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Tytuł:
Budowa geologiczna doliny Białego Dunajca
Geology of the Biały Dunajec Valley (Podhale region, S Poland)
Autorzy:
Mastella, L.
Ludwiniak, M.
Klimkiewicz, D.
Powiązania:
https://bibliotekanauki.pl/articles/2074964.pdf
Data publikacji:
2012
Wydawca:
Państwowy Instytut Geologiczny – Państwowy Instytut Badawczy
Tematy:
zachodnie Karpaty centralne
synklinorium podhalańskie
strefa uskokowa Białego Dunajca
sieć uskoków
Central Western Carpathians
Podhale Synclinorium
Biały Dunajec fault zone
fault network
Opis:
The Biały Dunajec Valley is one of the large, meridionally oriented valleys cutting the Podhale Synclinorium. The tectonic origin of this valley has been suggested since the beginning of the 20th century. A large fault zone with an azimuth of about 20° has been recognized here. This zone extends to the north and cuts the Pieniny Klippen Belt, which is significantly lowered in its eastern side. The southern part of the Biały Dunajec fault zone (SBD) extends probably into the Tatra Massif (into the Mała Łąka Valley area and far to the south into the border of the Koszysta elevation and the Goryczkowa depression). The majority of faults constituting the SBD were formed during the initial phase as strike-slip faults; they were reactivated later as dip-slip faults with a prevailing dip-slip, mainly normal component. As a whole, the SBD is a scissor-like fault: in the northern part, near the Szaflary village, downfaulted is its eastern block, whereas in the southern part - its western block.
Źródło:
Przegląd Geologiczny; 2012, 60, 9; 496--505
0033-2151
Pojawia się w:
Przegląd Geologiczny
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
O wykształceniu strukturalnym Górnośląskiego Zagłębia Węglowego - urywki dyskusji bez zakończenia
On structural features of the Upper Silesian Coal Basin: Scraps of discussion with no end
Autorzy:
Teper, L.
Powiązania:
https://bibliotekanauki.pl/articles/2074613.pdf
Data publikacji:
2008
Wydawca:
Państwowy Instytut Geologiczny – Państwowy Instytut Badawczy
Tematy:
Górnośląskie Zagłębie Węglowe
strefy uskokowe
analiza strukturalna
tektonika sejsmiczna
tectonics
Upper Silesian Coal Basin
fractality of fault network
fault size distributions
fundamental faults
seismotectonics
Opis:
Concepts of Adam Kotas, who inferred that structure of the Carboniferous sedimentary complex was produced by primary faulting in the crystalline basement of the Upper Silesian Coal Basin, have been further supported by results from fractal, structural and seismotectonic analyses of the basin features. Findings revealed selfsimilarity of the fault network strongly controlled by fundamental dislocations. Geometrical attributes of fold arrays and evidences of interlayer slip, together with focal mechanism solutions of mine tremors, helped to determine location, kinematics and dynamics of the principal deep-seated faults.
Źródło:
Przegląd Geologiczny; 2008, 56, 6; 481-481
0033-2151
Pojawia się w:
Przegląd Geologiczny
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault location of distribution network with distributed generation based on Karrenbauer transform and support vector machine regression
Autorzy:
Wang, Siming
Zhao, Kaikai
Powiązania:
https://bibliotekanauki.pl/articles/24202729.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
distributed generation
distribution network fault location
fault type
Karrenbauer transform
agent prediction model
SVR
support vector regression
Opis:
As the capacity and scale of distribution networks continue to expand, and distributed generation technology is increasingly mature, the traditional fault location is no longer applicable to an active distribution network and "two-way" power flow structure. In this paper, a fault location method based on Karrenbauer transform and support vector machine regression (SVR) is proposed. Firstly, according to the influence of Karrenbauer transformation on phase angle difference before and after section fault in a low-voltage active distribution network, the fault regions and types are inferred preliminarily. Then, in the feature extraction stage, combined with the characteristics of distribution network fault mechanism, the fault feature sample set is established by using the phase angle difference of the Karrenbauer current. Finally, the fault category prediction model based on SVR was established to solve the problem of a single-phase mode transformation modulus and the indistinct identification of two-phase short circuits, then more accurate fault segments and categories were obtained. The proposed fault location method is simulated and verified by building a distribution network system model. The results show that compared with other methods in the field of fault detection, the fault location accuracy of the proposed method can reach 98.56%, which can enhance the robustness of rapid fault location.
Źródło:
Archives of Electrical Engineering; 2023, 72, 2; 461--481
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Smart Substation Network Fault Classification Based on a Hybrid Optimization Algorithm
Autorzy:
Xia, Xin
Liu, Xiaofeng
Lou, Jichao
Powiązania:
https://bibliotekanauki.pl/articles/227220.pdf
Data publikacji:
2019
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
smart substation
network fault classification
improved separation interval method (ISIM)
support vector
machine (SVM)
Anti-noise processing (ANP)
Opis:
Accurate network fault diagnosis in smart substations is key to strengthening grid security. To solve fault classification problems and enhance classification accuracy, we propose a hybrid optimization algorithm consisting of three parts: anti-noise processing (ANP), an improved separation interval method (ISIM), and a genetic algorithm-particle swarm optimization (GA-PSO) method. ANP cleans out the outliers and noise in the dataset. ISIM uses a support vector machine (SVM) architecture to optimize SVM kernel parameters. Finally, we propose the GA-PSO algorithm, which combines the advantages of both genetic and particle swarm optimization algorithms to optimize the penalty parameter. The experimental results show that our proposed hybrid optimization algorithm enhances the classification accuracy of smart substation network faults and shows stronger performance compared with existing methods.
Źródło:
International Journal of Electronics and Telecommunications; 2019, 65, 4; 657-663
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Bayesian Network Based Fault Tolerance in Distributed Sensor Networks
Autorzy:
Lokesh, B. B.
Nalini, N.
Powiązania:
https://bibliotekanauki.pl/articles/308287.pdf
Data publikacji:
2014
Wydawca:
Instytut Łączności - Państwowy Instytut Badawczy
Tematy:
Bayesian network
distributed sensor networks
fault detection
fault tolerance
fault recovery
network control
routing
Opis:
A Distributed Sensor Network (DSN) consists of a set of sensors that are interconnected by a communication network. DSN is capable of acquiring and processing signals, communicating, and performing simple computational tasks. Such sensors can detect and collect data concerning any sign of node failure, earthquakes, floods and even a terrorist attack. Energy efficiency and fault-tolerance network control are the most important issues in the development of DSNs. In this work, two methods of fault tolerance are proposed: fault detection and recovery to achieve fault tolerance using Bayesian Networks (BNs). Bayesian Network is used to aid reasoning and decision making under uncertainty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using BNs. It is also used to detect energy depletion of node, link failure between nodes, and packet error in DSN. The proposed model is used to detect faults at node, sink and network level faults (link failure and packet error). The proposed fault recovery model is used to achieve fault tolerance by adjusting the network of the randomly deployed sensor nodes based on of its probabilities. Finally, the performance parameters for the proposed scheme are evaluated.
Źródło:
Journal of Telecommunications and Information Technology; 2014, 4; 44-52
1509-4553
1899-8852
Pojawia się w:
Journal of Telecommunications and Information Technology
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Rotor fault detector of the converter-fed induction motor based on RBF neural network
Autorzy:
Kowalski, C. T.
Kaminski, M.
Powiązania:
https://bibliotekanauki.pl/articles/200439.pdf
Data publikacji:
2014
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
induction motor
rotor fault
diagnostic symptom
RBF neural network
fault detector
Opis:
This paper deals with the application of the Radial Basis Function (RBF) networks for the induction motor fault detection. The rotor faults are analysed and fault symptoms are described. Next the main stages of the design methodology of the RBF-based neural detectors are described. These networks are trained and tested using measurement data of the stator current (MCSA). The efficiency of developed RBF-NN detectors is evaluated. Furthermore, influence of neural networks complexity and parameters of the RBF activation function on the quality of data classification is shown. The presented neural detectors are tested with measurement data obtained in the laboratory setup containing the converter-fed induction motor (IM) and changeable rotors with a different degree of damages.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2014, 62, 1; 69-76
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new method for estimating fault location in radial medium voltage distribution network only using measurements at feeder beginning
Autorzy:
Ngoc-Hung, Truong
Powiązania:
https://bibliotekanauki.pl/articles/24202735.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:
characteristic equation
distribution network
fault location
fault ranking index
global search
Opis:
This paper proposes a new fault location method in radial medium voltage distribution networks. The proposed method only uses the measurement data at the feeder beginning to approximate the characteristic equation showing the dependence between the positive-sequence voltage and phase angle at the monitoring point with the distance to the fault location for each fault type on each line segment. To determine these characteristic equation coefficients, the entire distribution network will be modeled and simulated by four types of faults at different locations along the lines to build the initial database. Based on this database, the mathematical functions in MATLAB software are applied to approximate these coefficients corresponding to each type of fault for each line segment in the network. Then, from the current and voltage measurement data at the feeder beginning, the algorithms of global search, comparison, and fault ranking are used to find out where the fault occurs on the distribution network. Two types of distribution network with and without branches are studied and simulated in this paper to verify and evaluate the effectiveness of the proposed method.
Źródło:
Archives of Electrical Engineering; 2023, 72, 2; 503--520
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A High-Accuracy of Transmission Line Faults (TLFs) Classification Based on Convolutional Neural Network
Autorzy:
Fuada, S.
Shiddieqy, H. A.
Adiono, T.
Powiązania:
https://bibliotekanauki.pl/articles/1844462.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fault detection
fault classification
transmission lines
convolutional neural network
machine learning
Opis:
To improve power system reliability, a protection mechanism is highly needed. Early detection can be used to prevent failures in the power transmission line (TL). A classification system method is widely used to protect against false detection as well as assist the decision analysis. Each TL signal has a continuous pattern in which it can be detected and classified by the conventional methods, i.e., wavelet feature extraction and artificial neural network (ANN). However, the accuracy resulting from these mentioned models is relatively low. To overcome this issue, we propose a machine learning-based on Convolutional Neural Network (CNN) for the transmission line faults (TLFs) application. CNN is more suitable for pattern recognition compared to conventional ANN and ANN with Discrete Wavelet Transform (DWT) feature extraction. In this work, we first simulate our proposed model by using Simulink® and Matlab®. This simulation generates a fault signal dataset, which is divided into 45.738 data training and 4.752 data tests. Later, we design the number of machine learning classifiers. Each model classifier is trained by exposing it to the same dataset. The CNN design, with raw input, is determined as an optimal output model from the training process with 100% accuracy.
Źródło:
International Journal of Electronics and Telecommunications; 2020, 66, 4; 655-664
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
System of Signal Injection and Extractioo for Protection and Insulation Monitoring in Medium Voltage Networks
Autorzy:
Kyrychenko, M.
Habrych, M.
Powiązania:
https://bibliotekanauki.pl/articles/410612.pdf
Data publikacji:
2017
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
insulation
distribution network
ground fault
signal injection
Opis:
Simple overcurrent criterion is most often used for detection and elimination of ground faults in radial industrial medium voltage networks. Since in Poland medium voltage networks work with noneffectively grounded neutral point, the ground fault currents can reach very low values, especially under high resistance faults. Such faults cannot be detected by any protection. Therefore, new methods to detect ground faults and to control the insulation in medium voltage network are of great importance. In the paper the idea for monitoring insulation parameters of the system, based on the simultaneous use of two different signals of non-industrial frequency, injected into the controlled network, is presented and discussed.
Źródło:
Present Problems of Power System Control; 2017, 8; 23-30
2084-2201
Pojawia się w:
Present Problems of Power System Control
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Metody lokalizacji zwarć łukowych w energetycznych liniach przesyłowych
Arcing faults location methods for power transmission lines logic
Autorzy:
Pustułka, M.
Łukowicz, M.
Iżykowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/267653.pdf
Data publikacji:
2013
Wydawca:
Politechnika Gdańska. Wydział Elektrotechniki i Automatyki
Tematy:
lokalizacja zwarć
sztuczna sieć neuronowa
fault location
artificial neural network
arcing fault
Opis:
W artykule przedstawiono trzy metody lokalizacji zwarć: algorytm Takagi, algorytm wykorzystujący pomiary z dwóch końców linii oraz algorytm z siecią neuronową. Do lokalizacji zwarcia w algorytmach wykorzystano napięcia i prądy mierzone z obu końców linii. Sieć neuronowa wspomagana była rozwiązaniem uzyskanym za pomocą algorytmu, który w celu określenia miejsca zwarcia oprócz naturalnych sygnałów pętli zwarciowych wykorzystywał również sygnały składowych symetrycznych.
This paper presents a three different fault location approaches: one-end Takagi algorithm, two-end algorithm considering natural fault loops and neural network. It is assumed that three-phase voltages and currents from both ends of the line measured asynchronously are the input signals of the fault locator. In addition to natural fault loop signals also the use of symmetrical components (positive and negative or incremental positive sequence components) for fault location were considered. Results of evaluation study have been included, analyzed and discussed. Impact of filtration has been considered as well.
Źródło:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej; 2013, 32; 103-106
1425-5766
2353-1290
Pojawia się w:
Zeszyty Naukowe Wydziału Elektrotechniki i Automatyki Politechniki Gdańskiej
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Fault diagnosis of analog circuit based on wavelet transform and neural network
Autorzy:
Wang, Hui
Powiązania:
https://bibliotekanauki.pl/articles/141368.pdf
Data publikacji:
2020
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
analog circuit
fault diagnosis
neural network
wavelet transform
Opis:
Analog circuits need more effective fault diagnosis methods. In this study, the fault diagnosis method of analog circuits was studied. The fault feature vectors were extracted by a wavelet transform and then classified by a generalized regression neural network (GRNN). In order to improve the classification performance, a wolf pack algorithm (WPA) was used to optimize the GRNN, and a WPA-GRNN diagnosis algorithm was obtained. Then a simulation experiment was carried out taking a Sallen–Key bandpass filter as an example. It was found from the experimental results that the WPA could achieve the preset accuracy in the eighth iteration and had a good optimization effect. In the comparison between the GRNN, genetic algorithm (GA)-GRNN and WPA-GRNN, the WPA-GRNN had the highest diagnostic accuracy, and moreover it had high accuracy in diagnosing a single fault than multiple faults, short training time, smaller error, and an average accuracy rate of 91%. The experimental results prove the effectiveness of the WPA-GRNN in fault diagnosis of analog circuits, which can make some contributions to the further development of the fault diagnosis of analog circuits.
Źródło:
Archives of Electrical Engineering; 2020, 69, 1; 175-185
1427-4221
2300-2506
Pojawia się w:
Archives of Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Arcing Faults Location Methods for Power Transmission Lines
Metody lokalizacji zwarć łukowych w energetycznych liniach przesyłowych
Autorzy:
Pustułka, M.
Łukowicz, M.
Iżykowski, J.
Powiązania:
https://bibliotekanauki.pl/articles/396832.pdf
Data publikacji:
2014
Wydawca:
ENERGA
Tematy:
fault location
artificial neural network
arcing fault
lokalizacja zwarć
sztuczna sieć neuronowa
zwarcia łukowe
Opis:
This paper presents three different fault location approaches: one-end Takagi algorithm, two-end algorithm considering natural fault loops and neural network. It is assumed that three-phase voltages and currents from both ends of the line measured asynchronously are the input signals of the fault locator. In addition to natural fault loop signals also the use of symmetrical components (positive and negative or incremental positive sequence components) for fault location were considered. Results of the evaluation study have been included, analyzed and discussed. The impact of filtration has also been considered.
W artykule przedstawiono trzy metody lokalizacji zwarć: algorytm Takagi, algorytm wykorzystujący pomiary z dwóch końców linii oraz algorytm z siecią neuronową. Do lokalizacji zwarcia w algorytmach użyto napięcia i prądów mierzonych z obu końców linii. Sieć neuronowa wspomagana była rozwiązaniem uzyskanym za pomocą algorytmu, który w celu określenia miejsca zwarcia, oprócz naturalnych sygnałów pętli zwarciowych, wykorzystywał również sygnały składowych symetrycznych. Przeanalizowany został wpływ filtracji sygnałów zasilających na dokładność algorytmów.
Źródło:
Acta Energetica; 2014, 1; 142-151
2300-3022
Pojawia się w:
Acta Energetica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Journal Bearing Fault Detection Based on Daubechies Wavelet
Autorzy:
Narendiranath, B. T.
Himamshu, H. S.
Prabin, K. N.
Rama, P. D.
Nishant, C.
Powiązania:
https://bibliotekanauki.pl/articles/176955.pdf
Data publikacji:
2017
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
journal bearing
fault diagnosis
Debauchies wavelet
artificial neural network
Opis:
Journal bearings are widely used to support the shafts in industrial machinery involving heavy loads, such as compressors, turbines and centrifugal pumps. The major problem that could arise in journal bearings is catastrophic failure due to corrosion or erosion and fatigue, which results in economic loss and creates major safety risks. Thus, it is necessary to provide suitable condition monitoring technique to detect and diagnose failures, and achieve cost savings to the industry. Therefore, this paper focuses on fault diagnosis on journal bearing using Debauchies Wavelet-02 (DB-02). Nowadays, wavelet transformation is one of the most popular technique of the time-frequency-transformations. An experimental setup was used to diagnose the faults in the journal bearing. The accelerometer is used to collect vibration data, from the journal bearing in the form of time domain. This was then used as input for a MATLAB code that could plot the time domain signal. This signal was then decomposed based on the wavelet transform. The fast Fourier transform is then used to obtain the frequency domain, which gives us the frequency having the highest amplitude. To diagnose the faults various operating conditions are used in the journal bearing such as Full oil, half loose, half oil, fault 1, fault 2, fault 3 and full loose. Then the Artificial Neural Networks (ANN) is used to classify faults. The network is trained based on data already collected and then it is tested based on random data points. ANN was able to classify the faults with the classification rate of 85.7%. Thus, the test process for unseen vibration data of the trained ANN combined with ideal output target values indicates high success rate for automated bearing fault detection.
Źródło:
Archives of Acoustics; 2017, 42, 3; 401-414
0137-5075
Pojawia się w:
Archives of Acoustics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Phenomena leading to asymmetry of phase–to–earth voltages in MV networks
Autorzy:
Lorenc, J.
Staszak, B.
Powiązania:
https://bibliotekanauki.pl/articles/97461.pdf
Data publikacji:
2016
Wydawca:
Politechnika Poznańska. Wydawnictwo Politechniki Poznańskiej
Tematy:
earth faults
MV network
voltage asymmetry
earth fault protection
Opis:
When analysing the earth–fault phenomena, the longitudinal impedance of individual elements is neglected, and the capacitances and conductivities of individual phases to ground are only taken into account. Such a procedure is well–founded for resonant earthed networks and the faults accompanied by a significant resistance at the disturbance’s location (called high–resistance faults). However, in such a case, the possibility of occurrence of voltage differences between the network neutral point and the earth should be considered. The voltage is the effect of natural asymmetry in the network’s phase admittances or in the supply voltages. Level of such a voltage asymmetry significantly affects the flawless of the earth–fault protections and the accurate tuning of Petersen coils in the earth–fault compensation process. In the paper, the results of research work on how different phenomena affect the level of the network phase voltage asymmetry are presented. The investigations have been carried out on the PSCAD software–based MV network model, and investigations take into account the relations between asymmetries in the phase capacitance and conductance of the network as well as the load asymmetry degree in the individual power lines.
Źródło:
Computer Applications in Electrical Engineering; 2016, 14; 158-167
1508-4248
Pojawia się w:
Computer Applications in Electrical Engineering
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Deep learning-based fault diagnosis for marine centrifugal fan
Autorzy:
Li, Congyue
Hu, Yihuai
Jiang, Jiawei
Yan, Guohua
Powiązania:
https://bibliotekanauki.pl/articles/32917700.pdf
Data publikacji:
2023
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
CEEMDAN
fault diagnosis
lightweight neural network
marine centrifugal fan
Opis:
Marine centrifugal fans usually work in harsh environments. Their vibration signals are non-linear. The traditional fault diagnosis methods of fans require much calculation and have low operating efficiency. Only shallow fault features can be extracted. As a result, the diagnosis accuracy is not high. It is difficult to realize the end-to-end fault diagnosis. Combining the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and lightweight neural network, a fault classification method is proposed. First, the CEEMDAN can decompose the vibration signal into several intrinsic modal functions (IMF). Then, the original signals can be transformed into 2-D images through pseudocolour coding of the IMFs. Finally, they are fed into the lightweight neural network for fault diagnosis. By embedding a convolutional block attention module (CBAM), the ability of the network to extract critical feature information is improved. The results show that the proposed method can adaptively extract the fault characteristics of a marine centrifugal fan. While the model is lightweight, the overall diagnostic accuracy can reach 99.3%. As exploratory basic research, this method can provide a reference for intelligent fault diagnosis systems on ships.
Źródło:
Polish Maritime Research; 2023, 1; 112-120
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A fault location strategy based on information fusion and CODAS algorithm under epistemic uncertainty
Autorzy:
Duan, Rongxing
Chen, Li
He, Jiejun
Huang, Shujuan
Powiązania:
https://bibliotekanauki.pl/articles/2172032.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne PAN
Tematy:
fault tree
expert evaluation
evidence network
information fusion
CODAS algorithm
Opis:
Application of new technology in modern systems not only substantially improves the performance, but also presents a severe challenge to fault location of these systems. This paper presents a new fault location strategy for maintenance personnel to recover them based on information fusion and improved CODAS algorithm. Firstly, a fault tree is adopted to develop the failure model of a complex system, and failure probability of components is determined by expert evaluations to handle the uncertainty problem. Moreover, a fault tree is converted into an evidence network to obtain importance degrees, which are used to construct a diagnostic decision table together with the risk priority number. Additionally, these results are updated to optimize the maintenance process using sensor information. A novel dynamic location strategy is designed based on interval CODAS algorithm and optimal fault location strategy can be obtained. Finally, a real system is analyzed to demonstrate the feasibility of the proposed maintenance strategy.
Źródło:
Eksploatacja i Niezawodność; 2022, 24, 3; 478--488
1507-2711
Pojawia się w:
Eksploatacja i Niezawodność
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Performance analysis of multi-layered clustering network using fault tolerance multipath routing protocol (MRP-FT) in a wireless sensor network (WSN)
Autorzy:
Kaur, Gagandeep
Powiązania:
https://bibliotekanauki.pl/articles/2204100.pdf
Data publikacji:
2023
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
scalability
fault tolerance
neural networks
Boltzmann learning
wireless sensor network
Opis:
Wireless sensor networks (WSNs) are ad hoc and self-configuring networks having the possibility that any sensor node can connect or leave the network. With no central controller in WSN, wireless sensor nodes are considered responsible for data routing in the networks. The wireless sensor nodes are very small in size and have limited resources, therefore, it becomes difficult to recharge or replace the battery of the sensor nodes at far places. The present study focused on reducing the battery consumption of the sensor nodes by the deployment of the newly proposed Fault Tolerance Multipath Routing Protocol (MRP-FT) as compared with the existing Low Energy Adaptive Clustering Hierarchy (LEACH) protocol under particle swarm optimisation based fault tolerant routing (PSO-FT) technique. The proposed algorithm of MRP-FT-based on the dynamic clustering technique using Boltzmann learning of the neural network and the weights were adjusted according to the area of networks, number of nodes and rounds, the initial energy of nodes (E0), transmission energy of nodes (d
Źródło:
Operations Research and Decisions; 2023, 33, 1; 75--92
2081-8858
2391-6060
Pojawia się w:
Operations Research and Decisions
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Research on big data attribute selection method in submarine optical fiber network fault diagnosis database
Autorzy:
Chen, G.
Powiązania:
https://bibliotekanauki.pl/articles/259788.pdf
Data publikacji:
2017
Wydawca:
Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
Tematy:
submarine optical fiber network
fault diagnosis database
big data attribute selection
Opis:
At present, in the fault diagnosis database of submarine optical fiber network, the attribute selection of large data is completed by detecting the attributes of the data, the accuracy of large data attribute selection cannot be guaranteed. In this paper, a large data attribute selection method based on support vector machines (SVM) for fault diagnosis database of submarine optical fiber network is proposed. Mining large data in the database of optical fiber network fault diagnosis, and calculate its attribute weight, attribute classification is completed according to attribute weight, so as to complete attribute selection of large data. Experimental results prove that ,the proposed method can improve the accuracy of large data attribute selection in fault diagnosis database of submarine optical fiber network, and has high use value.
Źródło:
Polish Maritime Research; 2017, S 3; 121-127
1233-2585
Pojawia się w:
Polish Maritime Research
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Soft Fault Clustering in Analog Electronic Circuits with the Use of Self Organizing Neural Network
Autorzy:
Grzechca, D.
Powiązania:
https://bibliotekanauki.pl/articles/220571.pdf
Data publikacji:
2011
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
fault detection
parametric faults
analogue electronic circuits
self-organizing neural network
Opis:
The paper presents a methodology for parametric fault clustering in analog electronic circuits with the use of a self-organizing artificial neural network. The method proposed here allows fast and efficient circuit diagnosis on the basis of time and/or frequency response which may lead to higher production yield. A self-organizing map (SOM) has been applied in order to cluster all circuit states into possible separate groups. So, it works as a feature selector and classifier. SOM can be fed by raw data (data comes from the time or frequency response) or some pre-processing is done at first. The author proposes conversion of a circuit response with the use of e.g. gradient and differentiation. The main goal of the SOM is to distribute all single faults on a two-dimensional map without state overlapping. The method is aimed for the development stage because the tolerances of elements are not taken into account, however single but parametric faults are considered. Efficiency analyses of fault clustering have been made on several examples e.g. a Sallen-Key BPF and an ECG amplifier. Testing procedure is performed in time and frequency domains for the Sallen-Key BPF with limited number of test points i.e. it is assumed that only input and output pins are available. A similar procedure has been applied to a real ECG amplifier in the frequency domain. Results prove a high efficiency in acceptable time which makes the method very convenient (easy and quick) as a first test in the development stage.
Źródło:
Metrology and Measurement Systems; 2011, 18, 4; 555-568
0860-8229
Pojawia się w:
Metrology and Measurement Systems
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning methods for diagnosing the causes of die-casting defects
Autorzy:
Okuniewska, Alicja
Perzyk, Marcin
Kozłowski, Jacek
Powiązania:
https://bibliotekanauki.pl/articles/29519775.pdf
Data publikacji:
2023
Wydawca:
Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:
fault diagnosis
machine learning tools
neural network
classification trees
support vector machine
Opis:
The research was focused on analyzing the causes of high-pressure die-casting defects, more specifically on casting leakage, which is considered perhaps the most important and common defect. The real data used for modelling was obtained from a high-pressure die-casting foundry that manufactures aluminum cylinder blocks for the world’s leading automotive brands. This paper compares and summarizes the results of applying advanced modelling using artificial neural networks, regression trees, and support vector machines methods to select artificial neural networks as the most effective method to perform a multidimensional optimization of process parameters to diagnose the causes of die-casting defects and to indicate the future research scope in this area. The developed system enables the prediction of the level of defects in castings with satisfactory accuracy and is therefore a highly relevant reference for process engineers of high-pressure foundries. This article indicates exactly which process parameters significantly influence the formation of a defect in a casting.
Źródło:
Computer Methods in Materials Science; 2023, 23, 2; 45-56
2720-4081
2720-3948
Pojawia się w:
Computer Methods in Materials Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An artificial neural networks approach to stator current sensor faults detection for DTC-SVM structure
Autorzy:
Klimkowski, K.
Powiązania:
https://bibliotekanauki.pl/articles/1193212.pdf
Data publikacji:
2016
Wydawca:
Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Tematy:
DTC-SVM
current sensor
induction motor
detector
fault tolerant drive
neural network
Opis:
In the paper, an analysis is made of the stator current sensor fault detector based on artificial neural network for vector controlled induction motor drive system. The systems with different learning algorithms and structures are analyzed and tested in different drive conditions. Simulation results are obtained in direct torque control algorithm (DTC-SVM) and performed in MATLAB/SimPowerSystem software.
Źródło:
Power Electronics and Drives; 2016, 1, 36/1; 127-138
2451-0262
2543-4292
Pojawia się w:
Power Electronics and Drives
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Integrated fault-tolerant control of a quadcopter UAV with incipient actuator faults
Autorzy:
Kantue, Paulin
Pedro, Jimoh O.
Powiązania:
https://bibliotekanauki.pl/articles/2172129.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
fault tolerant control
quadrocopter
incipient actuator fault
radial basis function
neural network
sterowanie tolerujące uszkodzenia
kwadrokopter
radialna funkcja bazowa
sieć neuronowa
Opis:
An integrated approach to the fault-tolerant control (FTC) of a quadcopter unmanned aerial vehicle (UAV) with incipient actuator faults is presented. The framework is comprised of a radial basis function neural network (RBFNN) fault detection and diagnosis (FDD) module and a reconfigurable flight controller (RFC) based on the extremum seeking control approach. The dynamics of a quadcopter subject to incipient actuator faults are estimated using a nonlinear identification method comprising a continuous forward algorithm (CFA) and a modified golden section search (GSS) one. A time-difference-of-arrival (TDOA) method and the post-fault system estimates are used within the FDD module to compute the fault location and fault magnitude. The impact of bi-directional uncertainty and FDD detection time on the overall FTC performance and system recovery is assessed by simulating a quadcopter UAV during a trajectory tracking mission and is found to be robust against incipient actuator faults during straight and level flight and tight turns.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 601--617
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An Expert System Coupled With a Hierarchical Structure of Fuzzy Neural Networks for Fault Diagnosis
Autorzy:
Calado, J. M. F.
Costa, I. S.
Powiązania:
https://bibliotekanauki.pl/articles/908283.pdf
Data publikacji:
1999
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
rozpoznanie błędu
wykrywanie błędu
system ekspertowy
sieć neuronowa rozmyta
fault diagnosis
fault detection
fault isolation
shallow knowledge
deep knowledge
expert system
fuzzy neural network
abrupt faults
incipient faults
Opis:
An on-line fault diagnosis system, designed to be robust to the normal transient behaviour of the process, is described. The overall system consists of an expert system cascade with a hierarchical structure of fuzzy neural networks, corresponding to a multi-stage fault detection and isolation system. The fault detection is performed through the expert system by means of fault detection heuristic rules, generated from deep and shallow knowledge of the process under consideration. If a fault is detected, the hierarchical structure of fuzzy neural networks starts and it performs the fault isolation task. The structure of this diagnosis system was designed to allow for the diagnosis of single and multiple simultaneous abrupt and incipient faults from only single abrupt fault symptoms. Also, it combines the advantages of both fuzzy reasoning and neural networks learning capacity. A continuous binary distillation column has been used as a test bed of the current approach. Single, double and triple simultaneous abrupt faults, as well as incipient faults, have been considered. The preliminary results obtained show a good accuracy, even in the case of multiple faults.
Źródło:
International Journal of Applied Mathematics and Computer Science; 1999, 9, 3; 667-687
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Moc opisowa drzew niezdatności z zależnościami czasowymi
Expressive power of probabilistic fault trees with time dependencies
Autorzy:
Magott, J.
Powiązania:
https://bibliotekanauki.pl/articles/257687.pdf
Data publikacji:
2009
Wydawca:
Sieć Badawcza Łukasiewicz - Instytut Technologii Eksploatacji - Państwowy Instytut Badawczy
Tematy:
sieć PERT
dynamiczne drzewa niezdatności
DDN
PDNZC
PERT network
dynamic fault tree
DFT
PFTTD
Opis:
W pracy przeanalizowano moc opisową probabilistycznych drzew niezdatności z zależnościami czasowymi (PDNZC) w wyrażaniu sieci PERT i dynamicznych drzew niezdatności (DDN). PDNZC składają się z bramek, zdarzeń oraz połączeń bramek ze zdarzeniami. Bramki podzielone są na dwie zasadnicze kategorie, a mianowicie: uogólniające i przyczynowe. W pracy stosowane są tylko bramki przyczynowe. Przedstawiono te bramki przyczynowe, które są używane w ilustracji mocy opisowej PDNZC. Pokazano jak za pomocą PDNZC można wyrazić sieci PERT. Badając moc opisową PDNZC w wyrażaniu DDN, skoncentrowano się na reprezentacji następujących bramek dynamicznych: priorytetowej AND, komponentów rezerwowych, funkcjonalnej zależności.
Descriptive Power of Probabilistic Fault Trees with Time Dependencies (PFTTD) in expressing PERT networks and Dynamic Fault Trees (DFT) are analysed in the paper. PFTTD are combined from events, gates, and connections between them. The gates are divided into two categories, namely, causal and general. A causal gate is characterised by delay times between causes (input events) and effect (output event). The output event of the generalisation gate is a combination of input events. In the paper, only causal gates are used. How to model PERT networks by PFTTD is shown. Th paper illustrates how PFTTD can model the following gates of DFT: priority AND, spare with cold stand-by, and functional dependency. In future research, the decision power of PFTTD will be studied. Algorithms for finding quantitative characteristics of PFTTD will be based on achievements of PERT networks and reliability theory.
Źródło:
Problemy Eksploatacji; 2009, 4; 33-40
1232-9312
Pojawia się w:
Problemy Eksploatacji
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Grid Fault Diagnosis Based on Information Entropy and Multi-source Information Fusion
Autorzy:
Zeng, Xin
Xiong, Xingzhong
Luo, Zhongqiang
Powiązania:
https://bibliotekanauki.pl/articles/1844639.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
information entropy
Bayesian network
multi-source information fusion
D-S evidence theory
fault diagnosis
Opis:
In order to solve the problem of misjudgment caused by the traditional power grid fault diagnosis methods, a new fusion diagnosis method is proposed based on the theory of multi-source information fusion. In this method, the fault degree of the power element is deduced by using the Bayesian network. Then, the time-domain singular spectrum entropy, frequency-domain power spectrum entropy and wavelet packet energy spectrum entropy of the electrical signals of each circuit after the failure are extracted, and these three characteristic quantities are taken as the fault support degree of the power components. Finally, the four fault degrees are normalized and classified as four evidence bodies in the D-S evidence theory for multi-feature fusion, which reduces the uncertainty brought by a single feature body. Simulation results show that the proposed method can obtain more reliable diagnosis results compared with the traditional methods.
Źródło:
International Journal of Electronics and Telecommunications; 2021, 67, 2; 143-148
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
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