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

Deadline for manuscript submissions: closed (20 June 2023) | Viewed by 29555

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Prof. Dr. Simon Tomažič

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Laboratory of Control Systems and Cybernetics, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: soft sensors; Raman spectroscopy; fuzzy model identification; machine learning with big data; predictive control of dynamic systems; sensor fusion; data mining; indoor positioning; autonomous mobile systems
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Special Issue Information

Dear Colleagues,

This Special Issue deals with the field of intelligent soft sensors that enable the online estimation of nonmeasurable process variables. Soft sensors or virtual sensors are common names for software algorithms in which multiple measurements are processed together. Typically, soft sensors are based on control theory and are also referred to as state observers. There may be dozens or even hundreds of measurements from hard sensors (Big Data). The interaction of signals can be used to compute new quantities that cannot be measured directly online or are difficult and expensive to measure. Soft sensors are particularly useful in data fusion, combining measurements of different characteristics and dynamics. They can be used for fault diagnosis (self-analysis, self-calibration, and self-maintenance) as well as for control applications. Well-known software algorithms that can be seen as soft sensors include, for example, Kalman filters. More recent implementations of soft sensors use neural networks, fuzzy logic, models based on evolving clustering, partial least squares, etc. In the digitized factories of the future, intelligent sensors represent one of the core building blocks for automating and optimizing production, as they make production more efficient in every respect.

Prof. Dr. Simon Tomažič
Guest Editor

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Keywords

soft sensors
data fusion
fault detection
process control
Kalman filter
neural networks
fuzzy logic
evolving clustering
modelling
big data

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Published Papers (12 papers)

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Editorial

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3 pages, 164 KiB

Open AccessEditorial

Intelligent Soft Sensors

by Simon Tomažič

Sensors 2023, 23(15), 6895; https://doi.org/10.3390/s23156895 - 3 Aug 2023

Viewed by 1433

Abstract

In this Special Issue, we embark on a journey into the exciting field of intelligent soft sensors, and take a deep dive into the groundbreaking advances and potential that these software algorithms have introduced in various fields [...] Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

Research

Jump to: Editorial

22 pages, 5936 KiB

Open AccessArticle

Halfway to Automated Feeding of Chinese Hamster Ovary Cells

by Simon Tomažič and Igor Škrjanc

Sensors 2023, 23(14), 6618; https://doi.org/10.3390/s23146618 - 23 Jul 2023

Cited by 1 | Viewed by 1668

Abstract

This paper presents a comprehensive study on the development of models and soft sensors required for the implementation of the automated bioreactor feeding of Chinese hamster ovary (CHO) cells using Raman spectroscopy and chemometric methods. This study integrates various methods, such as partial least squares regression and variable importance in projection and competitive adaptive reweighted sampling, and highlights their effectiveness in overcoming challenges such as high dimensionality, multicollinearity and outlier detection in Raman spectra. This paper emphasizes the importance of data preprocessing and the relationship between independent and dependent variables in model construction. It also describes the development of a simulation environment whose core is a model of CHO cell kinetics. The latter allows the development of advanced control algorithms for nutrient dosing and the observation of the effects of different parameters on the growth and productivity of CHO cells. All developed models were validated and demonstrated to have a high robustness and predictive accuracy, which were reflected in a 40% reduction in the root mean square error compared to established methods. The results of this study provide valuable insights into the practical application of these methods in the field of monitoring and automated cell feeding and make an important contribution to the further development of process analytical technology in the bioprocess industry. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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21 pages, 4275 KiB

Open AccessArticle

Development and Optimization of a Novel Soft Sensor Modeling Method for Fermentation Process of Pichia pastoris

by Bo Wang, Jun Liu, Ameng Yu and Haibo Wang

Sensors 2023, 23(13), 6014; https://doi.org/10.3390/s23136014 - 29 Jun 2023

Cited by 2 | Viewed by 1578

Abstract

This paper introduces a novel soft sensor modeling method based on BDA-IPSO-LSSVM designed to address the issue of model failure caused by varying fermentation data distributions resulting from different operating conditions during the fermentation of different batches of Pichia pastoris. First, the problem of significant differences in data distribution among different batches of the fermentation process is addressed by adopting the balanced distribution adaptation (BDA) method from transfer learning. This method reduces the data distribution differences among batches of the fermentation process, while the fuzzy set concept is employed to improve the BDA method by transforming the classification problem into a regression prediction problem for the fermentation process. Second, the soft sensor model for the fermentation process is developed using the least squares support vector machine (LSSVM). The model parameters are optimized by an improved particle swarm optimization (IPSO) algorithm based on individual differences. Finally, the data obtained from the Pichia pastoris fermentation experiment are used for simulation, and the developed soft sensor model is applied to predict the cell concentration and product concentration during the fermentation process of Pichia pastoris. Simulation results demonstrate that the IPSO algorithm has good convergence performance and optimization performance compared with other algorithms. The improved BDA algorithm can make the soft sensor model adapt to different operating conditions, and the proposed soft sensor method outperforms existing methods, exhibiting higher prediction accuracy and the ability to accurately predict the fermentation process of Pichia pastoris under different operating conditions. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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17 pages, 1371 KiB

Open AccessArticle

A Soft Sensor Model of Sintering Process Quality Index Based on Multi-Source Data Fusion

by Yuxuan Li, Weihao Jiang, Zhihui Shi and Chunjie Yang

Sensors 2023, 23(10), 4954; https://doi.org/10.3390/s23104954 - 21 May 2023

Cited by 2 | Viewed by 2091

Abstract

In complex industrial processes such as sintering, key quality variables are difficult to measure online and it takes a long time to obtain quality variables through offline testing. Moreover, due to the limitations of testing frequency, quality variable data are too scarce. To solve this problem, this paper proposes a sintering quality prediction model based on multi-source data fusion and introduces video data collected by industrial cameras. Firstly, video information of the end of the sintering machine is obtained via the keyframe extraction method based on the feature height. Secondly, using the shallow layer feature construction method based on sinter stratification and the deep layer feature extraction method based on ResNet, the feature information of the image is extracted at multi-scale of the deep layer and the shallow layer. Then, combining industrial time series data, a sintering quality soft sensor model based on multi-source data fusion is proposed, which makes full use of multi-source data from various sources. The experimental results show that the method effectively improves the accuracy of the sinter quality prediction model. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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24 pages, 7329 KiB

Open AccessArticle

Self-Sensing Variable Stiffness Actuation of Shape Memory Coil by an Inferential Soft Sensor

by Bhagoji Bapurao Sul, Dhanalakshmi Kaliaperumal and Seung-Bok Choi

Sensors 2023, 23(5), 2442; https://doi.org/10.3390/s23052442 - 22 Feb 2023

Cited by 4 | Viewed by 1938

Abstract

Self-sensing actuation of shape memory alloy (SMA) means to sense both mechanical and thermal properties/variables through the measurement of any internally changing electrical property such as resistance/inductance/capacitance/phase/frequency of an actuating material under actuation. The main contribution of this paper is to obtain the stiffness from the measurement of electrical resistance of a shape memory coil during variable stiffness actuation thereby, simulating its self-sensing characteristics by developing a Support Vector Machine (SVM) regression and nonlinear regression model. Experimental evaluation of the stiffness of a passive biased shape memory coil (SMC) in antagonistic connection, for different electrical (like activation current, excitation frequency, and duty cycle) and mechanical input conditions (for example, the operating condition pre-stress) is done in terms of change in electrical resistance through the measurement of the instantaneous value. The stiffness is then calculated from force and displacement, while by this scheme it is sensed from the electrical resistance. To fulfill the deficiency of a dedicated physical stiffness sensor, self-sensing stiffness by a Soft Sensor (equivalently SVM) is a boon for variable stiffness actuation. A simple and well-proven voltage division method is used for indirect stiffness sensing; wherein, voltages across the shape memory coil and series resistance provide the electrical resistance. The predicted stiffness of SVM matches well with the experimental stiffness and this is validated by evaluating the performances such as root mean squared error (RMSE), the goodness of fit and correlation coefficient. This self-sensing variable stiffness actuation (SSVSA) provides several advantages in applications of SMA: sensor-less systems, miniaturized systems, simplified control systems and possible stiffness feedback control. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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16 pages, 658 KiB

Open AccessEditor’s ChoiceArticle

Stress Detection Using Frequency Spectrum Analysis of Wrist-Measured Electrodermal Activity

by Žiga Stržinar, Araceli Sanchis, Agapito Ledezma, Oscar Sipele, Boštjan Pregelj and Igor Škrjanc

Sensors 2023, 23(2), 963; https://doi.org/10.3390/s23020963 - 14 Jan 2023

Cited by 13 | Viewed by 2888

Abstract

The article deals with the detection of stress using the electrodermal activity (EDA) signal measured at the wrist. We present an approach for feature extraction from EDA. The approach uses frequency spectrum analysis in multiple frequency bands. We evaluate the proposed approach using the 4 Hz EDA signal measured at the wrist in the publicly available Wearable Stress and Affect Detection (WESAD) dataset. Seven existing approaches to stress detection using EDA signals measured by wrist-worn sensors are analysed and the reported results are compared with ours. The proposed approach represents an improvement in accuracy over the other techniques studied. Moreover, we focus on time to detection (TTD) and show that our approach is able to outperform competing techniques, with fewer data points. The proposed feature extraction is computationally inexpensive, thus the presented approach is suitable for use in real-world wearable applications where both short response times and high detection performance are important. We report both binary (stress vs. no stress) as well as three-class (baseline/stress/amusement) results. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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19 pages, 6165 KiB

Open AccessArticle

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

by Shengyuan Xiao, Shuo Wang, Liang Ge, Hengxiang Weng, Xin Fang, Zhenming Peng and Wen Zeng

Sensors 2023, 23(2), 859; https://doi.org/10.3390/s23020859 - 11 Jan 2023

Cited by 5 | Viewed by 2985

Abstract

High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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17 pages, 466 KiB

Open AccessArticle

Improved Individualized Patient-Oriented Depth-of-Hypnosis Measurement Based on Bispectral Index

by Gorazd Karer and Igor Škrjanc

Sensors 2023, 23(1), 293; https://doi.org/10.3390/s23010293 - 27 Dec 2022

Cited by 4 | Viewed by 2030

Abstract

Total intravenous anesthesia is an anesthesiologic technique where all substances are injected intravenously. The main task of the anesthesiologist is to assess the depth of anesthesia, or, more specifically, the depth of hypnosis (DoH), and accordingly adjust the dose of intravenous anesthetic agents. However, it is not possible to directly measure the anesthetic agent concentrations or the DoH, so the anesthesiologist must rely on various vital signs and EEG-based measurements, such as the bispectral (BIS) index. The ability to better measure DoH is directly applicable in clinical practice—it improves the anesthesiologist’s assessment of the patient state regarding anesthetic agent concentrations and, consequently, the effects, as well as provides the basis for closed-loop control algorithms. This article introduces a novel structure for modeling DoH, which employs a residual dynamic model. The improved model can take into account the patient’s individual sensitivity to the anesthetic agent, which is not the case when using the available population-data-based models. The improved model was tested using real clinical data. The results show that the predictions of the BIS-index trajectory were improved considerably. The proposed model thus seems to provide a good basis for a more patient-oriented individualized assessment of DoH, which should lead to better administration methods that will relieve the anesthesiologist’s workload and will benefit the patient by providing improved safety, individualized treatment, and, thus, alleviation of possible adverse effects during and after surgery. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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17 pages, 2429 KiB

Open AccessArticle

A Simplified Algorithm for Setting the Observer Parameters for Second-Order Systems with Persistent Disturbances Using a Robust Observer

by Alejandro Rincón, Fredy E. Hoyos and John E. Candelo-Becerra

Sensors 2022, 22(18), 6988; https://doi.org/10.3390/s22186988 - 15 Sep 2022

Cited by 1 | Viewed by 1601

Abstract

The properties of the convergence region of the estimation error of a robust observer for second-order systems are determined, and a new algorithm is proposed for setting the observer parameters, considering persistent but bounded disturbances in the two observation error dynamics. The main contributions over closely related studies of the stability of state observers are: (i) the width of the convergence region of the observer error for the unknown state is expressed in terms of the interaction between the observer parameters and the disturbance terms of the observer error dynamics; (ii) it was found that this width has a minimum point and a vertical asymptote with respect to one of the observer parameters, and their coordinates were determined. In addition, the main advantages of the proposed algorithm over closely related algorithms are: (i) the definition of observer parameters is significantly simpler, as the fulfillment of Riccati equation conditions, solution of LMI constraints, and fulfillment of eigenvalue conditions are not required; (ii) unknown bounded terms are considered in the dynamics of the observer error for the known state. Finally, the algorithm is applied to a model of microalgae culture in a photobioreactor for the estimation of biomass growth rate and substrate uptake rate based on known concentrations of biomass and substrate. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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15 pages, 688 KiB

Open AccessArticle

Development of an Adaptive Computer-Aided Soft Sensor Diagnosis System for Assessment of Executive Functions

by Katalin Mohai, Csilla Kálózi-Szabó, Zoltán Jakab, Szilárd Dávid Fecht, Márk Domonkos and János Botzheim

Sensors 2022, 22(15), 5880; https://doi.org/10.3390/s22155880 - 6 Aug 2022

Cited by 5 | Viewed by 2448

Abstract

The main objective of the present study is to highlight the role of technological (soft sensor) methodologies in the assessment of the neurocognitive dysfunctions specific to neurodevelopmental disorders (for example, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), and specific learning disorder). In many cases neurocognitive dysfunctions can be detected in neurodevelopmental disorders, some of them having a well-defined syndrome-specific clinical pattern. A number of evidence-based neuropsychological batteries are available for identifying these domain-specific functions. Atypical patterns of cognitive functions such as executive functions are present in almost all developmental disorders. In this paper, we present a novel adaptation of the Tower of London Test, a widely used neuropsychological test for assessing executive functions (in particular planning and problem-solving). Our version, the Tower of London Adaptive Test, is based on computer adaptive test theory (CAT). Adaptive testing using novel algorithms and parameterized task banks allows the immediate evaluation of the participant’s response which in turn determines the next task’s difficulty level. In this manner, the subsequent item is adjusted to the participant’s estimated capability. The adaptive procedure enhances the original test’s diagnostic power and sensitivity. By measuring the targeted cognitive capacity and its limitations more precisely, it leads to more accurate diagnoses. In some developmental disorders (e.g., ADHD, ASD) it could be very useful in improving the diagnosis, planning the right interventions, and choosing the most suitable assistive digital technological service. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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22 pages, 3766 KiB

Open AccessArticle

Test Strategy Optimization Based on Soft Sensing and Ensemble Belief Measurement

by Wenjuan Mei, Zhen Liu, Lei Tang and Yuanzhang Su

Sensors 2022, 22(6), 2138; https://doi.org/10.3390/s22062138 - 10 Mar 2022

Cited by 7 | Viewed by 2164

Abstract

Resulting from the short production cycle and rapid design technology development, traditional prognostic and health management (PHM) approaches become impractical and fail to match the requirement of systems with structural and functional complexity. Among all PHM designs, testability design and maintainability design face critical difficulties. First, testability design requires much labor and knowledge preparation, and wastes the sensor recording information. Second, maintainability design suffers bad influences by improper testability design. We proposed a test strategy optimization based on soft-sensing and ensemble belief measurements to overcome these problems. Instead of serial PHM design, the proposed method constructs a closed loop between testability and maintenance to generate an adaptive fault diagnostic tree with soft-sensor nodes. The diagnostic tree generated ensures high efficiency and flexibility, taking advantage of extreme learning machine (ELM) and affinity propagation (AP). The experiment results show that our method receives the highest performance with state-of-art methods. Additionally, the proposed method enlarges the diagnostic flexibility and saves much human labor on testability design. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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20 pages, 1979 KiB

Open AccessArticle

Sensor Selection and State Estimation for Unobservable and Non-Linear System Models

by Thijs Devos, Matteo Kirchner, Jan Croes, Wim Desmet and Frank Naets

Sensors 2021, 21(22), 7492; https://doi.org/10.3390/s21227492 - 11 Nov 2021

Cited by 9 | Viewed by 4174

Abstract

To comply with the increasing complexity of new mechatronic systems and stricter safety regulations, advanced estimation algorithms are currently undergoing a transformation towards higher model complexity. However, more complex models often face issues regarding the observability and computational effort needed. Moreover, sensor selection is often still conducted pragmatically based on experience and convenience, whereas a more cost-effective approach would be to evaluate the sensor performance based on its effective estimation performance. In this work, a novel estimation and sensor selection approach is presented that is able to stabilise the estimator Riccati equation for unobservable and non-linear system models. This is possible when estimators only target some specific quantities of interest that do not necessarily depend on all system states. An Extended Kalman Filter-based estimation framework is proposed where the Riccati equation is projected onto an observable subspace based on a Singular Value Decomposition (SVD) of the Kalman observability matrix. Furthermore, a sensor selection methodology is proposed, which ranks the possible sensors according to their estimation performance, as evaluated by the error covariance of the quantities of interest. This allows evaluating the performance of a sensor set without the need for costly test campaigns. Finally, the proposed methods are evaluated on a numerical example, as well as an automotive experimental validation case. Full article

(This article belongs to the Special Issue Intelligent Soft Sensors)

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