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1

An exploration of vibration based damage detection techniques for composite materials

Pathan Firoj U., Patil Milind M., Baviskar Prasad R., Aher Vishnu S.

International Journal of Applied Mechanics and Engineering

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2025

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Vol. 30, no. 3

97--113

EN

Structural damage monitoring is inevitable for the structures to perform during their intended service life adroitly. In the present review, literature related to techniques for diagnosing vibration-intensive damages have been evaluated in order to determine the material characteristics, such as stiffness and damping. Also, extensive review has been presented in the for damage detection in composite materials. The review encompasses the literature published in last 42 years, i.e., 1982 to 2024. The literature review is classified into sections as damage detection workflow, composite materials, damage detection techniques, and advanced damage detection techniques. The usage of strain energy, mode-shapes, waveform dimension, wavelet transform and updating finite element models in detection of damage are also discussed. Further, an overview of concepts, techniques, and advancement in vibration-induced damage detection are presented. The limitations of each technique are explained. An insight on advanced techniques and tools from genetic algorithm and artificial neural network regarding their employability to detect the damage is provided. This work portrays the damage detection methodologies.

2

Damage Localization Using Fiber Bragg Grating Sensors in Self-referencing Conguration: A Numerical Study

Patange Abhishek, Moaf Farzam Omidi, Fiborek Piotr, Arumuganainar Adityan, Soman Rohan

Computer Assisted Methods in Engineering and Science

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2025

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Vol. 32, no. 2

213--234

EN

This study investigates a self-referencing method for damage detection and localization using guided waves (GW) sensed by fiber Bragg grating (FBG) sensors. The research integrates advanced numerical simulations with an innovative configuration of sensors to enhance structural health monitoring (SHM). A self-referencing setup, employing FBG sensors with edge filtering method and remote bonding, enables a baseline-free damage detection approach. The methodology is validated as a proof-of-concept numerical model. The simulation framework incorporates a three-dimensional spectral element method for precise and efficient modelling of GW propagation and interactions with structural anomalies. Three different machine learning (ML) techniques are employed to detect and localize damages, demonstrating effectiveness of ML methods compared to traditional methods. The three techniques employed are decision tree, logistic model tree and random forest. Key findings highlight the effectiveness of random forest models in classifying damage states with a 98.67% accuracy. Different feature selection methods, are used to identify critical features. The proposed methodology reduces sensor requirements, lowers system complexity and cost, and enables efficient SHM solutions in extreme or large-scale environments. This work underscores the potential of ML techniques to perform detection and localization where traditional techniques fail.

3

Feasibility Study of Neural Network-based Classification of Conveyor Belt Damage Using Partial DiagBelt Data

Rzeszowska Aleksandra, Jurdziak Leszek, Błażej Ryszard

Civil and Environmental Engineering Reports

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2025

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Vol. 35, no. 3

313--324

EN

Non-invasive methods for diagnosing conveyor belts enable effective detection of damage, significantly reducing the costs associated with belt replacement. Additionally, they allow for continuous monitoring of the belts’ technical condition and degree of wear over extended periods of operation. Such solutions also enhance safety in environments where conveyor systems are used. While belt wear is an inevitable process, its rate can vary depending on specific operational conditions, such as the conveyor’s location, its length, the type of material being transported, and the belt’s operating speed. This article discusses an artificial intelligence-based approach to classifying conveyor belt damage. A two-layer neural network was implemented in the MATLAB environment using the Deep Learning Toolbox. By optimizing the network, a high level of operational efficiency was achieved, reaching an accuracy range of 80–90%. This solution opens new possibilities for precise diagnostics and monitoring of conveyor belts’ technical state, contributing to improved durability and reduced operational costs.

4

Wavelet-based stochastic finite element analysis of steel girders

Kamiński Marcin, Guminiak Michał Jan, Knitter-Piątkowska Anna, Kawa Olga

Archives of Civil Engineering

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2025

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Vol. 71, nr 2

225--242

EN

The paper presents the problem of damage detection in steel girders. Static displacements at the selected point of the structure play the role of measured variables. Structural response signal decomposition is performed according to the Mallat pyramid algorithm, which is used to perform the discrete wavelet transform (DWT). This procedure allows us to quite well determine the location of structural damage. The geometry and the placement of any defective part of the structure may have a random character. It can be assumed that the random processes occurring in the broadly understood structure mechanics are Gaussian in nature. The first four probabilistic moments are estimated using three approaches independent: semi-analytical (SAM), perturbative (SPT), and Monte-Carlo simulations (MCS). The semi-analytical random approach seems to be the most optimal due to the necessary computation time. The incorporation of the mathematical stochastic apparatus into the classical (deterministic) analysis of the statics of the structure makes it possible to estimate the reliability measures of the analyzed girder.

PL

W artykule przedstawiono problematykę wykrywania uszkodzeń dźwigarów stalowych. Rolę zmiennych mierzonych pełnią przemieszczenia statyczne w wybranym punkcie konstrukcji. Strukturalna dekompozycja sygnału odpowiedzi odbywa się zgodnie z algorytmem piramidy Mallata, który służy do wykonywania dyskretnej transformaty falkowej (DWT). Procedura ta pozwala dość dobrze określić lokalizację uszkodzeń konstrukcji. Geometria i umiejscowienie wadliwej części konstrukcji może mieć charakter losowy. Można założyć, że procesy losowe zachodzące w szeroko rozumianej mechanice konstrukcji mają charakter gaussowski. Pierwsze cztery momenty losowe szacowane są za pomocą trzech niezależnych metod: pół-analitycznej, perturbacyjnej i symulacji Monte-Carlo. Zastosowanie matematycznego aparatu stochastycznego do klasycznej (deterministycznej) analizy statyki konstrukcji umożliwia oszacowanie miar niezawodności analizowanego dźwigara stalowego.

5

Improved SOM algorithm for damage characterization based on visual sensing

Zhu Hongtao, Guo Shuyun

Archives of Civil Engineering

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2025

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Vol. 71, nr 2

129--142

EN

In the field of concrete structure health monitoring, accurately and swiftly identifying damage characteristics stands as a pivotal task. To enhance the accuracy and efficiency of concrete damage identification, this research proposes an improved Self-Organizing Map algorithm based on visual sensing. By optimizing feature extraction and representation methods, introducing novel learning strategies, and incorporating spatial attention mechanisms, the model becomes adept at capturing and identifying concrete damage features more effectively. Additionally, employing stochastic gradient descent as an optimization algorithm enhances the model training efficiency. Experimental results showcase that the model exhibits a detection time of merely 0.8 seconds, while demonstrating outstanding fitting and clustering performance, achieving an actual accuracy of 98.2%. Compared to methods based on digital image monitoring and deep learning detection, it shows an improvement of 12.7% and 31.8%, respectively. The proposed enhanced model significantly augments the accuracy and efficiency of concrete damage identification, providing an effective solution for the health monitoring of concrete structures, particularly in scenarios requiring large-scale and real-time monitoring. This advancement elevates the practicality and convenience of concrete damage detection, propelling progress in the field of building safety.

6

Application of the Discrete Wavelet Transform to damage detection in a guy cable of guyed antenna mast

Knitter-Piątkowska Anna, Przychodzki Maciej, Guminiak Michał

Archives of Civil Engineering

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2025

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Vol. 71, nr 1

187--201

EN

The paper delves into the critical issue of damage detection within the guy cable of a truss steel mast, a pivotal component in structural integrity. It introduces a model wherein damage manifests as a localized cross-section reduction in a single element of the cable. Employing Discrete Wavelet Transform (DWT), a pioneering methodology, the study scrutinizes the behavior of the affected elements through static structural analyses. Signal decomposition via the Mallat pyramid algorithm facilitates comprehensive examination. Static displacements at the connection point between the cable and the truss mast serve as the measured variables. Through systematic investigation, the paper evaluates the impact of damage size and location, external loading force, and cable tension force on the efficacy of the proposed approach. Utilizing data derived from Finite Element Method (FEM) computations for wavelet analysis the authors substantiate the findings with numerical examples, thus offering valuable insights into damage detection strategies for structural health monitoring and engineering applications.

PL

W artykule przedstawiono problem wykrywania uszkodzeń w odciągach stalowego kratowego masztu antenowego. Uszkodzenie w jednym odciągu jest modelowane jako miejscowa redukcja pola przekroju jednego elementu. Analizy statycznych odpowiedzi strukturalnych przeprowadzane są z wykorzystaniem dyskretnej transformaty falkowej (ang.: Discrete Wavelet Transform – DWT), które to podejście jest nowatorskim do badania elementów wiotkich. Zastosowano dekompozycję sygnału odpowiedzi konstrukcji (elementu konstrukcji) zgodnie z algorytmem Mallata. Mierzone zmienne to statyczne przemieszczenia punktu, w którym kabel łączy się z masztem kratownicy. Uszkodzenie kabla zostało wprowadzone jako miejscowe zmniejszenie pola poprzecznego jego przekroju. Zbadano wpływ wielkości uszkodzenia, wartości siły wzbudzenia oraz siły naciągu odciągu na skuteczność proponowanego podejścia. Dane do analizy falkowej uzyskano z wyników obliczeń metody elementów skończonych (MES) dla geometrycznie nieliniowego sformułowania zadania. Zaprezentowano przykłady numeryczne, które wykazały skuteczność zaprezentowanej metody wykrywania uszkodzeń.

7

Using drones and artificial intelligence to assess damage in aircraft assembly joints

Krysiak Marcin, Kubik Filip

Technologia i Automatyzacja Montażu

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2024

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nr 2

47--53

EN

Rapid development of Artificial Intelligence (AI) technologies in recent years has created new opportunities to address the growing challenges in the aviation industry. Machine learning and Deep Learning, particularly through Convolutional Neural Networks (CNNs), have advanced image recognition capabilities, enhancing inspection processes possibilities. This paper explores the integration of AI with drones to improve the precision, efficiency, and speed of inspections of airframe emphasizing the necessity of accurate equipment preparation and precise operational planning. The study demonstrates how AI algorithms can process high-resolution images and sensor data to identify and classify defects. The motivation for this paper is to address the critical need for more efficient inspection methods in aviation, driven by the industry's increasing demand for higher repair process throughput and stringent safety standards.

PL

Szybki rozwój technologii sztucznej inteligencji (SI) w ostatnich latach stworzył nowe możliwości radzenia sobie z rosnącymi wyzwaniami w przemyśle lotniczym. Metody uczenia maszynowego i głębokiego uczenia, szczególnie za pomocą konwolucyjnych sieci neuronowych (CNN), poprawiły zdolności rozpoznawania obrazów, usprawniając możliwości procesów inspekcji. Niniejszy artykuł opisuje propozycję integracji SI z dronami i w celu poprawy precyzji, efektywności i szybkości inspekcji płatowców podkreślając konieczność dokładnego przygotowania sprzętu i precyzyjnego planowania operacji. Tekst omawia przetwarzanie obrazów wysokiej rozdzielczości i danych z czujników, identyfikując i klasyfikując uszkodzenia. Motywacją do omówienia danego tematu jest konieczność opracowania bardziej efektywnych metod inspekcji w lotnictwie, co wynika z rosnącego zapotrzebowania na większą przepustowość procesów napraw i rygorystyczne standardy bezpieczeństwa w branży.

8

System wizyjny do analizy położenia sieci trakcyjnej względem odbieraka prądu

Konieczka Adam, Stachowiak Dorota, Garczarek Adam, Dominiak Wojciech

Przegląd Elektrotechniczny

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2024

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R. 100, nr 7

169--173

PL

W artykule przedstawiono system wizyjny do analizy położenia sieci trakcyjnej względem odbieraka prądu. Jest on przeznaczony do montażu na dachu pojazdu kolejowego. Wyposażono go w kamerę i minikomputer Raspberry Pi 3B+, który analizuje zarejestrowany obraz oraz wykorzystuje moduł GPS do rejestracji miejsc, w których wykryto nieprawidłowe ustawienie sieci trakcyjnej. Wyniki przeprowadzonych badań, także z wykorzystaniem pojazdu szynowego, wskazują na możliwość szerokiego zastosowania proponowanego systemu.

EN

The article presents a vision system for analyzing the position of the catenary in relation to the current collector. This system is designed to be mounted on the roof of a railway vehicle. The system is equipped with a camera and a Raspberry Pi 3B+ minicomputer that analyzes the recorded image and uses a GPS module to record the locations where incorrect alignment of the overhead contact line was detected. The results of the tests carried out, also using a railway vehicle, indicate the possibility of widespread use of the proposed system.

9

Synergy of remote sensing data collected with low-cost mobile mapping platform for detection and prediction of damages: a park alley case study

Pilarska-Mazurek Magdalena, Bakuła Krzysztof, Górka Jakub, Czernic Paweł, Lejzerowicz Anna, Ostrowski Wojciech, Chetverikov Borys

Archives of Civil Engineering

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2024

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Vol. 70, nr 3

619--634

EN

Data synergy involves acquiring and combining data from different sensors to achieve better problem analysis and research results. For more comprehensive data analysis, the sensors are not only mounted on one platform. Still, they should also be compatible with software and hardware, e.g. for the same timestamp registration by different sensors. The aim of this article is to propose the synergy between various remote sensing sensors including the ground penetrating radar (GPR), LiDAR (Light Detection and Ranging) sensor and three photogrammetric RGB cameras for damage detection in a pavement in a park alley. The data were acquired with a low-cost platform, in the Pole Mokotowskie Park in Warsaw, Poland. Three drives were made along the same path with the platform, so it was possible to assess the repeatability of the data. Based on the GPR data, orthophotomap, and digital terrain model (DTM) from images, an analysis of the cracks in the pavement was done. The paper proves additive value from the synergy of data collected for the alley also in the form of a common visualization of acquired data. Results presented in the article showed that using mobile mapping platform and technologies describing the situation above and below the ground level enable a more detailed analysis and inspection of the damages in the park alley.

PL

Synergia danych obejmuje pozyskiwanie i łączenie danych z różnych sensorów w celu wykonania lepszej jakości analiz i uzyskania lepszych wyników badań. W celu zapewnienia bardziej kompleksowej analizy danych, oprócz tego, że sensory są montowane na jednej platformie, powinny być one również kompatybilne z oprogramowaniem i sprzętem, np. w celu rejestracji tego samego znacznika czasu przez różne sensory. Celem tego artykułu jest zaproponowanie synergii między różnymi sensorami teledetekcyjnymi, z georadarem (GPR), czujnikiem LiDAR (Light Detection and Ranging) i trzema fotogrametrycznymi kamerami RGB do wykrywania uszkodzeń chodnika w alejce parkowej. Dane zostały pozyskane za pomocą niskokosztowej platformy w parku Pole Mokotowskie w Warszawie. Wykonano trzy przejazdy platformą po tej samej trasie, dzięki czemu możliwa była ocena powtarzalności danych. Na podstawie danych z georadaru, i numerycznego modelu terenu (NMT) ze zdjęć przeprowadzono analizę pęknięć w nawierzchni. W artykule udowodniono wartość dodaną wynikającą z synergii również w postaci wspólnej wizualizacji pozyskanych dla chodnika danych. Wyniki przedstawione w artykule wykazały, że wykorzystanie mobilnej platformy oraz technologii opisujących sytuację nad i pod poziomem gruntu umożliwia bardziej szczegółową analizę i inspekcję uszkodzeń w alejce parkowej.

10

DIC application for damage detection in FRP composite specimens based on an example of a shearing test

Ziaja Dominika, Jurek Michał, Śliwa Romana, Wiater Agnieszka, Kulpa Maciej

Archives of Civil and Mechanical Engineering

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2024

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Vol. 24, no. 1

art. no. e47, 2024

EN

Fibre reinforced polymer (FRP) composite materials are widely used in many branches of life from aerospace and automotive, through boatbuilding, mechanical and civil engineering even to art. Thanks to their lightweight, high strength, and ease of shaping are very attractive materials. The main disadvantage of FRP composites is the possibility of defects in their structures which influence on proper work of the material. These defects can appear in the production phase as well as becaused by impact. Especially sensitive to defects/damages are the responsible structures such as those used in the aerospace industry. In this paper, the application of digital image correlation (DIC) to tracking the development of strain fields in FRP composites with different types of fibres is proposed. Thanks to the use of the DIC the finding of areas where the strain distribution (caused by loads) is different than in surroundings is possible. The presented measurement technique is vision-based, non-contact, and enables full-field measurements without disturbing structure behaviour by any additional mass. This method, in combination with other non-destructive testing is potentially applicable in damage detection of the aircraft sheathing parts. However, further research in order to its application to online monitoring is required, especially considering the minimisation of equipment, supply of energy and wireless data transmission.

11

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

Zemali Zakaria, Cherroun Lakhmissi, Hadroug Nadji, Nadour Mohamed, Hafaifa Ahmed

Diagnostyka

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2023

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Vol. 24, No. 1

art. no. 2022110

EN

This paper aims to present a robust fault diagnosis structure-based observers for actuator faults in the pitch part system of the wind turbine benchmark. In this work, two linear estimators have been proposed and investigated: the Kalman filter and the Luenberger estimator for observing the output states of the pitch system in order to generate the appropriate residual between the measured positions of blades and the estimated values. An inference step as a decision block is employed to decide the existence of faults in the process, and to classify the detected faults using a predetermined threshold defined by upper and lower limits. All actuator faults in the pitch system of the horizontal wind turbine benchmark are studied and investigated. The obtained simulation results show the ability of the proposed diagnosis system to determine effectively the occurred faults in the pitch system. Estimation of the output variables is effectively realized in both situations: without and with the occurrence of faults in the studied process. A comparison between the two used observers is demonstrated.

12

Fault detection and diagnosis of photovoltaic system based on neural networks approach

Rahmoune Ben Mohamed, Iratni Abdelhamid, Amari Amel Sabrine, Hafaifa Ahmed, Colak Ilhami

Diagnostyka

|

2023

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Vol. 24, No. 3

art. no. 2023303

EN

Solar energy has become one of the most important renewable energies in the world. With the increasing installation of power plants in the world, the supervision and diagnosis of photovoltaic systems have become an important challenge with the increased occurrence of various internal and external faults. Indeed, this work proposes a new solar power plant diagnosis based on the artificial neural network approach. The developed model was to improve the performance and reliability of the power plant located in Tamanrasset, Algeria, which is subjected to varying weather conditions in terms of radiation and ambient temperature. By using the real data collected from the studied system, this approach allow to increase electricity production and address any issues that may arise quickly, ensuring uninterrupted power supply for the region. Neural networks have shown interesting results with high accuracy. This fault diagnosis approach allows to determine the time of occurrence of a fault affecting the examined PV system. Also, allow an early detection of failures and degradation of the system, which contributes to improving the productivity of this photovoltaic installation. With a significant reduction in the time needed to repair the damage caused by these faults and improve the reliability and continuity of the electrical energy production service.

13

Fabrication of a test rig for gearbox fault simulation and diagnosis

Dubaish Asaad Abdulhussein, Jaber Alaa Abdulhady

Diagnostyka

|

2023

|

Vol. 24, No. 2

art. no. 2023204

EN

Gearboxes are one of the most important and widely exposed to different types of faults in machines. Therefore, manufacturers and researchers have made significant efforts to develop different fault detection and diagnostic approaches for gearboxes. However, many research foundations, such as universities, are currently working on developing different gearbox test rigs to understand the failure mechanisms in gearboxes. As a result, in this article, a gearbox testing rig was proposed and fabricated to evaluate gear performance under lowspeed working conditions. It describes the primary mechanical apparatus and the measurement tools used during the experimental analysis of a multistage gearbox transmission system. The data-gathering equipment used to acquire the observed vibration data is also discussed. LabVIEW software was used to build a data acquisition platform using an accelerometer and a NI DAQ device. Then different vibration tests were conducted under different operating conditions, when the gearbox was healthy and then faulty, on this test rig, and the gathered vibration data were analyzed based on time domain signal analysis. The preliminary results are promising and open the horizon for simulating different gearbox test scenarios.

14

Siamese neural networks on the trail of similarity in bugs in 5G mobile network base stations

Zarębski Sebastian, Kuzmich Aliaksandr, Sitko Sebastian, Rusek Krzysztof, Chołda Piotr

Przegląd Telekomunikacyjny + Wiadomości Telekomunikacyjne

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2022

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nr 4

198--201

EN

To improve the R&D process, by reducing duplicated bug tickets, we used an idea of composing BERT encoder as Siamese network to create a system for finding similar existing tickets. We proposed several different methods of generating artificial ticket pairs, to augment the training set. Two phases of training were conducted. The first showed that only and approximate 9% pairs were correctly identified as certainly similar. Only 48% of the test samples are found to be pairs of similar tickets. With the fine-tuning we improved that result up to 81%, proving the concept to be viable for further improvements.

15

Detection of fault events in medium voltage grid based on analysis of data from fault recorders

Sajewicz Dariusz, Wójcik Augustyn, Zegadło Piotr

Przegląd Elektrotechniczny

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2022

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R. 98, nr 3

131--135

EN

The aim of this paper is to demonstrate the effectiveness of newly developed fault detection methods based on a simple statistical approach encompassing linear discriminant analysis and signal processing. Fault prediction relates to the detection of: the type of operation of the medium voltage network, leakage (damaged insulator in the line string) and a measure of the distance of ground fault in an unbranched line, in a branched line and on its branches. The conducted research confirms the high efficiency of detection faults in all areas concerned.

PL

Celem pracy jest wykazanie skuteczności nowo opracowanych metod detekcji uszkodzeń opartych na prostym podejściu statystycznym obejmującym liniową analizę dyskryminacyjną i przetwarzanie sygnałów. Przeprowadzone badania potwierdzają wysoką skuteczność wykrywania uszkodzeń we wszystkich rozpatrywanych obszarach.

16

A graph theory-based approach to the description of the process and the diagnostic system

Kościelny Jan Maciej, Bartyś Michał, Syfert Michał, Sztyber Anna

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 2

213--227

EN

The paper proposes an original, comprehensive, and methodically consistent graph theory-based approach to the description of the diagnosed process and the diagnosing system. The main baseline of the presented approach is in the dichotomous approach to diagnosing. It involves a separate description of both the process and the diagnostic system. This approach reflects the practice of designing implementable diagnostic systems. Thus, it can be seen as a proposal of a new, alternative, and, at the same time, flexible design procedure with great potential for applications. The primary motivation behind it was an attempt to circumvent the numerous limitations of well-known and well-established diagnosis approaches proposed by the communities working on fault detection and isolation (FDI) and artificial intelligence theories for diagnosis (DX). Accordingly, the paper identifies and provides an extensive discussion and a critical analysis of the existing limitations. Numerous examples and references to practical applications of the approach are indicated.

17

A data driven fault isolation method based on reference faulty situations with application to a nonlinear chemical process

Ragot José, Mourot Gilles, Kallas Maya

International Journal of Applied Mathematics and Computer Science

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2022

|

Vol. 32, no. 4

635--655

EN

The diagnosis of systems is one of the major steps in their control and its purpose is to determine the possible presence of dysfunctions, which affect the sensors and actuators associated with a system but also the internal components of the system itself. On the one hand, the diagnosis must therefore focus on the detection of a dysfunction and, on the other hand, on the physical localization of the dysfunction by specifying the component in a faulty situation, and then on its temporal localization. In this contribution, the emphasis is on the use of software redundancy applied to the detection of anomalies within the measurements collected in the system. The systems considered here are characterized by non-linear behaviours whose model is not known a priori. The proposed strategy therefore focuses on processing the data acquired on the system for which it is assumed that a healthy operating regime is known. Diagnostic procedures usually use this data corresponding to good operating regimes by comparing them with new situations that may contain faults. Our approach is fundamentally different in that the good functioning data allow us, by means of a non-linear prediction technique, to generate a lot of data that reflect all the faults under different excitation situations of the system. The database thus created characterizes the dysfunctions and then serves as a reference to be compared with real situations. This comparison, which then makes it possible to recognize the faulty situation, is based on a technique for evaluating the main angle between subspaces of system dysfunction situations. An important point of the discussion concerns the robustness and sensitivity of fault indicators. In particular, it is shown how, by non-linear combinations, it is possible to increase the size of these indicators in such a way as to facilitate the location of faults.

18

Intelligent fault diagnosis of power transmission line using fuzzy logic and artificial neural network

Touati Khaled Omer Mokhtar, Boudiaf Mohamed, Merzouk Imad, Hafaifa Ahmed

Diagnostyka

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2022

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Vol. 23, No. 4

art. no. 2022410

EN

In the industrial sector, transmission lines are an important part of the electrical grid. Thus it is important to protect it from all the different faults that may occur as soon as possible to supply the electric power continuously. This paper presents a modern solutions and a comparative study of fault detection and identification in electrical transmission lines using artificial neural network (ANN) compare to the fuzzy logic. Faults in transmission line of various types have been created using simulation model. An intelligent monitoring system (IFD: Intelligent Fault Diagnosis) was used at both ends of a 230 kV overhead transmission line, voltage and current measurements exploited as indicator data for this system. Both approaches were found to be robust, accurate and reliable to detect the fault when it occurs, to determine the fault type short circuit or opening of a power line (open circuit), to locate the fault and to determine which phase was faulted.

19

A novel method for identification of damage location in frame structures using a modal parameters-based indicator

Nashta Mina Roodgar, Taghipour Reza, Bozorgnasab Mohsen, Mirgolbabaei Hessam

Archives of Civil Engineering

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2022

|

Vol. 68, nr 3

633--643

EN

Diverse strategies for identifying and finding the damages in structures have been continuously engaging to originators within the field. Due to the direct connection between the firmness, characteristic frequency, and mode shapes within the structure, the modular parameters may well be utilized for recognizing and finding the damages in structures. In current consider, a modern damage marker named Damage Localization Index (DLI) is applied, utilizing the mode shapes and their derivative. A finite element model of a frame with twenty and thirty components has been utilized, separately. The numerical model is confirmed based on experimental information. The indicator has been explored for the damaged components of a frame with one bay. The results have been compared with those of the well-known index CDF. To demonstrate the capability and exactness of the proposed method, the damages with low seriousness at different areas of the structures are explored. The results are investigated in noisy condition, considering 3% and 5% noise on modal data. The outcomes show the high level of accuracy of the proposed method for identifying the location of the damaged elements in frames.

20

Application of wavelet transform for identification of single phase-to-ground fault in compensated networks

Osipov Dmitry S., Kovalev Vladimir Z., Arkhipova Olga V., Dolgikh Nadezda N.

Przegląd Elektrotechniczny

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2021

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R. 97, nr 6

34--38

EN

In a number of EU countries medium voltage networks operate in the compensated neutral mode. In that case an arc suppression coil is commonly shunted with a resistor. The most common type of damage to such networks is a single phase-to-ground fault. The paper presents the method for two stage identification of a line where the fault has occured. The first stage is based on the analysis of high frequency components arising under transients. At the first stage a continuous wavelet transform is used to find frequencies. The second stage involves an analysis of the steady-state mode of a single phase-to-ground fault. Based on the energy spectrum of higher harmonics a damaged line is detected. To determine the energy spectrum at the second stage of the work a wavelet packet transform is applied. Wavelet transform has a number of advantages compared with short-time Fourier transform (STFT), particularly when analyzing non-stationary modes. The proposed method can be implemented to organize digital protection against ground faults.

PL

Sieci średniego napięcia w wielu krajach UE działają w skompensowanym trybie neutralnym. W takim przypadku cewka gasząca łuk jest zwykle bocznikowana przez rezystor. Najczęstszym rodzajem uszkodzeń w takich sieciach jest zwarcie jednofazowe do ziemi. W artykule przedstawiono technikę dwuetapowej identyfikacji linii, na której nastąpiło uszkodzenie. Pierwszy etap opiera się na analizie składowych wysokiej częstotliwości powstających pod wpływem stanów nieustalonych. W pierwszym etapie do znalezienia częstotliwości używana jest ciągła transformata falkowa. Drugi etap obejmuje analizę stanu ustalonego pojedynczego zwarcia międzyfazowego. Na podstawie widma energii wyższych harmonicznych wykrywana jest uszkodzona linia. Do wyznaczenia widma energii w drugim etapie pracy stosuje się transformację pakietu falkowego. Transformacja falkowa ma wiele zalet w porównaniu z krótkotrwałą transformatą Fouriera, szczególnie w przypadku analizy modów niestacjonarnych. Zaproponowaną metodę można zaimplementować do organizacji cyfrowej ochrony przed zwarciami doziemnymi.