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Intelligent Sensors for Industrial Process Monitoring

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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: closed (31 July 2023) | Viewed by 24338

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Special Issue Editors

Prof. Dr. Chao Tan

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: process measurement and instrumentation; process tomography
Special Issues, Collections and Topics in MDPI journals

Dr. Yong Bao

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Guest Editor

School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: ultrasound measurement technique; industrial process tomography

Dr. Marco J Da Silva

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Guest Editor

Graduate Program in Electrical and Computer Engineering, Federal University of Technology-Paraná, Curitiba, PR, Brazil
Interests: sensors and sensor systems; electronic instrumentation; sensor data processing; multidimensional sensing; process imaging; multiphase flow

Dr. Sebastian Felix Reinecke

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Guest Editor

Helmholtz-Zentrum Dresden-Rossendorf, Institute of Fluid Dynamics, 01328 Dresden, Germany
Interests: process measurement and instrumentation; autonomous sensor systems; multiphase flow measurement

Special Issue Information

Dear Colleagues,

Nowadays, the fast growth of intelligent manufacturing and Industrial 4.0 require sophisticated and agile sensors to collect rich process parameters for process diagnosis, optimization, and control. Compared with traditional temperature and pressure sensors, the newly demanded sensors are expected to collect more complex data, such as the velocity, composition, and viscosity of fluids, and thereafter, the extract process status from the time-series of those data in real-time, which were not achievable through simple sensors. This requires the sensors to operate in various new sensing principles and collect the 2D/3D distributed information in space and also in time and/or spectrum. Additionally, new process diagnosis and information processing techniques are required to analyze the complex dynamic process.

This Special Issue solicits papers which focus on sensors for industrial process measurement and diagnosis, including, but not limited to, sensing principles, sensing methods, sensing systems, and sensing modeling. In addition, process analysis papers are also welcome, such as process modeling with machine learning and data-driven methods, as well as sensor data treatment algorithms. Both review articles and original research papers are solicited.

Prof. Dr. Chao Tan
Dr. Yong Bao
Dr. Marco Jose da Silva
Dr. Sebastian Felix Reinecke
Guest Editors

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Keywords

industrial process measurement
process imaging
fluid flow measurement
process tomography
intelligent sensors
process diagnosis

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Published Papers (10 papers)

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12 pages, 2977 KiB

Open AccessArticle

Miniaturized Sensors for Detection of Ethanol in Water Based on Electrical Impedance Spectroscopy and Resonant Perturbation Method—A Comparative Study

by Angelo Leo, Anna Grazia Monteduro, Silvia Rizzato, Angelo Milone and Giuseppe Maruccio

Sensors 2022, 22(7), 2742; https://doi.org/10.3390/s22072742 - 2 Apr 2022

Cited by 5 | Viewed by 3105

Abstract

The development of highly sensitive, portable and low-cost sensors for the evaluation of ethanol content in liquid is particularly important in several monitoring processes, from the food industry to the pharmaceutical industry. In this respect, we report the optimization of two sensing approaches based on electrical impedance spectroscopy (EIS) and complementary double split ring resonators (CDSRRs) for the detection of ethanol in water. Miniaturized EIS sensors were realized with interdigitated electrodes, and the ethanol sensing was carried out in liquid solutions without any functionalization of the electrodes. Impedance fitting analysis, with an equivalent circuit over a frequency range from 100 Hz to 1 MHz, was performed to estimate the electric parameters, which allowed us to evaluate the amount of ethanol in water solutions. On the other hand, complementary double split ring resonators (CDSRRs) were optimized by adjusting the device geometry to achieve higher quality factors while operating at a low fundamental frequency despite the small size (useful for compact electronic packaging). Both sensors were found to be efficient for the detection of low amounts of ethanol in water, even in the presence of salts. In particular, EIS sensors proved to be effective in performing a broadband evaluation of ethanol concentration and are convenient when low cost is the priority. On the other end, the employment of split ring resonators allowed us to achieve a very low limit of detection of 0.2 v/v%, and provides specific advantages in the case of known environments where they can enable fast real-time single-frequency measurements. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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17 pages, 13734 KiB

Open AccessArticle

Metal Surface Defect Detection Method Based on TE01 Mode Microwave

by Meng Shi, Lijian Yang, Songwei Gao and Guoqing Wang

Sensors 2022, 22(13), 4848; https://doi.org/10.3390/s22134848 - 27 Jun 2022

Cited by 6 | Viewed by 1964

Abstract

With the aim of addressing the difficulty of detecting metal surface cracks and corrosion defects in complex environments, we propose a detection method for metal surface cracks and corrosion defects based on TE01-mode microwave. The microwave detection equations of cracks and corrosion defects were established by the Maxwell equations when the TE01 mode was excited by microwaves, and the relationship model between the defect size and the microwave characteristic quantity was established. A finite integral simulation model was established to analyze the influence of defects on the microwave electric field, magnetic field, and tube wall current in the rectangular waveguide, as well as the return loss at the defect; an experimental platform for the detection of metal surface cracks and corrosion defects was built. The absolute value of the return loss of the microwave reflected wave increased, and with the increase of the defect width, the microwave detection frequency at the defect decreased. The TE01-mode microwave has good detection ability for metal surface cracks and corrosion defects and can effectively detect cracks with a width of 0.3 mm. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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23 pages, 3933 KiB

Open AccessArticle

Real-Time Detection and Short-Term Prediction of Blast Furnace Burden Level Based on Space-Time Fusion Features

by Yanli Chen, Zhipeng Chen, Weihua Gui and Chunhua Yang

Sensors 2022, 22(14), 5412; https://doi.org/10.3390/s22145412 - 20 Jul 2022

Cited by 2 | Viewed by 1845

Abstract

Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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24 pages, 7399 KiB

Open AccessArticle

High-Precision Real-Time Detection of Blast Furnace Stockline Based on High-Dimensional Spatial Characteristics

by Pan Liu, Zhipeng Chen, Weihua Gui and Chunhua Yang

Sensors 2022, 22(16), 6245; https://doi.org/10.3390/s22166245 - 19 Aug 2022

Cited by 1 | Viewed by 1777

Abstract

The real-time, continuity, and accuracy of blast furnace stockline information are of great significance in reducing energy consumption and improving smelting efficiency. However, the traditional mechanical measurement method has the problem of measuring point discontinuity, while the radar measurement method exhibits problems such as weak anti-interference ability, low accuracy, and poor stability. Therefore, a high-dimensional, spatial feature stockline detection method based on the maximum likelihood radial basis function model (MLRBFM) and structural dynamic self-optimization RBF neural network (SDSO-RBFNN) is proposed. Firstly, the discrete time series joint partition method is used to extract the time dimension periodic features of the blast furnace stockline. Based on MLRBFM, the high-dimensional spatial features of the stockline are then obtained. Finally, an SDSO-RBFNN is constructed based on an eigen orthogonal matrix and a right triangular matrix decomposition (QR) direct clustering algorithm with spatial–temporal features as input, so as to obtain continuous, high-precision stockline information. Both the simulation results and industrial validation indicate that the proposed method can provide real-time and accurate stockline information, and has great practical value for industrial production. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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15 pages, 7267 KiB

Open AccessArticle

Small-Diameter Tube Wall Damage-Detection Method Based on TE01 Mode Microwave

by Meng Shi, Lijian Yang, Songwei Gao and Guoqing Wang

Sensors 2022, 22(17), 6476; https://doi.org/10.3390/s22176476 - 28 Aug 2022

Cited by 5 | Viewed by 1373

Abstract

Accidents occur frequently in urban gas pipelines, and pipeline damage detection is an important means of ensuring pipeline safety. Aiming at the problem that the small diameter pipeline is difficult to detect, this paper proposes a detection method for the inner wall damage of a small-diameter pipeline based on the TE01 mode microwave and uses the TE01 mode to detect the inner wall damage of the pipeline by the terminal short-circuit reflection method. By analyzing the transition of microwave propagation mode at the defect, based on the Maxwell equation and the field distribution equation of the TE01 mode microwave in the pipe and the pipe wall current equation, the microwave reflection coefficient at the defect is established when the microwave distortion modes at the defect are TE and TM modes. A small-diameter pipeline simulation model is established, and the influence of the electric field, magnetic field, wall current distribution, and reflected wave reflection coefficient in the pipeline when inner wall defects of different widths are analyzed using the finite integral theory during microwave detection of the TE01 mode. An experimental platform for the microwave detection of small-diameter pipes was built to detect defects on the inner walls of pipes with different widths. The results show that the inner wall defect causes the electric field, magnetic field, current propagation period, and energy distribution of the TE01 mode microwave propagated in the pipe to be distorted, and the microwave reflection coefficient and return loss exhibit a significant frequency shift with the change in the defect width. The experimental and simulation results had a good consistency. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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16 pages, 4331 KiB

Open AccessArticle

Development of Online Tool Wear-Out Detection System Using Silver–Polyester Thick Film Sensor for Low-Duty Cycle Machining Operations

by Jegadeeshwaran Rakkiyannan, Lakshmipathi Jakkamputi, Mohanraj Thangamuthu, Abhishek D. Patange and Sakthivel Gnanasekaran

Sensors 2022, 22(21), 8200; https://doi.org/10.3390/s22218200 - 26 Oct 2022

Cited by 4 | Viewed by 1898

Abstract

This paper deals with the design and development of a silver–polyester thick film sensor and associated system for the wear-out detection of single-point cutting tools for low-duty cycle machining operations. Conventional means of wear-out detection use dynamometers, accelerometers, microphones, acoustic emission sensors, thermal infrared cameras, and machine vision systems that detect tool wear during the process. Direct measurements with optical instruments are accurate but affect the machining process. In this study, the use of a thick film sensor to detect wear-out for aa real-time low-duty machining operation was proposed to eliminate the limitations of the current methods. The proposed sensor monitors the tool condition accurately as the wear acts directly on the sensor, which makes the system simple and more reliable. The effect of tool temperature on the sensor during the machining operation was also studied to determine the displacement/deformation of tracing and the polymer substrate at different service temperatures. The proposed tool wear detection system with the silver–polyester thick film sensor mounted directly on the cutting tool tip proved to be highly capable of detecting the tool wear with good reliability. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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18 pages, 7172 KiB

Open AccessArticle

Advanced Control Systems in Industry 5.0 Enabling Process Mining

by Alessandro Massaro

Sensors 2022, 22(22), 8677; https://doi.org/10.3390/s22228677 - 10 Nov 2022

Cited by 25 | Viewed by 4080

Abstract

This paper merges new research topics in Industry 5.0 using the Business Process Modeling and Notation (BPMN) approach able to integrate Artificial Intelligence (AI) in production processes. The goal is to provide an innovative approach to model production management in industry, adopting a new “proof of concept” of advanced Process Mining (PM) automatizing decisions and optimizing machine setting and maintenance interventions. Advanced electronic sensing and actuation systems, integrating supervised and unsupervised AI algorithms, are embedded in the PM model as theoretical process workflows suggested by a Decision Support System (DSS) engine enabling an intelligent decision-making procedure. The paper discusses, as examples, two theoretical models applied to specific industry sectors, such as food processing and energy production. The proposed work provides important elements of engineering management related to the digitalization of production process matching with automated control systems setting production parameters, thus enabling the self-adapting of product quality supervision and production efficiency in modern industrial systems. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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26 pages, 10509 KiB

Open AccessArticle

Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces

by Silvia Maria Zanoli, Crescenzo Pepe and Lorenzo Orlietti

Sensors 2023, 23(8), 3966; https://doi.org/10.3390/s23083966 - 13 Apr 2023

Cited by 3 | Viewed by 2554

Abstract

In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators’ manual conduction and/or previous level 2 systems control. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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13 pages, 7097 KiB

Open AccessCommunication

Towards Real-Time Analysis of Gas-Liquid Pipe Flow: A Wire-Mesh Sensor for Industrial Applications

by Philipp Wiedemann, Felipe de Assis Dias, Manuel Trepte, Eckhard Schleicher and Uwe Hampel

Sensors 2023, 23(8), 4067; https://doi.org/10.3390/s23084067 - 18 Apr 2023

Viewed by 1723

Abstract

Real-time monitoring of gas-liquid pipe flow is highly demanded in industrial processes in the chemical and power engineering sectors. Therefore, the present contribution describes the novel design of a robust wire-mesh sensor with an integrated data processing unit. The developed device features a sensor body for industrial conditions of up to 400 °C and 135 bar as well as real-time processing of measured data, including phase fraction calculation, temperature compensation and flow pattern identification. Furthermore, user interfaces are included via a display and 4…20 mA connectivity for the integration into industrial process control systems. In the second part of the contribution, we describe the experimental verification of the main functionalities of the developed system. Firstly, the calculation of cross-sectionally averaged phase fractions along with temperature compensation was tested. Considering temperature drifts of up to 55 K, an average deviation of 3.9% across the full range of the phase fraction was found by comparison against image references from camera recordings. Secondly, the automatic flow pattern identification was tested in an air–water two-phase flow loop. The results reveal reasonable agreement with well-established flow pattern maps for both horizontal and vertical pipe orientations. The present results indicate that all prerequisites for an application in industrial environments in the near future are fulfilled. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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23 pages, 11132 KiB

Open AccessArticle

Fault-Diagnosis and Fault-Recovery System of Hall Sensors in Brushless DC Motor Based on Neural Networks

by Kenny Sau Kang Chu, Kuew Wai Chew and Yoong Choon Chang

Sensors 2023, 23(9), 4330; https://doi.org/10.3390/s23094330 - 27 Apr 2023

Cited by 10 | Viewed by 2844

Abstract

This paper proposes a neural-network-based framework using Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) for detecting faults and recovering signals from Hall sensors in brushless DC motors. Hall sensors are critical components in determining the position and speed of motors, and faults in these sensors can disrupt their normal operation. Traditional fault-diagnosis methods, such as state-sensitive and transition-sensitive approaches, and fault-recovery methods, such as vector tracking observer, have been widely used in the industry but can be inflexible when applied to different models. The proposed fault diagnosis using the CNN-LSTM model was trained on the signal sequences of Hall sensors and can effectively distinguish between normal and faulty signals, achieving an accuracy of the fault-diagnosis system of around 99.3% for identifying the type of fault. Additionally, the proposed fault recovery using the CNN-LSTM model was trained on the signal sequences of Hall sensors and the output of the fault-detection system, achieving an efficiency of determining the position of the phase in the sequence of the Hall sensor signal at around 97%. This work has three main contributions: (1) a CNN-LSTM neural network structure is proposed to be implemented in both the fault-diagnosis and fault-recovery systems for efficient learning and feature extraction from the Hall sensor data. (2) The proposed fault-diagnosis system is equipped with a sensitive and accurate fault-diagnosis system that can achieve an accuracy exceeding 98%. (3) The proposed fault-recovery system is capable of recovering the position in the sequence states of the Hall sensors, achieving an accuracy of 95% or higher. Full article

(This article belongs to the Special Issue Intelligent Sensors for Industrial Process Monitoring)

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