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Sensing Technology and Applications for Industrial Maintenance and Automation
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Sensing Technology and Applications for Industrial Maintenance and Automation

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 11116

Special Issue Editors

Dr. Miguel Delgado-Prieto

E-Mail Website
Guest Editor
Automatic Control Department, Universitat Politècnica de Catalunya, 08222 Terrassa, Spain
Interests: cyber-physical systems; fault detection and diagnosis methods; machine and deep learning; electrical machines and drives
Special Issues, Collections and Topics in MDPI journals
Dr. Athanasios Karlis

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Komotini, Greece
Interests: electrical machines and drives; diagnostics of electrical machines; renewable energies and smart grids
Special Issues, Collections and Topics in MDPI journals
Dr. Toomas Vaimann

E-Mail Website
Guest Editor
Department of Electrical Power Engineering and Mechatronics, Tallinn University of Technology, 19086 Tallinn, Estonia
Interests: electrical machines and diagnostics of electrical machines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Most of the maintenance and repair tasks of industrial assets require the direct physical intervention of qualified technical personnel. However, currently, there are critical advancements being made in a set of related tasks, including the continuous recording of signals/information, the exhaustive analysis of data, the communication among platforms, the interpretation of the conditions, and even the maintenance planning among others, that represent a key objective of automation through the integration of emerging technologies.

The current state-of-the-art emerging technologies such as artificial intelligence, cognitive computing, computer vision or digital twins, among others, are promoting the deployment of industrial Internet of Things platforms and/or cyber–physical systems that can coexist with classic CIM-based industrial systems. Such new automation architectures provide new capabilities to consider industrial operations that benefit productivity, quality control, and sustainability in the whole process.

In this regard, the integration of such technologies and related procedures is expected to bring about a real revolution within the industrial maintenance and automation field by increasing industrial key performance indicators through making better choices in advance and/or eliminating inefficiencies.

Thus, the aim of this Special Issue is to gather the latest original developments in sensing technology for industrial applications in the maintenance and automation field. This Special Issue will be focused on (but is not limited to) the following topics:

  • Condition monitoring and decision support systems;
  • Process modelling and operation optimization;
  • Cyber–physical systems for industrial application supervision;
  • Cognitive computing and computer vision for process monitoring;
  • Novelty detection and incremental learning systems;
  • Multi-fault diagnosis and data fusion strategies;
  • Novel sensors and edge monitoring solutions;
  • Industrial Internet of Things platforms.

Dr. Miguel Delgado-Prieto
Dr. Athanasios Karlis
Dr. Toomas Vaimann
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

11 pages, 2021 KiB  
Communication
Temperature Compensation Method Based on Bilinear Interpolation for Downhole High-Temperature Pressure Sensors
by Yizhan Shu, Chenquan Hua, Zerun Zhao, Pengcheng Wang, Haocheng Zhang, Wenxin Yu and Haobo Yu
Sensors 2024, 24(16), 5123; https://doi.org/10.3390/s24165123 - 7 Aug 2024
Viewed by 3337
Abstract
Due to their high accuracy, excellent stability, minor size, and low cost, silicon piezoresistive pressure sensors are used to monitor downhole pressure under high-temperature, high-pressure conditions. However, due to silicon’s temperature sensitivity, high and very varied downhole temperatures cause a significant bias in [...] Read more.
Due to their high accuracy, excellent stability, minor size, and low cost, silicon piezoresistive pressure sensors are used to monitor downhole pressure under high-temperature, high-pressure conditions. However, due to silicon’s temperature sensitivity, high and very varied downhole temperatures cause a significant bias in pressure measurement by the pressure sensor. The temperature coefficients differ from manufacturer to manufacturer and even vary from batch to batch within the same manufacturer. To ensure high accuracy and long-term stability for downhole pressure monitoring at high temperatures, this study proposes a temperature compensation method based on bilinear interpolation for piezoresistive pressure sensors under downhole high-temperature and high-pressure environments. A number of calibrations were performed with high-temperature co-calibration equipment to obtain the individual temperature characteristics of each sensor. Through the calibration, it was found that the output of the tested pressure measurement system is positively linear with pressure at the same temperatures and nearly negatively linear with temperature at the same pressures, which serves as the bias correction for the subsequent bilinear interpolation temperature compensation method. Based on this result, after least squares fitting and interpolating, a bilinear interpolation approach was introduced to compensate for temperature-induced pressure bias, which is easier to implement in a microcontroller (MCU). The test results show that the proposed method significantly improves the overall measurement accuracy of the tested sensor from 21.2% F.S. to 0.1% F.S. In addition, it reduces the MCU computational complexity of the compensation model, meeting the high accuracy demand for downhole pressure monitoring at high temperatures and pressures. Full article
(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)
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19 pages, 18403 KiB  
Article
Development and Experiment of Semi-Physical Simulation Platform for Space Manipulator
by Jilong Xu, Yasheng Guo, Fucai Liu and Haoyu Huang
Sensors 2024, 24(13), 4354; https://doi.org/10.3390/s24134354 - 4 Jul 2024
Viewed by 747
Abstract
To address the extended development cycle, high costs, and maintenance difficulties associated with existing microgravity simulation methods, this study has developed a semi-physical simulation platform for robotic arms tailored to different gravity environments and loading conditions. The platform represents difficult-to-model joints as physical [...] Read more.
To address the extended development cycle, high costs, and maintenance difficulties associated with existing microgravity simulation methods, this study has developed a semi-physical simulation platform for robotic arms tailored to different gravity environments and loading conditions. The platform represents difficult-to-model joints as physical objects, while the easily modeled components are simulated based on principles of similarity. In response to the strong coupling, nonlinearity, and excess force disturbance issues in the electric variable load loading system, a fractional-order linear active disturbance rejection control algorithm was employed. The controller parameters were tuned using an improved particle swarm algorithm with modified weight coefficients, and experimental results demonstrate that a fractional-order linear active disturbance rejection control improves response speed and disturbance rejection performance compared to linear sliding mode control. The study investigated the differences in the drive force of joint motors in space robotic arms under varying gravity environments and loading conditions. Experimental results indicate that load torque is the primary influencing factor on joint motor drive force, while radial force serves as a secondary influencing factor. Additionally, when the axis of the joint motor is perpendicular to the ground, it can, to some extent, simulate microgravity conditions on the ground. Full article
(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)
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21 pages, 10785 KiB  
Article
Vibration Signal Noise-Reduction Method of Slewing Bearings Based on the Hybrid Reinforcement Chameleon Swarm Algorithm, Variate Mode Decomposition, and Wavelet Threshold (HRCSA-VMD-WT) Integrated Model
by Zhuang Li, Xingtian Yao, Cheng Zhang, Yongming Qian and Yue Zhang
Sensors 2024, 24(11), 3344; https://doi.org/10.3390/s24113344 - 23 May 2024
Cited by 1 | Viewed by 877
Abstract
To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic [...] Read more.
To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm’s (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA’s superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance. Full article
(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)
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20 pages, 5966 KiB  
Article
Design of Experiments to Compare the Mechanical Properties of Polylactic Acid Using Material Extrusion Three-Dimensional-Printing Thermal Parameters Based on a Cyber–Physical Production System
by Miguel Castillo, Roberto Monroy and Rafiq Ahmad
Sensors 2023, 23(24), 9833; https://doi.org/10.3390/s23249833 - 14 Dec 2023
Cited by 3 | Viewed by 1185
Abstract
The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend [...] Read more.
The material extrusion 3D printing process known as fused deposition modeling (FDM) has recently gained relevance in the additive manufacturing industry for large-scale part production. However, improving the real-time monitoring of the process in terms of its mechanical properties remains important to extend the lifespan of numerous critical applications. To enhance the monitoring of mechanical properties during printing, it is necessary to understand the relationship between temperature profiles and ultimate tensile strength (UTS). This study uses a cyber–physical production system (CPPS) to analyze the impact of four key thermal parameters on the tensile properties of polylactic acid (PLA). Layer thickness, printing speed, and extrusion temperature are the most influential factors, while bed temperature has less impact. The Taguchi L-9 array and the full factorial design of experiments were implemented along with the deposited line’s local fused temperature profile analysis. Furthermore, correlations between temperature profiles with the bonding strength during layer adhesion and part solidification can be stated. The results showed that layer thickness is the most important factor, followed by printing speed and extrusion temperature, with very close influence between each other. The lowest impact is attributed to bed temperature. In the experiments, the UTS values varied from 46.38 MPa to 56.19 MPa. This represents an increase in the UTS of around 17% from the same material and printing design conditions but different temperature profiles. Additionally, it was possible to observe that the influence of the parameter variations was not linear in terms of the UTS value or temperature profiles. For example, the increase in the UTS at the 0.6 mm layer thickness was around four times greater than the increase at 0.4 mm. Finally, even when it was found that an increase in the layer temperature led to an increase in the value of the UTS, for some of the parameters, it could be observed that it was not the main factor that caused the UTS to increase. From the monitoring conditions analyzed, it was concluded that the material requires an optimal thermal transition between deposition, adhesion, and layer solidification in order to result in part components with good mechanical properties. A tracking or monitoring system, such as the one designed, can serve as a potential tool for reducing the anisotropy in part production in 3D printing systems. Full article
(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)
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14 pages, 4599 KiB  
Article
New Complementary Resonator for Permittivity- and Thickness-Based Dielectric Characterization
by Tanveerul Haq and Slawomir Koziel
Sensors 2023, 23(22), 9138; https://doi.org/10.3390/s23229138 - 12 Nov 2023
Cited by 3 | Viewed by 1578
Abstract
The design of high-performance complementary meta-resonators for microwave sensors featuring high sensitivity and consistent evaluation of dielectric materials is challenging. This paper presents the design and implementation of a novel complementary resonator with high sensitivity for dielectric substrate characterization based on permittivity and [...] Read more.
The design of high-performance complementary meta-resonators for microwave sensors featuring high sensitivity and consistent evaluation of dielectric materials is challenging. This paper presents the design and implementation of a novel complementary resonator with high sensitivity for dielectric substrate characterization based on permittivity and thickness. A complementary crossed arrow resonator (CCAR) is proposed and integrated with a fifty-ohm microstrip transmission line. The CCAR’s distinct geometry, which consists of crossed arrow-shaped components, allows for the implementation of a resonator with exceptional sensitivity to changes in permittivity and thickness of the material under test (MUT). The CCAR’s geometrical parameters are optimized to resonate at 15 GHz. The CCAR sensor’s working principle is explained using a lumped-element equivalent circuit. The optimized CCAR sensor is fabricated using an LPKF protolaser on a 0.762-mm thick dielectric substrate AD250C. The MUTs with dielectric permittivity ranging from 2.5 to 10.2 and thickness ranging from 0.5 mm to 1.9 mm are used to investigate the properties and calibrate the proposed CCAR sensor. A two-dimensional calibration surface is developed using an inverse regression modelling approach to ensure precise and reliable measurements. The proposed CCAR sensor is distinguished by its high sensitivity of 5.74%, low fabrication cost, and enhanced performance compared to state-of-the-art designs, making it a versatile instrument for dielectric characterization. Full article
(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)
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25 pages, 9879 KiB  
Article
Investigation on the Integration of Low-Cost NIR Spectrometers in Mill Flour Industries for Protein, Moisture and Ash Content Estimation
by Vasileios Boglou, Dimosthenis Verginadis and Athanasios Karlis
Sensors 2023, 23(20), 8476; https://doi.org/10.3390/s23208476 - 15 Oct 2023
Viewed by 1601
Abstract
The flour milling industry—a vital component of global food production—is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash [...] Read more.
The flour milling industry—a vital component of global food production—is undergoing a transformative phase driven by the integration of smart devices and advanced technologies. This transition promises improved efficiency, quality and sustainability in flour production. The accurate estimation of protein, moisture and ash content in wheat grains and flour is of paramount importance due to their direct impact on product quality and compliance with industry standards. This paper explores the application of Near-Infrared (NIR) spectroscopy as a non-destructive, efficient and cost-effective method for measuring the aforementioned essential parameters in wheat and flour by investigating the effectiveness of a low-cost handle NIR spectrometer. Furthermore, a novel approach using Fuzzy Cognitive Maps (FCMs) is proposed to estimate the protein, moisture and ash content in grain seeds and flour, marking the first known application of FCMs in this context. Our study includes an experimental setup that assesses different types of wheat seeds and flour samples and evaluates three NIR pre-processing techniques to enhance the parameter estimation accuracy. The results indicate that low-cost NIR equipment can contribute to the estimation of the studied parameters. Full article
(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)
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17 pages, 11182 KiB  
Article
Display of WEDM Quality Indicators of Heat-Resistant Alloy Processing in Acoustic Emission Parameters
by Sergey N. Grigoriev, Mikhail P. Kozochkin, Vladimir D. Gurin, Alexander P. Malakhinsky, Artur N. Porvatov and Yury A. Melnik
Sensors 2023, 23(19), 8288; https://doi.org/10.3390/s23198288 - 7 Oct 2023
Cited by 5 | Viewed by 830
Abstract
The widespread nature of heat-resistant alloys is associated with the difficulties in their mechanical machining. It forces the use of the wire electrical discharge machining to be wider. The productivity, roughness, and dimensions of the modified layer of the machined surfaces are indicators [...] Read more.
The widespread nature of heat-resistant alloys is associated with the difficulties in their mechanical machining. It forces the use of the wire electrical discharge machining to be wider. The productivity, roughness, and dimensions of the modified layer of the machined surfaces are indicators of the machining quality. The search for new diagnostic parameters that can expand the information content of the operational monitoring/diagnostics of wire electrical discharge machining and accompany the currently used electrical parameters’ data is an urgent research task. The article presents the studies of the relationship between the parameters of acoustic emission signals accompanying wire electrical discharge machining of heat-resistant alloys, process quality indicators, and characteristics of discharge pulses. The results are presented as mathematical expressions and graphs demonstrating the experimentally obtained dependencies. The research focuses on the formed white layer during wire electrical discharge machining. Pictures of thin cross-sections of the machined surfaces with traces of the modified layer are provided. The issues of crack formation in the modified layer and base materials are considered. Full article
(This article belongs to the Special Issue Sensing Technology and Applications for Industrial Maintenance and Automation)
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