Soft sensor validation for monitoring and resilient control of sequential subway indoor air quality through memory-gated recurrent neural networks-based autoencoders

Loy-Benitez, Jorge; Heo, SungKu

doi:10.1016/j.conengprac.2020.104330

Cited by 51 publications

(17 citation statements)

References 63 publications

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“…Another line of future work is to compare point-wise detection as done here, where a single data point must be classified, to temporal classification, where a series of data points is passed to the model (e.g. [Loy-Benitez et al 2020] and [Gupta et al 2020]). The latter requires larger models and can introduce lag to the detection, which is a trade-off that can impact its usefulness in embedded and real-time settings.…”

Section: Discussionmentioning

confidence: 99%

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Sensor Validation for Indoor Air Quality using Machine Learning

Seibert¹,

Araújo²,

McElligott³

2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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To guarantee a high indoor air quality is an increasingly important task. Sensors measure pollutants in the air and allow for monitoring and controlling air quality. However, all sensors are susceptible to failures, either permanent or transitory, that can yield incorrect readings. Automatically detecting such faulty readings is therefore crucial to guarantee sensors' reliability. In this paper we evaluate three Machine Learning algorithms applied to the task of classifying a single reading from a sensor as faulty or not, comparing them to standard statistical approaches. We show that all tested machine learning methods -- Multi-layer Perceptron, K-Nearest Neighbor and Random Forest -- outperform their statistical counterparts, both by allowing better separation boundaries and by allowing for the use of contextual information. We further show that this result does not depend on the amount of data, but ML methods are able to continue to improve as more data is made available.

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Section: Discussionmentioning

confidence: 99%

“…More recently, several works propose the usage of Recurrent Neural Networks or Convolutional Neural Networks to analyse a time-series data to detect faults [Loy-Benitez et al 2020, Gupta et al 2020, Eren 2017, with promising results. These models are however quite complex, often requiring dedicated hardware for training and inference.…”

Section: Related Workmentioning

confidence: 99%

Sensor Validation for Indoor Air Quality using Machine Learning

Seibert¹,

Araújo²,

McElligott³

2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

View full text Add to dashboard Cite

show abstract

“…The RNN has been widely used in the soft sensor model due to its feedback structure having the memory function and the ability of capturing the dynamic characteristics of objects. 24 Zhang et al 9 employed the PCA-WD-NARX algorithm to realize the dynamic soft sensor model of a complex chemical process in purified terephthalic acid plants and achieved remarkable results. However, NARX, which adopts a hierarchical connection structure and is trained by the gradient descent method, still belongs to the traditional RNN and has the risk of gradient explosion and gradient dispersion.…”

Section: Related Workmentioning

confidence: 99%

Novel Gray Orthogonal Echo State Network Integrating the Process Mechanism for Dynamic Soft Sensor Development

Zhang

Han

et al. 2021

Ind. Eng. Chem. Res.

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Polypropylene is an important raw material for producing medical masks. The melt index (MI) is one of the most important quality indexes in the propylene polymerization (PP) production process, but it cannot be physically measured in real time. In consideration of the strong nonlinearity, obvious dynamic characteristics, and complex mechanism of the PP process, the gray soft sensor model, which combines the merits of mechanism-driven modeling and data-driven modeling, has great research value. In this study, we propose a novel gray dynamic soft sensor modeling strategy. The influence factors of the MI are analyzed based on the process mechanism of PP production plants to select appropriate process variables and make necessary mechanism transformation. Then, the kernel principal component analysis and wavelet denoising are used to eliminate the multicollinearity and “noise” interference among process variables. Finally, an improved orthogonal sparse echo state network is used to construct the gray dynamic soft sensor model. The experimental results based on the real field data of the PP production plant show that the orthogonalization and sparseness of the reservoir can effectively enhance the performance of the reservoir and improve the operational efficiency. Meanwhile, the proposed dynamic soft sensing model has better prediction ability than the corresponding methods. Moreover, this study is of great significance to guide and optimize the PP production process.

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“…Recently, DL modeling in industrial processes have been introduced for soft sensor and monitoring tasks. [29][30][31][32] In addition, Laplacian regularization has been incorporated as a regularization term to penalize the objective function. 33 However, this Laplacian-based method lacks extrapolating ability and may exhibit fast fading performance when only a few labeled data are available.…”

Section: Introductionmentioning

confidence: 99%

Manifold semi-supervised learning for aluminum electrolysis temperature identification based on regularized hierarchical extreme learning machine

Lei

Liu

Karimi

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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The aim of this article is to develop a soft approach for a real-time cell temperature prediction in the aluminum electrolysis reduction. Under the limited labeled data constraint, Laplacian semi-supervised learning methods, which can fully utilize the underlying structure of the data distribution and further extract information contained in all available data, has recently received extensive attention in the field of soft sensor modeling. Since the Laplacian underlying manifold is a constant, it remains a challenging task to improve the extrapolating ability for the case that only a few labeled samples are available. This study presents a soft modeling method based on a semi-supervised deep learning structure, which was developed from the hierarchical autoencoders with extreme learning machine. Furthermore, a Laplacian–Hessian semi-supervised extreme learning machine is built to learn all the geometric distribution information. The Laplacian–Hessian semi-supervised extreme learning machine method is applied to estimate the cell temperature in an aluminum reduction process. The experimental results demonstrate the performance and robustness of the proposed algorithm are superior to those of the existing state-of-the-art methods.

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Soft sensor validation for monitoring and resilient control of sequential subway indoor air quality through memory-gated recurrent neural networks-based autoencoders

Cited by 51 publications

References 63 publications

Sensor Validation for Indoor Air Quality using Machine Learning

Sensor Validation for Indoor Air Quality using Machine Learning

Novel Gray Orthogonal Echo State Network Integrating the Process Mechanism for Dynamic Soft Sensor Development

Manifold semi-supervised learning for aluminum electrolysis temperature identification based on regularized hierarchical extreme learning machine

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