely in determining the grade of product and is considered as one of the most important indicators in the quality control of the industrial propylene polymerization process. However, not all process variables can be measured online resulting from the large time delay and high analytic costs.…”

Section: Introductionmentioning

confidence: 99%

A novel Bayesian inference soft sensor for real‐time statistic learning modeling for industrial polypropylene melt index prediction

Sun

Wang

Liu

et al. 2017

J of Applied Polymer Sci

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A novel real-time soft sensor based on a sparse Bayesian probabilistic inference framework is proposed for the prediction of melt index in industrial polypropylene process. The Bayesian framework consists of a relevance vector machine for predicting melt index and a particle filtering algorithm for soft sensor optimization. An online correcting strategy is also developed for improving the performance of real-time melt index prediction. The method takes advantages of the probabilistic inference and using prior statistical knowledge of polymerization process. Developed soft sensors are validated with ten public databases from UCI machine learning repository and real data from industrial polypropylene process. Experimental results indicate the effectiveness of proposed method and show the improvement in both prediction precision and generalization capability compared with the reported models in literatures.

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Review of soft sensor methods for regression applications

Cited by 265 publications

References 102 publications

Small moving window calibration models for soft sensing processes with limited history

Small moving window calibration models for soft sensing processes with limited history

A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

A novel Bayesian inference soft sensor for real‐time statistic learning modeling for industrial polypropylene melt index prediction

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