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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 9308

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Special Issue Editors

Dr. Chamil Abeykoon

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Guest Editor

School of Materials, Faculty of Science and Engineering, University of Manchester, Manchester M13 9PL, UK
Interests: process monitoring, modelling and control; soft sensors and soft sensing; process instrumentation; renewable energy technologies; phase change materials, additive manufacturing; polymer/composite processing; heat transfer
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Maria Gabriella Xibilia

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Guest Editor

Department of Engineering, University of Messina, Contrada Di Dio, Vill. S. Agata , 98166 Messina, Italy
Interests: system identification; soft sensors; soft computing; machine learning; neural networks; nonlinear control; complex systems; industrial automation; process monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is designed to introduce all types of soft sensors and sensing techniques, including manufacturing methods, materials, and application in various fields such as virtual reality interfaces, health care systems, motion capture systems, fault detection, and diagnosis. We would like to invite researchers to contribute their original research and qualified reviews related to this topic. The potential topics include, but are not limited to:

soft sensors;
innovative sensing methodologies;
soft robotics;
soft actuators;
soft materials/composites for sensor/sensing and detection;
stretchable sensors;
flexible sensors;
skin patch sensor/sensors printed on the skin;
novel manufacturing techniques of soft sensors;
soft sensors for motion capture and analysis;
soft sensors in the intelligent process industry;
multi-sensor data fusion for soft sensing;
applications in fault detection and diagnosis and monitoring of complex processes;
applications in state estimation, control, and optimization;
applications in process analytical technology (PAT), manufacturing, chemical-, bio-, pharmaceutical-, oil-, and process engineering;
machine learning/AI;
applications in weather mapping, environmental observations;
applications in agriculture, irrigation, air quality, water treatment, etc.;
self-powered sensors.

Dr. Chamil Abeykoon
Prof. Dr. Maria Gabriella Xibilia
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

soft sensors
innovative sensing methodologies
novel manufacturing techniques of soft sensors
soft sensors for motion capture and analysis
soft sensors in the intelligent process industry
multi-sensor data fusion for soft sensing
applications in fault detection and diagnosis and monitoring of complex processes
applications in state estimation, control, and optimization
applications in process analytical technology (PAT), manufacturing, chemical-, bio-, pharmaceutical-, oil-, and process engineering
machine learning/AI
applications in weather mapping, environmental observations
applications in extreme environment
applications in agriculture, irrigation, air quality, water treatment, etc.

Published Papers (6 papers)

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Research

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19 pages, 8673 KiB

Open AccessArticle

A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas

by Yonggang Wang, Zhida Li and Nannan Zhang

Sensors 2024, 24(7), 2340; https://doi.org/10.3390/s24072340 - 07 Apr 2024

Viewed by 396

Abstract

As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas’s oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm–long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an

R^{2}

value of 0.98664. The whale optimization algorithm–long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization–least squares support vector machine (PSO-LSSVM), and particle swarm optimization–long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants. Full article

(This article belongs to the Special Issue Soft Sensors and Sensing Techniques)

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19 pages, 6902 KiB

Open AccessArticle

Research on the Soft-Sensing Method of Indicator Diagram of Beam Pumping Unit

by Huaijun Zhao, Junping Wang, Tianyu Liu, Yang Yu, Dingxing Hu and Chenxin Cai

Sensors 2024, 24(6), 1794; https://doi.org/10.3390/s24061794 - 11 Mar 2024

Viewed by 458

Abstract

An accurate calculation of the indicator diagram of a pumping unit is the key factor in analyzing the performance of an oilfield production and operation and in preparing and optimizing an oilfield development plan. Aiming at the problems of the poor stability of the conventional load-displacement sensor method and the wave equation method, owing to the influence of an alternating load on the force sensor and the difficulty in measuring the crank angle using the electrical parameter method, a new soft sensing method employing the input electrical parameters of the motor and the beam inclination has been proposed to obtain the indicator diagram. At first, this method is established based on the beam angle of the pumping unit, which is easily measured using the suspension point displacement mathematics calculation model and the torque factor. Subsequently, the electric motor input parameters, the parameters of the four-bar linkage, and the relationship between the polished rod load have been established. Finally, the motor and the beam angle of the measured electrical parameters have been substituted into the calculation of the suspension point displacement and load value and pull in accordance with the guidelines to eliminate the singularity mutation values. After processing the measured data through a Butterworth filter, the indicator diagram is obtained. The results of the engineering experiment and application show that the average relative error of the method is less than 3.95%, and the maximum relative error remains within 2% for 6 months, which verifies the stability of the soft sensing method. Full article

(This article belongs to the Special Issue Soft Sensors and Sensing Techniques)

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28 pages, 1535 KiB

Open AccessArticle

How Not to Make the Joint Extended Kalman Filter Fail with Unstructured Mechanistic Models

by Cristovão Freitas Iglesias, Jr. and Miodrag Bolic

Sensors 2024, 24(2), 653; https://doi.org/10.3390/s24020653 - 19 Jan 2024

Viewed by 728

Abstract

The unstructured mechanistic model (UMM) allows for modeling the macro-scale of a phenomenon without known mechanisms. This is extremely useful in biomanufacturing because using the UMM for the joint estimation of states and parameters with an extended Kalman filter (JEKF) can enable the real-time monitoring of bioprocesses with unknown mechanisms. However, the UMM commonly used in biomanufacturing contains ordinary differential equations (ODEs) with unshared parameters, weak variables, and weak terms. When such a UMM is coupled with an initial state error covariance matrix

P (t = 0)

and a process error covariance matrix

Q

with uncorrelated elements, along with just one measured state variable, the joint extended Kalman filter (JEKF) fails to estimate the unshared parameters and state simultaneously. This is because the Kalman gain corresponding to the unshared parameter remains constant and equal to zero. In this work, we formally describe this failure case, present the proof of JEKF failure, and propose an approach called SANTO to side-step this failure case. The SANTO approach consists of adding a quantity to the state error covariance between the measured state variable and unshared parameter in the initial P(t = 0) of the matrix Ricatti differential equation to compute the predicted error covariance matrix of the state and prevent the Kalman gain from being zero. Our empirical evaluations using synthetic and real datasets reveal significant improvements: SANTO achieved a reduction in root-mean-square percentage error (RMSPE) of up to approximately 17% compared to the classical JEKF, indicating a substantial enhancement in estimation accuracy. Full article

(This article belongs to the Special Issue Soft Sensors and Sensing Techniques)

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17 pages, 3292 KiB

Open AccessArticle

An Intelligent Anomaly Detection Approach for Accurate and Reliable Weather Forecasting at IoT Edges: A Case Study

by Şükrü Mustafa Kaya, Buket İşler, Adnan M. Abu-Mahfouz, Jawad Rasheed and Abdulaziz AlShammari

Sensors 2023, 23(5), 2426; https://doi.org/10.3390/s23052426 - 22 Feb 2023

Cited by 3 | Viewed by 2430

Abstract

Industrialization and rapid urbanization in almost every country adversely affect many of our environmental values, such as our core ecosystem, regional climate differences and global diversity. The difficulties we encounter as a result of the rapid change we experience cause us to encounter many problems in our daily lives. The background of these problems is rapid digitalization and the lack of sufficient infrastructure to process and analyze very large volumes of data. Inaccurate, incomplete or irrelevant data produced in the IoT detection layer causes weather forecast reports to drift away from the concepts of accuracy and reliability, and as a result, activities based on weather forecasting are disrupted. A sophisticated and difficult talent, weather forecasting needs the observation and processing of enormous volumes of data. In addition, rapid urbanization, abrupt climate changes and mass digitization make it more difficult for the forecasts to be accurate and reliable. Increasing data density and rapid urbanization and digitalization make it difficult for the forecasts to be accurate and reliable. This situation prevents people from taking precautions against bad weather conditions in cities and rural areas and turns into a vital problem. In this study, an intelligent anomaly detection approach is presented to minimize the weather forecasting problems that arise as a result of rapid urbanization and mass digitalization. The proposed solutions cover data processing at the edge of the IoT and include filtering out the missing, unnecessary or anomaly data that prevent the predictions from being more accurate and reliable from the data obtained through the sensors. Anomaly detection metrics of five different machine learning (ML) algorithms, including support vector classifier (SVC), Adaboost, logistic regression (LR), naive Bayes (NB) and random forest (RF), were also compared in the study. These algorithms were used to create a data stream using the time, temperature, pressure, humidity and other sensor-generated information. Full article

(This article belongs to the Special Issue Soft Sensors and Sensing Techniques)

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31 pages, 3182 KiB

Open AccessArticle

Soft Sensor Design via Switching Observers

by Fotis N. Koumboulis, Dimitrios G. Fragkoulis, Nikolaos D. Kouvakas and Aikaterini Feidopiasti

Sensors 2023, 23(4), 2114; https://doi.org/10.3390/s23042114 - 13 Feb 2023

Cited by 1 | Viewed by 1226

Abstract

The goal of the paper is the design of soft sensors for single input single output (SISO) nonlinear processes. This goal is of essential importance for process monitoring, fault detection and fault isolation. The observer-based technique, being a fruitful direction in soft sensor design, is followed to develop soft sensors for nonlinear processes with known dynamics and unknown physical parameters. A new and general approach, based on the identified I/O linear approximant system descriptions, around prespecified operating points, and a bank of switching linear observers, will be developed. The system property of the I/O reconstructability of the state space linear approximant of a nonlinear model is presented. The design of each observer is based on the I/O measurements and structural characteristics of the nonlinear process. Observer-oriented target areas are introduced, and the respective dense web principle is formulated. The design is completed by the design of a data-driven rule-based system, providing stepwise switching among the observers of the bank. The number of observers of the bank is equal to the number of the linear approximants of the nonlinear process model and is equal to the number of the respective target operating areas. The target operating areas are required to satisfy the dense web principle. The information provided by the soft sensor is the estimation of the non-measured variables of the process. The information used by the soft sensor is the identified I/O approximants of the process as well as the real time values of the measurement variables. The efficiency of the design scheme is illustrated through symbolic and numerical simulation results for a chemostat. The nonlinear model of the chemostat is initially approximated by a set of ten linear approximants. After, the I/O approximants are identified, the respective observers are designed and the target operating areas are determined, where several cases of the satisfaction of the dense web principle are investigated. The soft sensor is composed in terms of the designed observers. Simulation results illustrate the satisfactory performance of the designed soft sensor. Full article

(This article belongs to the Special Issue Soft Sensors and Sensing Techniques)

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Review

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26 pages, 7555 KiB

Open AccessReview

Condition Monitoring of Additively Manufactured Injection Mould Tooling: A Review of Demands, Opportunities and Potential Strategies

by Albert Weinert, David Tormey, Christopher O’Hara and Marion McAfee

Sensors 2023, 23(4), 2313; https://doi.org/10.3390/s23042313 - 19 Feb 2023

Cited by 1 | Viewed by 2655

Abstract

Injection moulding (IM) is an important industrial process, known to be the most used plastic formation technique. Demand for faster cycle times and higher product customisation is driving interest in additive manufacturing (AM) as a new method for mould tool manufacturing. The use of AM offers advantages such as greater design flexibility and conformal cooling of components to reduce cycle times and increase product precision. However, shortcomings of metal additive manufacturing, such as porosity and residual stresses, introduce uncertainties about the reliability and longevity of AM tooling. The injection moulding process relies on high volumes of produced parts and a minimal amount of tool failures. This paper reviews the demands for tool condition monitoring systems for AM-manufactured mould tools; although tool failures in conventionally manufactured tooling are rare, they do occur, usually due to cracking, deflection, and channel blockages. However, due to the limitations of the AM process, metal 3D-printed mould tools are susceptible to failures due to cracking, delamination and deformation. Due to their success in other fields, acoustic emission, accelerometers and ultrasound sensors offer the greatest potential in mould tool condition monitoring. Due to the noisy machine environment, sophisticated signal processing and decision-making algorithms are required to prevent false alarms or the missing of warning signals. This review outlines the state of the art in signal decomposition and both data- and model-based approaches to determination of the current state of the tool, and how these can be employed for IM tool condition monitoring. The development of such a system would help to ensure greater industrial uptake of additive manufacturing of injection mould tooling, by increasing confidence in the technology, further improving the efficiency and productivity of the sector. Full article

(This article belongs to the Special Issue Soft Sensors and Sensing Techniques)

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