Publikační činnost Katedry automatizační techniky a řízení / Publications of Department of Control Systems and Instrumentation (352)

Permanent URI for this collectionhttp://hdl.handle.net/10084/64776

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry automatizační techniky a řízení (352) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

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Now showing 1 - 20 out of 94 results

Transverse cracking signal characterization in CFRP laminates using modal acoustic emission and digital image correlation techniques
(Elsevier, 2024) Šofer, Michal; Cienciala, Jakub; Šofer, Pavel; Paška, Zbyněk; Fojtík, František; Fusek, Martin; Czernek, Pavel
The process of formation and subsequent propagation of transverse cracks in 90 degrees plies of carbon-fiber laminated composites was studied using modal acoustic emission approach and digital image correlation techniques. The results from modal acoustic emission approach, which included a newly developed processing tool for acoustic emission waveforms, provided information for identification and subsequent characterization or localization of signals originating from transverse cracking by analysis of the separated flexural and extensional Lamb wave modes in terms of their modal parameters. The digital image correlation method served as a verification tool of the acoustic emission data outputs in the terms of activity of significant localized events originating from the formation of the transverse crack in the 90oply. This made it possible to specify more locally the accompanying activity belonging to the formation or propagation of the magistral transverse crack. The manuscript also presents results related to the evolution of flexural/extensional wave modal parameters as the function of loading force for both [0/0/0/90]S and [90/0/0/0]S panels. It can be concluded that the detection of transverse cracks requires the need for applying a more complex acoustic emission data analysis methodology, while the standard parametric analysis, including the waveform peak frequency, is not sufficient. The presented methodology may serve as a basis for development of robust analysis tool capable of detecting the investigated phenomena.
Hardware implementation of a solar-powered buck-boost converter for enhanced cathodic protection using Texas Instruments C2000 board
(IEEE, 2024) Fekik, Arezki; Mahdal, Miroslav; Hamida, Mohamed Lamine; Ghanes, Malek; Vaidyanathan, Sundarapandian; Bousbaine, Amar; Denoun, Hakim
This article delves into the hardware implementation of a buck-boost converter on a Texas Instruments C2000 board, tailored for impressed current cathodic protection to safeguard submerged metal structures against corrosion. Impressed current cathodic protection is vital for combating corrosion in buried or submerged metal structures, where a reliable power supply is crucial. The use of solar energy captured by photovoltaic panels emerges as an environmentally sustainable and economically viable solution for this critical application. The paper examines the design, hardware implementation, and system performance, focusing on the integration of the Texas Instruments C2000 board which is, pivotal for the automation and success of the impressed current cathodic protection system. The developed work aims to advance the sustainability of submerged metal structures by presenting a solution combining impressed current cathodic protection with the ecological advantages of solar energy.
Application of chaotic signals for improving the performance of new generation ball mills
(IEEE, 2024) Ay, Mükremin; Kalayci, Onur; Mahdal, Miroslav; Turhan, Mücahit; Coşkun, Selçuk; Çalışkan, Fatih; Pehlivan, Ihsan; Vaidyanathan, Sundarapandian
New Generation Ball Mills have started to be preferred in milling systems regarding energy efficiencyinrecentyears.Inthisarticle, modernizationstudies havebeencarriedouttoensurethatthecurrent newgeneration ball mill (NGBM)operatesonachaoticbasis.Inexperimental studies, chaotic signals loaded on the PLCdevice moved the milling chamber chaotically on horizontal or circular axes. While the grinding chamber is moving at constant speeds in both the horizontal and circular axis in the current NGBM, the improved New Generation Ball Mill (INGBM) has gained the ability to move at constant or chaotic speeds in both horizontal and vertical axes. In this study, the milling chamber of INGBM has a constant frequency speed in the horizontal and circular axes in the first scenario, a chaotic system in the horizontal axis, and a constant frequency speed in the circular axis in the second scenario. In the third scenario, it was ensured that it was moved at constant frequency speed in the horizontal axis and with the chaotic system in the circular axis. Experimental studies were carried out on the milling of SiC powder, which was chosen as an examplein all scenarios. Sieve Analysis method and Scanning Electron Microscope (SEM) analysis methods were used when examining the ground powders. For both methods, the best results were obtained in the horizontal axis constant frequency speed (35 Hz) and circular axis chaotic (23-27 Hz, Lorenz system) operating scenario. It is seen that INGBM is 42% better in the powder size criterion and 3.44% better in the energy efficiency criterion.
Implementation of a universal framework using design patterns for application development on microcontrollers
(MDPI, 2024) Babiuch, Marek; Foltýnek, Petr
This article focuses on the area of software development for microcontrollers and details the implementation of modern programming practices and principles in embedded systems and IoT applications. This article explains how we implemented previously unimplemented principles and applied design patterns for quality software design on microcontrollers, which are currently only used for developing applications on the higher layers of the IoT reference model. A custom modular framework for microcontrollers is presented, based on applying SOLID principles and adapting design patterns specific to the microcontrollers’ application development needs. The imple mented framework enables independent communication between modules and flexible integration of hardware components. It is designed with platform independence in mind, contributing to its wide adaptability and ease of use in diverse development environments. By applying these technological approaches, we can create applications that are not only testable and extensible in terms of application logic but also allow for easy adaptation to changes in these hardware resources. Utilizing these capa bilities represents an innovative approach to development for microcontrollers that fundamentally improves the long-term sustainability and scalability of applications.
Optimizing brushless direct current motor design: An application of the multi-objective generalized normal distribution optimization
(Elsevier, 2024) Pandya, Sundaram B.; Jangir, Pradeep; Mahdal, Miroslav; Kalita, Kanak; Chohan, Jasgurpreet Singh; Abualigah, Laith
In this study, we tackle the challenge of optimizing the design of a Brushless Direct Current (BLDC) motor. Utilizing an established analytical model, we introduced the Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) method, a biomimetic approach based on Pareto optimality, dominance, and external archiving. We initially tested MOGNDO on standard multi-objective benchmark functions, where it showed strong performance. When applied to the BLDC motor design with the objectives of either maximizing operational efficiency or minimizing motor mass, the MOGNDO algorithm consistently outperformed other techniques like Ant Lion Optimizer (ALO), Ion Motion Optimization (IMO), and Sine Cosine Algorithm (SCA). Specifically, MOGNDO yielded the most optimal values across efficiency and mass metrics, providing practical solutions for real-world BLDC motor design. The MOGNDO source code is available at: https://github.com/kanak02/MOGNDO.
A new hyperjerk system with a half line equilibrium: Multistability, period doubling reversals, antimonotonocity, electronic circuit, FPGA design, and an application to image encryption
(IEEE, 2024) Sambas, Aceng; Mahdal, Miroslav; Vaidyanathan, Sundarapandian; Ovilla-Martínez, Brisbane; Tlelo-Cuautle, Esteban; Abd El-Latif, Ahmed A.; Abd-El-Atty, Bassem; Benkouide, Khaled; Bonny, Talal
A hyperjerk system pertains to a dynamical system regulated by an ordinary differential equation of nth order, where n >= 4. The main contribution of this work is the finding of a new autonomous hyperjerk system with a half line equilibrium. The mathematical framework of the proposed hyperjerk system contains eight terms with an absolute function nonlinearity. The essential dynamic characteristics of the model are explored, encompassing analysis of equilibrium points and their stability, depiction of the phase trajectories, illustration of bifurcation patterns, and visualization of Lyapunov exponent graphs. Our finding shows that the new 4D hyperjerk system exhibits special behavior like multistability, period doubling reversals and antimonotonocity. The proposed hyperjerk system has been implemented with an electronic circuit using MultiSim 14.0. Moreover, the FPGA implementation of the proposed hyperjerk system is performed by applying two numerical methods: Forward Euler and Trapezoidal. Experimental attractors are given from an oscilloscope by using the Zybo Z7-20 FPGA development board, which are in good agreement with the MATLAB and MultiSim 14.0 simulations. Finally, based on the chaotic dynamical behavior of the proposed chaotic hyperjerk system, a new image encryption approach is proposed. The experimental outcomes of the presented encryption algorithm prove its efficiency and security.
Multiple control policy in unreliable two-phase bulk queueing system with active Bernoulli feedback and vacation
(MDPI, 2024) Niranjan, S. P.; Latha, S. Devi; Mahdal, Miroslav; Karthik, Krishnasamy
In this paper, a bulk arrival and two-phase bulk service with active Bernoulli feedback, vacation, and breakdown is considered. The server provides service in two phases as mandatory according to the general bulk service rule, with minimum bulk size ' a ' and maximum bulk size ' b '. In the first essential service (FES) completion epoch, if the server fails, with probability 'delta ' , then the renewal of the service station is considered. On the other hand, if there is no server failure, with a probability ' 1 - delta ' , then the server switches to a second essential service (SES) in succession. A customer who requires further service as feedback is given priority, and they join the head of the queue with probability beta. On the contrary, a customer who does not require feedback leaves the system with a probability ' 1- beta '. If the queue length is less than ' a ' after SES, the server may leave for a single vacation with probability ' 1 - beta '. When the server finds an inadequate number of customers in the queue after vacation completion, the server becomes dormant. After vacation completion, the server requires some time to start service, which is attained by including setup time. The setup time is initiated only when the queue length is at least ' a '. Even after setup time completion, the service process begins only with a queue length 'N' (N > b). The novelty of this paper is that it introduces an essential two-phase bulk service, immediate Bernoulli feedback for customers, and renewal service time of the first essential service for the bulk arrival and bulk service queueing model. We aim to develop a model that investigates the probability-generating function of the queue size at any time. Additionally, we analyzed various performance characteristics using numerical examples to demonstrate the model's effectiveness. An optimum cost analysis was also carried out to minimize the total average cost with appropriate practical applications in existing data transmission and data processing in LTE-A networks using the DRX mechanism.
Multibody simulation model as part of digital twin architecture: Stewart platform example
(IEEE, 2024) Walica, Dominik; Noskievič, Petr
The digital twin is considered a new and promising concept whose added value is seen mainly in end applications. However, the benefit of considering the digital twin application since the early development of the system seems not to be stressed enough. The ability to choose system components, methods, and tools can have a symbiotic effect during system integration. This work describes a development process of a Stewart platform digital twin based on its multibody simulation model created in Matlab/Simulink. Although the multibody simulation model is useful in the design phase, after adjustments and verification, it can also be reused as a virtual entity of the digital twin. This integration is enabled by the methods, tools, and system architecture selected for the purpose of including a digital twin. This is considered to be the main contribution of this paper to emerging methodologies for the development of mechatronic systems and its digital twins. However, practical integration comes with challenges that are related to the model fidelity and synchronisation of the virtual and physical entities and are important to overcome in order to employ the system in the real applications.
Live event detection for people's safety using NLP and deep learning
(IEEE, 2024) Sen, Amrit; Rajakumaran, Gayathri; Mahdal, Miroslav; Usharani, Shola; Rajasekharan, Vezhavendhan; Vincent, Rajiv; Sugavanan, Karthikeyan
Today, humans pose the greatest threat to society by getting involved in robbery, assault, or homicide activities. Such circumstances threaten the people working alone at night in remote areas especially women. Any such kind of threat in real time is always associated with a sound/noise which may be used for an early detection. Numerous existing measures are available but none of them sounds efficient due to lack of accuracy, delays in exact prediction of threat. Hence a novel software-based prototype is developed to detect threats from a person's surrounding sound/noise and automatically alert the registered contacts of victims by sending email, SMS, WhatsApp messages through their smartphones without any other hardware components. Audio signals from Kaggle dataset are visualized, analyzed using Exploratory Data Analytics (EDA) techniques. By feeding EDA outcomes into various Deep Learning models: Long short-term memory (LSTM), Convolutional Neural Networks (CNN) yields accuracy of 96.6% in classifying the audio-events.
Influence of meta-atom geometry on the occurrence of local resonance regions in two-dimensional finite phononic structures
(Polska Akademia Nauk, Instytut fizyki, 2023) Garus, Sebastian; Sochacki, Wojciech; Garus, Justyna; Šofer, Michal; Šofer, Pavel; Gruszka, K. M.
In this work, the influence of different cross-sections of meta-atoms and their distribution on the occurrence of local resonance regions in inter-meta-atomic spaces of finite phononic structures was investigated. Software based on the Mathematica package was designed and implemented using the finite difference algorithm in the time domain to simulate mechanical wave propagation in phononic structures. Then, for the recorded time series from the inter-meta-atomic spaces, resonant frequency distributions were determined using Fourier transforms, and an analysis of the differences in frequency distributions depending on the location of the inter-meta-atomic space was carried out.
Exploring deep learning methods for computer vision applications across multiple sectors: Challenges and future trends
(Tech Science Press, 2023) Ganesh, Narayanan; Shankar, Rajendran; Mahdal, Miroslav; Murugan, Janakiraman Senthil; Chohan, Jasgurpreet Singh; Kalita, Kanak
Computer vision (CV) was developed for computers and other systems to act or make recommendations based on visual inputs, such as digital photos, movies, and other media. Deep learning (DL) methods are more successful than other traditional machine learning (ML) methods in CV. DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization, object detection, and face recognition. In this review, a structured discussion on the history, methods, and applications of DL methods to CV problems is presented. The sector-wise presentation of applications in this paper may be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV. This review will provide readers with context and examples of how these techniques can be applied to specific areas. A curated list of popular datasets and a brief description of them are also included for the benefit of readers.
Acoustic emission and infrared thermography study of low strain tensile behaviour of AISI 304L stainless steel
(Polska Akademia Nauk, Instytut Metalurgii i Inżynierii Materiałowej, 2023) Sapietová, Alžbeta; Raček, Marek; Dekýš, Vladimír; Sapieta, Milan; Sága, Milan; Šofer, Pavel
In-situ study of deformation behaviour and mechanisms occurring during early stages of deformation is of a great practical importance. Low stacking fault energy materials, as is the case of AISI 304L, show non-linear deformation characteristics way below the bulk yield point. Shockley partial dislocations, formation of stacking faults respectively, resulting in creation of shear bands and ε-martensite transformation are the mechanisms occurring in the low strains in the studied steel. Acoustic emission and infrared thermography have been used in this study to investigate the deformation kinetics at the low strain stages of slow strain rate tensile tests. Acoustic emission cumulative energy together with the tracking of specimen maximum temperature have been found to be very useful in-situ techniques both supplementing each other in the sense of the sensitivity to different mechanisms. Mechanical, acoustic emission and infrared thermography results are discussed in detail with respect to potential occurred mechanism.
Point sampling net: Revolutionizing instance segmentation in point cloud data
(IEEE, 2023) Gomathi, Nandhagopal; Rajathi, Krishnamoorthi; Mahdal, Miroslav; Elangovan, Muniyandy
Today, there is a great need for 3D instance segmentation, which has several uses in robotics and augmented reality. Unlike projective observations like 2D photographs, 3D models offer a metric reconstruction of the sceneries without occlusion or scale ambiguity of the environment. In agriculture, understanding Plant growth phenotyping enhances comprehension of complex genetic features and acceler ates the advancement of contemporary breeding and smart farming. A reduction in crop production quality is caused by leaf diseases in agriculture. In order to increase productivity in the agricultural industry, it is therefore possible to automate the recognition of leaf diseases. Diverse leaf disease patterns affect the detection’s accuracy in the majority of systems. During phenotyping, 3D PCs (PC) of components of plants like the stems and leaves are segmented in order to follow autonomous growth and estimate the level of stress the crop has experienced. This research proposed a Point Sampling Method with occupancy grid representation for segmenting PCs of different plant species, which was developed. To handle unordered input sets, this approach mainly relies on the application of the single symmetric function max pooling. In reality, a set of optimization functions are used by the network to choose points which is more curious or instructive from the PC and encapsulate the selection reason, and the fully connected layers, used for shape classification or shape segmentation, integrate these learned ideal significances hooked on a global descriptor regarding the overall shape. After being trained on the Point Sampling Network-created plant dataset, the network can simultaneously realize semantic and leaf instance segmentation.
An adaptive sleep apnea detection model using multi cascaded atrous-based deep learning schemes with hybrid artificial humming bird pity beetle algorithm
(IEEE, 2023) Aswath, Selvaraj; Sundaram, Valarmathi Ravichandran Shanmuga; Mahdal, Miroslav
Obstructive Sleep Apnea (OSA) is the cessation in breathing that must be identified as early as possible to save the patient’s life. Apart from physical diagnosis, a deep learning model can serve the purpose of detecting the apnea swiftly. The detection largely depends upon biological signals such as ECG, EEG, EMG, etc. Because of the high dimensionality nature of the bio signals, feature extraction is very critical in detecting sleep apnea. Many such feature extraction models were fragile to resolve the complexity issue and failed to reduce the non-robustness nature. To surmount all these issues, a novel adaptive deep learning-based model is designed for detecting the sleep apnea. Here two feature sets have been extracted from the ECG signals: Spectral features through Short Term Fourier Transform (STFT) and QRS analysis followed by an auto encoder to extract the deep temporal features. The novel Artificial Hummingbird Pity Beetle Algorithm (AHPBA) is proposed to choose the optimal features and weight parameters, which assists in concatenation of the two feature sets. Then these fused features were given into Multi Cascaded Atrous based Deep Learning Schemes (MCA-DLS) for classification purpose, then it is further optimized by AHPBA by maximizing the variance. MCA-DLS have performed well compared to classifying the signals individually using One Dimensional Convolutional Neural Networks (1DCNN), Long Short-Term Memory (LSTM) and Deep Neural Networks (DNN) as the average accuracy of MCA-DLS is 94.51% whereas the other three provides an average accuracy of 90.83%, 91.98%, and 93.25% respectively for the considered datasets. By using AHPBA the average accuracy of MCA-DLS was enhanced to 96.4%, which is higher than the conventional optimization techniques which are discussed in the result section.
Improving power quality in grid-connected photovoltaic systems: A comparative analysis of model predictive control in three-level and two-level inverters
(MDPI, 2023) Gada, Saliha; Fekik, Arezki; Mahdal, Miroslav; Vaidyanathan, Sundarapandian; Maidi, Ahmed; Bouhedda, Ali
The Single-Stage Grid-Connected Solar Photovoltaic (SSGC-SPV) topology has recently gained significant attention, as it offers promising advantages in terms of reducing overall losses and installation costs. We provide a comprehensive overview of the system components, which include the photovoltaic generator, the inverter, the Incremental Conductance Maximum Power Point Tracking (IC-MPPT) algorithm, and the PI regulator for DC bus voltage control. Moreover, this study presents detailed system configurations and control schemes for two types of inverters: 2L-3PVSI and 3L-3PNPC. In order to perform a comparative study between the two structures, we subjected them to the same irradiation profile using the same grid configuration. The Photovoltaic Array (PVA) irradiance is increased instantaneously, in 0.2 s, from 400 W/m2 to 800 W/m2, is kept at 800 W/m2 for 0.2 s, is then gradually decreased from 800 W/m2 to 200 W/m2 in 0.2 s, is then kept at 200 W/m2 for 0.2 s, and is then finally increased to 1000 W/m2 for 0.2 s. We explain the operational principles of these inverters and describe the various switching states involved in generating output voltages. To achieve effective control, we adopt the Finite Set-Model Predictive Control (FS-MPC) algorithm, due to the benefits of excellent dynamic responsiveness and precise current tracking abilities. This algorithm aims to minimise the cost function, while taking into account the dynamic behaviour of both the PV system and the inverter, including any associated delays. To evaluate the performance of the FS-MPC controller, we compare its application in the three-level inverter configuration with the two-level inverter setup. The DC bus voltage is maintained at 615 V using the PI controller. The objective is to achieve a Total Harmonic Distortion (THD) below 5%, with reference to the IEEE standards. The 2L-3PVSI inverter is above the threshold at an irradiance of 200 W/m2. The 3L-3PNPC inverter offers a great THD percentage, meaning improved quality of the power returned to the grid.
Enhanced dual-selection krill herd strategy for optimizing network lifetime and stability in wireless sensor networks
(MDPI, 2023) Balaram, Allam; Babu, Rajendiran; Mahdal, Miroslav; Fathima, Dowlath; Panwar, Neeraj; Ramesh, Janjhyam Venkata Naga; Elangovan, Muniyandy
Wireless sensor networks (WSNs) enable communication among sensor nodes and require efficient energy management for optimal operation under various conditions. Key challenges include maximizing network lifetime, coverage area, and effective data aggregation and planning. A longer network lifetime contributes to improved data transfer durability, sensor conservation, and scalability. In this paper, an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource efficient WSNs with minimal overhead is introduced. The proposed approach increases overall energy utilization and reduces inter-node communication, addressing energy conservation challenges in node deployment and clustering for WSNs as optimization problems. A dynamic layering mechanism is employed to prevent repetitive selection of the same cluster head nodes, ensuring effective dual selection. Our algorithm is designed to identify the optimal solution through enhanced exploitation and exploration processes, leveraging a modified krill-based clustering method. Comparative analysis with benchmark approaches demonstrates that the proposed model enhances network lifetime by 23.21%, increases stable energy by 19.84%, and reduces network latency by 22.88%, offering a more efficient and reliable solution for WSN energy management.
Study of non-linear impulsive neutral fuzzy delay differential equations with non-local conditions
(MDPI, 2023) Gunasekar, Tharmalingam; Thiravidarani, Jothivelu; Mahdal, Miroslav; Raghavendran, Prabakaran; Venkatesan, Arikrishnan; Elangovan, Muniyandy
This manuscript aims to investigate the existence and uniqueness of fuzzy mild solutions for non-local impulsive neutral functional differential equations of both first and second order, incorporating finite delay. Furthermore, the study explores the properties of fuzzy set-valued mappings of a real variable, where these mappings exhibit characteristics such as normality, convexity, upper semi-continuity, and compact support. The application of the Banach fixed-point theorem is employed to derive the results. The research extensively employs fundamental concepts from fuzzy set theory, functional analysis, and the Hausdorff metric. Additionally, an illustrative example is provided to exemplify the practical implementation of the proposed concept.
A comparative study of fuzzy domination and fuzzy coloring in an optimal approach
(MDPI, 2023) Meenakshi, Annamalai; Kannan, Adhimoolam; Mahdal, Miroslav; Karthik, Krishnasamy; Guráš, Radek
An optimal network refers to a computer or communication network designed, configured, and managed to maximize efficiency, performance, and effectiveness while minimizing cost and resource utilization. In a network design and management context, optimal typically implies achieving the best possible outcomes between various factors. This research investigated the use of fuzzy graph edge coloring for various fuzzy graph operations, and it focused on the efficacy and efficiency of the fuzzy network product using the minimal spanning tree and the chromatic index of the fuzzy network product. As a network made of nodes and vertices, measurement with vertices is a parameter for domination, and edge measurement is a parameter for edge coloring, so we used these two parameters in the algorithm. This paper aims to identify an optimal network that can be established using product outcomes. This study shows a way to find an optimal fuzzy network based on comparative optimal parameter domination and edge coloring, which can be elaborated with applications. An algorithm was generated using an optimal approach, which was subsequently implemented in the form of applications.
Compact automatic controlled internal combustion engine cogeneration system based on natural gas with waste heat recovery from the combustion process
(Elsevier, 2023) Pawlenka, Tomáš; Juránek, Martin; Klaus, Pavel; Beseda, Marek; Buráň, Michal; Suchánek, Miroslav; Sehnoutka, Petr; Kulhánek, Jiří
This paper is related to cogeneration, or combined heat and power systems (CHP) and its development, which is based on an already used and low-cost internal combustion engine ICE with a fuel system redesigned for the injection of natural gas. The main role of this system is heating and electricity production and is mainly designed for small or medium-sized households or family houses. Heat is recovered from the engine's cooling circuit and its exhaust system using a special exhaust heat exchanger. The entire process is automatically controlled to keep the output heat transfer fluid at the required temperature and to keep the engine temperature within the operating range. This fluid is then used for heating the building or domestic hot water DHW. As a power generation unit - PGU, a three-phase asynchronous motor with the power of 12.5 kW was used. The theoretical charging current can be around 400 A. Part of the development is the design of control loops, which are implemented in the main control system. This control system can be connected to a smart home energy management system SHEMS and is designed for fully automatic operation. The functionality of all operating states and conditions was supported by testing and measurements. The paper includes an analysis of the energy balance from testing and measurements. The maximum overall efficiency of the CHP can reach up to 87% in operation mode, with a heating power output of 15 kW and an electrical power output of 4 kW.
Integrated edge deployable fault diagnostic algorithm for the Internet of Things (IoT): A methane sensing application
(MDPI, 2023) Kumar, S. Vishnu; Mary, G. Aloy Anuja; Mahdal, Miroslav
The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model’s performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology’s experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.

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