Wyniki wyszukiwania - BazTech

1

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.

2

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.

3

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

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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.

4

Fabrication of a test rig for gearbox fault simulation and diagnosis

Dubaish Asaad Abdulhussein, Jaber Alaa Abdulhady

Diagnostyka

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2023

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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.

5

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.

6

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.

7

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.

8

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

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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.

9

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.

10

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

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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.

11

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.

12

Fault detection for DFIG based on sliding mode observer of new reaching law

Li RuiQi, Yu WenXin, Wang JunNian, Lu Yang, Jiang Dan, Zhong GuoLiang, Zhou ZuanBo

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 3

art. no. e137389

EN

For fault detection of doubly-fed induction generator (DFIG), in this paper, a method of sliding mode observer (SMO) based on a new reaching law (NRL) is proposed. The SMO based on the NRL (NRL- SMO) theoretically eliminates system chatter caused by the reaching law and can be switched in time with system interference in terms of robustness and smoothness. In addition, the sliding mode control law is used as the index of fault detection. Firstly, this paper gives the NRL with the theoretically analyzes. Secondly, according to the mathematical model of DFIG, NRL-SMO is designed, and its analysis of stability and robustness are carried out. Then this paper describes how to choose the optimal parameters of the NRL-SMO. Finally, three common wind turbine system faults are given, which are DFIG inter-turn stator fault, grid voltage drop fault, and rotor current sensor fault. The simulation models of the DFIG under different faults is established. The simulation results prove that the superiority of the method of NRL-SMO in state tracking and the feasibility of fault detection.

13

Detection and classification of short-circuit faults on a transmission line using current signal

Coban Melih, Tezcan Suleyman S.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 4

art. no. e137630

EN

This study offers two Support Vector Machine (SVM) models for fault detection and fault classification, respectively. Different short circuit events were generated using a 154 kV transmission line modeled in MATLAB/Simulink software. Discrete Wavelet Transform (DWT) is performed to the measured single terminal current signals before fault detection stage. Three level wavelet energies obtained for each of three-phase currents were used as input features for the detector. After fault detection, half cycle (10 ms) of three-phase current signals was recorded by 20 kHz sampling rate. The recorded currents signals were used as input parameters for the multi class SVM classifier. The results of the validation tests have demonstrated that a quite reliable, fault detection and classification system can be developed using SVM. Generated faults were used to training and testing of the SVM classifiers. SVM based classification and detection model was fully implemented in MATLAB software. These models were comprehensively tested under different conditions. The effects of the fault impedance, fault inception angle, mother wavelet, and fault location were investigated. Finally, simulation results verify that the offered study can be used for fault detection and classification on the transmission line.

14

A new index for damage identification in beam structures based on modal parameters

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

Archive of Mechanical Engineering

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2021

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LXVIII, nr 4

375--394

EN

The structural damages can lead to structural failure if they are not identified at early stages. Different methods for detecting and locating the damages in structures have been always appealing to designers in the field. Due to direct relation between the stiffness, natural frequency, and mode shapes in the structure, the modal parameters could be used for the purpose of detecting and locating the damages in structures. In the current study, a new damage indicator named “DLI” is proposed, using the mode shapes and their derivatives. A finite element model of a beam is used, and the numerical model is validated against experimental data. The proposed index is investigated for two beams with different support conditions and the results are compared with those of two well-known indices – MSEBI and CDF. To show the capability and accuracy of the proposed index, the damages with low severity at various locations of the structures containing the elements near the supports were investigated. The results under noisy conditions are investigated by considering 3% and 5% noise on modal data. The results show a high level of accuracy of the proposed index for identifying the location of the damaged elements in beams.

15

Seismic fault detection with progressive transfer learning

Zhou Ruoshui, Yao Xingmiao, Wang Yaojun, Hu Guangmin, Yu Fucai

Acta Geophysica

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2021

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Vol. 69, no. 6

2187--2203

EN

Fault detection of seismic data is a key step in seismic data interpretation. Many techniques have got good seismic fault detection results by supervised deep learning, which assumes that the training data and the prediction data have a similar data distribution. However, the seismic data distributions are different when the prediction data is far away from the training data set even in the same work area, which results in an irrational fault detection result. In order to solve this problem, we first propose a progressive learning framework to update the training data set, which can reduce the difference between the training data set and the prediction data. In addition, we propose a fault label correctness measure index to improve the stability of the framework. Finally, we introduce domain-adversarial neural network to reduce the impact of data distribution differences and integrate it into the progressive learning framework. We perform fault detection on actual seismic data: compared with the traditional deep learning model, our method can improve the fault continuity and obtain more fault details.

16

Faults detection and classification on parallel transmission lines using modified Clarke’s transformation-ANN approach

Saini Makmur, Yunus Shiddiq, Sulthan Ahmed Rizal, Djalal Muh.Ruswandi, Mustafa Mohd. Wasir, Rahimuddin Rahimuddin, Kitta Ikhlas

Przegląd Elektrotechniczny

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2020

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R. 96, nr 4

23--27

EN

This paper introduces a comparative study for fault detection and classification on parallel transmission line using cascade forward and feed forward back propagation. Both calculations were based on discrete wavelet transform (DWT) and Clarke’s transformation. Daubechies4 mother wavelet (Db4) was applied to decompose coefficients of wavelet transforms coefficients (WTC) and wavelet energy coefficients (WEC) of high frequency signals. The coefficients were inputs for training of neural network back-propagation (BPNN). The results showed that the feed forward back propagation algorithm of Artificial Neural Network (ANN) models responded better than Cascade forward back propagation algorithm models, particularly in fault detection and classification on parallel transmission. The results showed that the proposed method for fault analysis was able to classify all the faults on the parallel transmission line rapidly and correctly.

PL

W pracy przedstawiono badanie porównawcze wykrywania i klasyfikacji uszkodzeń równoległej linii przesyłowej z wykorzystaniem propagacji kaskadowej do przodu i do tyłu. Oba obliczenia oparto na dyskretnej transformacie falkowej (DWT) i transformacji Clarke'a. Falkę macierzystą Daubechies4 (Db4) zastosowano do dekompozycji współczynników przekształceń falkowych (WTC) i współczynników energii falkowej (WEC) sygnałów wysokiej częstotliwości. Współczynniki stanowiły dane wejściowe do szkolenia propagacji wstecznej sieci neuronowej (BPNN). Wyniki pokazały, że algorytm propagacji wstecznego sprzężenia zwrotnego modeli sztucznej sieci neuronowej (ANN) zareagował lepiej niż modele algorytmu kaskadowego propagacji wstecznej, szczególnie w wykrywaniu błędów i klasyfikacji w transmisji równoległej. Wyniki pokazały, że zaproponowana metoda analizy uszkodzeń była w stanie szybko i poprawnie sklasyfikować wszystkie uszkodzenia na równoległej linii przesyłowej.

17

Wykrywanie uszkodzeń w konstrukcjach inżynierskich przy zastosowaniu metody dyskretnej transformacji falkowej

Knitter-Piątkowska Anna, Guminiak Michał

Przegląd Budowlany

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2020

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R. 91, nr 11

61--64

PL

Badania związane z wykrywaniem uszkodzeń i osłabień elementów konstrukcyjnych stanową bardzo ważny element kompleksowej analizy budowli inżynierskich. W analizie identyfikacji uszkodzeń wiodącą rolę odgrywają tzw. metody nieniszczące, które pozwalają dostatecznie precyzyjnie zlokalizować powstałe uszkodzenia. Prezentowana praca poświęcona jest zastosowaniu dyskretnej transformacji falkowej w procesie lokalizacji uszkodzeń konstrukcji. Dowolne uszkodzenie, np. w postaci lokalnego osłabienia sztywności konstrukcji (pęknięcia), jest przyczyną zaburzenia w rejestrowanym sygnale odpowiedzi - ugięciu, deformacji przekroju lub np. przyspieszeniu wybranego punktu konstrukcji. Zaburzenie sygnału jest na tyle małe, że dopiero jego przetworzenie za pomocą analizy falkowej pozwala zlokalizować miejsce uszkodzenia. Zaletą przedstawionej procedury jest wykorzystanie wyłącznie sygnału odpowiedzi rzeczywistej konstrukcji uszkodzonej. Przedstawiono krótki przegląd dotychczasowych analiz konstrukcji płytowych (płyt cienkich).

EN

Research related to the detection of damage and weakening of structural elements is a very important element of a comprehensive analysis of engineering structures. In the analysis of damage identification, the leading role is played by the so-called non-destructive methods that allow for sufficiently precise localization of the damage. The presented work is devoted to the application of the discrete wavelet transformation (DWT) to the process of identification and localization damages in structures. Any damage, e.g. in the form of a local weakening of the structure stiffness (cracks), causes disturbances in the recorded response signal - deflection, deformation of the cross-section or e.g. acceleration of a selected point of the structure. However, the signal disturbance is so small that only its processing by means of wavelet analysis allows to locate the damage site. The advantage of the presented procedure is the use of the response signal only of the real - damaged structure. The presented work is an overview of the results obtained so far. The slabs were analyzed as the basic surface structural systems that form the building structure.

18

Residual life estimation of structural beam using experimental and numerical modal analysis methods

Siva Gandam Anand, Ramakrishna Shinigam

Journal of Mechanical and Energy Engineering

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2020

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Vol. 4, No 2

135--146

EN

A structural beam is a common element in many mechanical structures such as ship propeller shaft, crane boom, and aircraft wings. In the present paper experimental and numerical modal analysis are carried out for estimating the damage, geometric location of the damage, severity of damage and residual life of structural beam to prevent unexpected failures of mechanical structures. Experimental and numerical modal analysis results for healthy and cracked beam are compared for validation of numerical methodology used in the present paper. Experimental modal analysis is performed on both healthy and cracked beam with the help of impact hammer, acceleration sensor and FFT (Fast Fourier Transformer) analyzer associated with EDM (Engineering Data Management) software. Modal tests are conducted using impact method on selected locations of the entire healthy and cracked beam to find the first three natural frequencies, which are used to detect the presence of damage and geometric location of the damage. Three parametric studies are carried out to know the effect of crack depth, crack location and crack orientation on the natural frequencies of the cracked beam. Finally, the residual life of a healthy and cracked beam was estimated using Basiquin’s equation and finite element analysis software called ANSYS 18.1.

19

Faults detection based on fuzzy concepts for vibrations monitoring in gas turbine

Alili Bachir, Hafaifa Ahmed, Iratni Abdelhamid

Diagnostyka

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2020

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Vol. 21, No. 4

67--77

EN

The use of new technologies in modern industry improves productivity but induces complexity in the industrial system. This complexity makes it vulnerable to faults, which requires significant expense in terms of safety, reliability and availability. Indeed, a diagnostic operation is essential for the operational safety and availability of these industrial systems. This diagnostic operation is based on two important functions which are the detection and localization of anomalies, which consists to verifying the consistency of the data taken in real time from the installation with a reliable model, to ensure the good performance of the monitoring system. Hence, the diagnosis of gas turbines is a main component for making maintenance decisions for this type of machine. In this paper, the faults detection approach based on fuzzy logic is applied for the vibrations monitoring of a gas turbine, in order to monitor their operating state by including the detection and occurrence of vibration faults, thus using determined fault indicators based on the input/output variables of the examined gas turbine. In this work, the investigation results of fuzzy fault detection approach applied on gas turbine vibration are presented, based on the actual data recorded in the different gas turbine operating modes. However, analysis of the defect detection results was performed in order to determine the influence of these vibration defects on the deferent operating modes of the examined machine. This makes it possible to find the causes of failures and then to deduce the actions to follow the operational safety of the examined turbine.

20

Modelling and parameter identification of steel–concrete composite beams in 3D rigid finite element method

Abramowicz Małgorzata, Berczyński Stefan, Wróblewski Tomasz

Archives of Civil and Mechanical Engineering

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2020

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Vol. 20, no. 4

47--70

EN

This study presents spatial vibration modelling of steel–concrete composite beams. Structures of this type are commonly used as elements of composite floors and primary carrying girders in bridge structures. Two-dimensional models used to date did not enable analysis of all eigenmodes, specifically torsional, flexural horizontal, and distortional. A discrete computational model was developed in the convention of the rigid finite element method, the so-called RFEM model. It was assumed that the concrete slab and the steel I-section would be modelled separately. This approach realistically reflects the actual performance of the connection, comprising studs connecting the concrete slab and the steel section. The model was used to analyse two steel–concrete composite beams with different connector spacings. The paper presents the results of experiments conducted on the two composite beams. Their dynamic characteristics, including frequency and vibration modes, were determined with impulse response methods. Based on experimental research, identification of connection parameters with substitute longitudinal moduli of elasticity of reinforced concrete was conducted. A comparison of experimental results with those calculated with the model confirmed their good agreement.