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data-driven soft sensor for the Estimation of Sound Power Levels of Refrigeration Compressors Through Vibration Measurements

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2020年第8期67卷 7065-7072页

作者： de Siqueira Nascimento, Ahryman Seixas Busse Costa Flesch, Rodolfo Cesar Flesch, Carlos Alberto Univ Fed Santa Catarina Dept Mech Engn BR-88040970 Florianopolis SC Brazil Univ Fed Santa Catarina Dept Automat & Syst Engn BR-88040970 Florianopolis SC Brazil

The quality assessment of the sound power generated by refrigeration compressors is an important and costly process for manufacturers, mainly due to the need for acoustic rooms. Hence, very few samples per lot are tested. In this article, an alternative method based on soft sensing is proposed to estimate the sound power of refrigeration compressors. The method uses vibration measurements from the compressor surface as the input and the sound power generated as the output of machine learning (ML) models to create data-driven soft sensors. This method is less restrictive than the traditional ones and allows a test to be done close to the manufacturing line, in a much faster and cheaper way. In order to create the dataset to train the ML models, sound power and vibration levels of compressors were measured simultaneously in a reverberation room. The results from the data-driven soft sensors were compared with the ones obtained using a simple analytic model defined in an ISO technical note and showed that, even with as little as one vibration measurement point, the proposed method presented better results than the ISO analytic model with nine measurement points.

关键词： Vibration measurement Compressors Analytical models Power measurement Frequency measurement Velocity measurement ISO Standards data-driven soft sensor machine learning (ML) models refrigeration compressor sound power level (SWL) vibration

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A data-driven soft sensor to Forecast Energy Consumption in Wastewater Treatment Plants: A Case Study

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IEEE sensorS JOURNAL 2021年第4期21卷 4908-4917页

作者： Harrou, Fouzi Cheng, Tuoyuan Sun, Ying Leiknes, TorOve Ghaffour, Noreddine King Abdullah Univ Sci & Technol KAUST Div Comp Elect & Math Sci & Engn CEMSE Thuwal 239556900 Saudi Arabia King Abdullah Univ Sci & Technol KAUST Biol & Environm Sci & Engn BESE Div Water Desalinat & Reuse Ctr Thuwal 239556900 Saudi Arabia

Energy consumption is vital to the global costs of wastewater treatment plants (WWTPs). With the increase of installed WWTPs worldwide, the modeling and forecast of their energy consumption have become a critical factor in WWTP design to meet environmental and economic requirements. The accurate and swift energy consumption forecasting soft-sensors are not only supportive to the daily electric and financial budgeting by WWTP practitioners on the micro-scale, but also beneficial to local municipal operation and fundamental to regional environmental impact estimation on the macro-scale. Energy consumption in WWTPs is influenced by different biological and environmental factors, making it complicated and challenging to build soft-sensors. This article intends to provide short-term forecasting of WWTP energy consumption based on data-driven soft sensors using traditional time-series and deep learning methods. Ten data-driven soft sensors, including the ordinary least square, exponential smoothing state space, local regression, auto-regressive integrated moving average (ARIMA), structural time series model, Bayesian structural time series, non-linear auto-regressive, long short-term memory with and without updates, and gated recurrent units have been investigated and compared for WWTP energy consumption forecasting. Energy consumption time-series data from a membrane bioreactor-based WWTP in the middle east is used to evaluate the performances of the proposed soft-sensors. Results showed that ARIMA achieved slightly improved performances, among others. The employment of adaptive deep learning-based soft sensors is expected to enhance the capabilities of the deep models to quickly and accurately follow the trend of future data.

关键词： sensors Energy consumption Time series analysis Forecasting Deep learning Predictive models Wastewater treatment WWTP energy consumption data-driven soft sensor deep learning time-series forecasting

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Refining data-driven soft sensor modeling framework with variable time reconstruction

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JOURNAL OF PROCESS CONTROL 2020年 87卷 91-107页

作者： Yao, Le Ge, Zhiqiang Zhejiang Univ Coll Control Sci & Engn Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China Alibaba Zhejiang Univ Joint Inst Frontier Technol Hangzhou 310027 Peoples R China

Due to the difference of variable positions brought by process structure, time-delay exists between process variables and quality variables. In this paper, this commonly overlooked problem in data-driven soft sensor modeling is illustrated and solved. The main idea in this paper is to take the variable time-delay (VTD) as a model parameter to reconstruct the dataset and then solve it through optimizing the objective function of models. However, the combination of VTD would lead to an intractable high computational complexity, then it is proposed to use an efficient population-based Integer Differential Evolution (IDE) algorithm to select the optimal VTD values and cooperatively learn model parameters. With the help of IDE algorithm, a Variable Time Reconstruction (VTR) modeling framework is then formulated for soft sensor development. As examples, three types of VTR-based soft sensors are developed under this framework to cope with different cases of data features. The presented numerical and industrial cases demonstrate that the proposed VTR-based model can effectively learn the VTD values, which can reconstruct and recover the original data pattern, and thus significantly help increase the generalization performance of soft sensor models. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： data-driven soft sensor Variable time-delay Variable Time Reconstruction Variational Bayesian Regression model Integer Differential Evolution algorithm

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A data-driven soft-sensor for monitoring ASTM-D86 of CDU side products using local instrumental variable (LIV) technique

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JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2018年 84卷 49-59页

作者： Bidar, Bahareh Khalilipour, Mir Mohammad Shahraki, Farhad Sadeghi, Jafar Univ Sistan & Baluchestan CPIC Dept Chem Engn Zahedan 98164 Iran

Atmospheric crude distillation unit is the main unit operation in petroleum refining industries. The main difficulties in quality control of column are the availability of quality measurements. The design of product quality estimator will help improve quality monitoring and control performance in oil refinery industry by accurately predicting the side products properties, simultaneously. The objective of this paper is to design and implement state dependent parameter (SDP) based soft sensors using local instrumental variables (LIV) technique for an industrial atmospheric crude distillation unit. On the basis of tray temperature measurements of the column, soft sensor models for estimation of 95%ASTM-D86 of product streams have been developed. Three soft sensors are separately designed in an offline manner for each product quality with steady-state data of the column. The performance of proposed soft sensors is evaluated through testing data and also by online implementation in simulated control system. The prediction results, after tuning controller parameters, show excellent agreement with quality predictions from the rigorous model. Based on developed soft sensors, it is possible to estimate product properties in a continuous manner with minimum delay compared to laboratory ASTM analysis and apply perfect control as well as compliance with product quality specifications. (C) 2018 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： data-driven soft sensor Instrumental variable State dependent parameter Quality prediction ASTM-D86 temperature Crude distillation column

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Multistage Net: Learning Continuous Multistage Manufacturing Processes of Liquid Products Without Intermediate Output and Lead-Time Labels

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第12期20卷 13765-13775页

作者： Cho, Hojin Lim, Chiehyeon Ulsan Natl Inst Sci & Technol Dept Ind Engn Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence Ulsan 44919 South Korea

Manufacturers have implemented continuous multistage manufacturing processes (MMPs) for their efficiency and flexibility, especially in high-volume production of liquid products. While significant attention has been given to the development of data-driven soft sensors in manufacturing fields, research explicitly addressing continuous MMPs of liquid products is very scarce due to the following challenges: obtaining intermediate output labels and determining lead-time between stages. To overcome these challenges, we introduce Multistage Net, a novel machine learning model designed for continuous MMPs of liquid products. In Multistage Net, several interstage blocks are organized in a hierarchical structure within a multistage module. The interstage block is proposed to capture the sequential dependency between the previous and current stages and concurrently explores the lead-time relationship. From the interconnected interstage blocks, the multistage module can learn the sequential nature of MMPs across all stages even in the absence of intermediate output labels. Through validation experiments on two real-world datasets, it is shown that Multistage Net demonstrates superior prediction performance compared to baseline models. Moreover, further analysis reveals that the prediction performance of Multistage Net is not significantly impacted by the nonexistence of lead-time labels.

关键词： Continuous multistage manufacturing processes data-driven soft sensor deep learning industrial intelligence

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A soft Sensing Approach for Efficient Monitoring of Nanobody-Based Scorpion Antivenom Production 21st

A Soft Sensing Approach for Efficient Monitoring of Nanobody...

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21st International Symposium on Distributed Computing and Artificial Intelligence

作者： Acosta-Pavas, Juan C. Corrales, David Camilo Martinez-Menendez, Irene Villela, Susana Maria Alonso Bouhaouala-Zahar, Balkiss Georgakilas, Georgios K. Mexis, Konstantinos Xenios, Stefanos Dalamagas, Theodore Kokosis, Antonis O'donohue, Michael Fillaudeau, Luc Aceves-Lara, Cesar A. Univ Toulouse CNRS TBI INRAINSA Toulouse France INRAE UMS 1337 TWB 135 Ave Rangueil F-31077 Toulouse France Inst Pasteur Tunis Lab Biomol Venins & Applicat Theranost LBVAT 13 Pl PasteurBP-741002 Belvedere Tunis Tunisia Univ Tunis El Manar Fac Med Tunis 15 Rue Djebel Lakhdhar Bab Saadoun Tunis 10007 Tunisia Athena Res Ctr Maroussi Greece Natl Tech Univ Athens Sch Chem Engn Iroon Polytechneiou 9 Zografos 15780 Athens Greece

ISBN: (纸本)9783031820724;9783031820731

The growing demand for recombinant-based products has increased rapidly in recent years. One of the current market scopes is the production of recombinant scorpion antivenoms in E. coli. However, there are limitations in real-time quantification of Critical Quality Attributes (CQAs), such as recombinant proteins or biomass concentrations. soft sensors merge as an alternative to solve this problem. In this work, six data-driven soft sensors were developed for online monitoring of the recombinant protein concentration. First, feature selection methods were tested to determine the relevant online variables to train the soft sensors. Then, the protein experimental dataset was augmented using a hybrid model. The K-Nearest Neighbors (KNN), and the Radial Basis Function (RBF) Support Vector Machine (SVM) obtained the best results with R-2 values higher than 0.996, RMSE and MAE values less than 0.058 and 0.051. However, the KNN soft sensor was computed in less time than the RBF SVM soft sensor. These results show the feasibility of KNN and RBF soft sensors for real-time monitoring of recombinant scorpion antivenoms.

关键词： Process Monitoring E. Coli Antivenom Production Machine learning data-driven soft sensor Feature Selection

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Laplacian regularization of linear regression model for semi-supervised industrial soft sensor development

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 254卷

作者： Zheng, Junhua Ye, Lingjian Ge, Zhiqiang Zhejiang Univ Sci & Technol Sch Automat & Elect Engn Hangzhou 310023 Peoples R China Huzhou Univ Sch Engn Huzhou Key Lab Intelligent Sensing & Optimal Contr Huzhou 313000 Peoples R China Peng Cheng Lab Shenzhen 518000 Peoples R China

With more and more data collected from modern industrial processes, data-based soft sensor modeling methods have become popular for online prediction of those difficult-to-measure key variables. As one of the most widely used methods, the linear regression model has been studied for many years. In this paper, a new semi-supervised form of data-based soft sensor is developed upon this simple but commonly used linear regression model, in order to make it more engineering applicable. Particularly, a semi-supervised model structure is formulated by introducing a Laplacian regularization term, through which a lot more unlabeled data samples can be well incorporated for modeling. With an improved estimation for the input data distribution, the real data features can be better captured and thus the model parameters are well restricted, in order to provide a better performance. In addition, the basic Laplacian regularized linear regression model is further extended to nonlinear counterparts by the introductions of basis function and kernel trick in the input data space. For performance evaluation, two industrial case studies are provided, based on which both feasibility and effectiveness of the developed method are confirmed.

关键词： Semi-supervised data analytics Laplacian regularization data-driven soft sensor Quality prediction Industrial processes

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sensor-fault tolerance in a wastewater treatment plant by means of ANFIS-based soft sensor and control reconfiguration

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NEURAL COMPUTING & APPLICATIONS 2018年第10期30卷 3265-3276页

作者： Belchior, Carlos Alberto C. Araujo, Rui Alexandre M. Souza, Francisco Alexandre A. Landeck, Jorge Afonso C. Univ Coimbra ISR Dept Elect & Comp Engn P-3030290 Coimbra Portugal Univ Coimbra Instrumentat Ctr Dept Phys P-3004516 Coimbra Portugal

This work presents a sensor-fault-tolerant design applied to a decentralized dissolved oxygen control in an activated sludge process subject to sensor faults such as bias and slow drifts. The core idea is to use a data-driven soft sensor based on adaptive neuro-fuzzy inference system to act as a backup of the joint sensor and controller block, and to exploit the data/analytical correlations existing in the system. After fault detection and isolation, a control reconfiguration technique takes action in order to surmount/counteract the effect of the fault until the faulty sensor is repaired. The approach presented here was applied to the Benchmark Simulation Model n.1 and was able to demonstrate the improvements on the control system dependability.

关键词： sensor-fault-tolerant control data-driven soft sensor ANFIS Activated sludge process Decentralized dissolved oxygen control

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Robust Supervised Probabilistic Factor Analysis and Its Application to Industrial soft sensor Modeling

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IEEE ACCESS 2019年 7卷 184038-184052页

作者： Shao, Weiming Zhang, Hongwei Song, Zhihuan Zhejiang Univ Coll Control Sci & Engn Hangzhou 310007 Peoples R China Xian Polytech Univ Sch Elect & Informat Xian 710048 Peoples R China

data-driven soft sensors have recently drawn considerable and increasing research interest in process industries. To achieve good performance, data analytics algorithms usually have to address complex characteristics presented by industrial datasets. Outlying data samples, which result in heavy-tailed distributions, is particularly challenging to deal with, as they can significantly distort the estimation of model parameters. In order to resolve such issue, this paper proposes a robust supervised probabilistic factor analysis model (RSPFA), including the model structure and the expectation-maximization-based training algorithm. Unlike the conventional assumption of Gaussian distributed dataset, the RSPFA exploits the Student's t distribution, and enhances the robustness by the means of the immunity of the Student's t distribution. Besides, to adapt the RSPFA to nonlinear industrial processes, a locally weighted RSPFA (LW-RSPFA) is further developed using the philosophy of 'divide and conquer'. The proposed methods are evaluated with three cases including one synthetic case and two real-world industrial cases, through which the effectiveness and applicability of the RSPFA and LW-RSPFA are verified.

关键词： data-driven soft sensor robust factor analysis Student's t distribution outlying data expectation-maximization locally weighted learning

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soft sensor for COx Content in Tail Gas of PX Oxidation Side Reactions Based on Particle Filters and EM Algorithm

Soft Sensor for COx Content in Tail Gas of PX Oxidation Side...

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2013 International Conference on Future software Engineering and Multimedia Engineering(ICFM 2013)

作者： Ranran Liu Zhengming Li School of Electrical and Information Engineering Jiangsu University Changzhou Liu Guojun Vocational Technology College

Based on expectation-maximization algorithm, parameter estimation was proposed for data-driven nonlinear models in this work. On this basis, particle filters were used to approximately calculate integrals, deriving EM algorithm based on particle filter. And the effectiveness of using the proposed algorithm for the soft sensor of CO content in tail gas of PX oxidation side reactions was verified through simulation results.

关键词： Expectation-maximization algorithm particle filtering data-driven soft sensor

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