Wyniki wyszukiwania - Biblioteka Nauki

1

Real-time simulation and experimental implementation of luenberger observer-based speed sensor fault detection of bldc motors

100%

Al-Mutayeb Y. , Almobaied M. , Ouda M.

Acta Mechanica et Automatica

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2024

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tom Vol. 18, no. 1

144--157

EN

Modern control systems’ dependability, safety and efficiency have all been improved by studying fault-tolerant control systems (FTCS). FTCS techniques can typically be active or passive controls. The fault detection and diagnosis (FDD) method is used in this study’s active control branch to identify probable faults that could develop in the speed Hall sensors of brushless DC motors (BLDC). FDD methodologies can be categorised into two types, depending on the available data and the process involved: model-based methods and data-based methods. The proposed approach in this study explores the implementation of the Luenberger observer methodology as part of the model-based approach. The chosen methodology was practically implemented and subjected to experimental evaluation. The proposed observer relies on the residual signal, which displays the difference between the plant’s observed and estimated speed signals and serves as a failure alert for the entire system. Given the increasing demand for BLDC motors in various industrial control applications, including medical fields, automation and robotics, this particular motor was selected as a benchmark to thoroughly evaluate and validate the proposed method. The primary contribution of this paper lies in the real-time application of model-based sensor fault detection methods to BLDC motors. The efficiency of the suggested method is showcased through extensive MATLAB simulations, where the obtained results confirm the successful detection of faults with a high level of responsiveness. As a result, the project was successfully implemented in real-time, and the experimental results exhibited a close correlation with the simulated outcomes. This consistency between simulation and practical implementation validates the accuracy and reliability of the proposed methodology for detecting faults in the BLDC motor speed sensor. The results underscore the heightened reliability and safety attained by promptly and accurately detecting sensor faults during the operation of the motor.

2

The construction of high-order b-spline wavelets and their decomposition relations for fault detection and localisation in composite beams

75%

Katunin A.

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2011

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tom Vol. 46, nr 3

43-59

EN

B-spline scaling functions and wavelets have found wide applicability in many scientific and practical problems thanks to their unique properties. They show considerably better results in comparison to other wavelets, and they are used as well in mathematical approximations, signal processing, image compression, etc. But only the first four wavelets from this family were mathematically formulated. In this work, the author formulates the quartic, quintic and sextic B-spline wavelets and their decomposition relations in explicit form. This allows for the improvement of the sensitivity of fault detection and localisation in composite beams using discrete wavelet transform with decomposition.

PL

B-splajnowe funkcje skalujące i falki znajdują szerokie zastosowanie w wielu zagadnieniach naukowych i praktycznych dzięki ich wyjątkowym właściwościom. Pokazują one znacznie lepsze wyniki w porównaniu z innymi falkami i są z powodzeniem stosowane w matematycznych aproksymacjach, przetwarzaniu sygnałów, kompresji obrazów itd. Ale tylko pierwsze cztery falki z tej rodziny zostały sformułowane matematycznie. W niniejszej pracy autor sformułował falki B-splajnowe wyższych rzędów i ich zależności dekompozycji w postaci jawnej. Pozwalają one na zwiększenie dokładności przy detekcji i lokalizacji uszkodzeń w belkach kompozytowych z zastosowaniem dyskretnej transformacji falkowej z dekompozycją.

3

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

63%

Alili B. , Hafaifa A. , Iratni A.

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tom 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.

4

Failure identification and isolation of DC-DC boost converter using a sliding mode controller and adaptive threshold

63%

Khater F. , Aibeche A. , Fellag S. A. , Doghmane M. Z. , Akroum H.

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2024

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tom Vol. 25, No. 3

art. no. 2024303

EN

DC-DC converters have become essential components in various industrial applications, including aerospace, electric vehicles, and renewable energy systems. However, ensuring enhanced reliability remains a critical challenge for these converters. Fault diagnosis and reliability analysis are crucial for preventing damage and minimizing maintenance costs. This study focuses on investigating the operational behavior of DC-DC boost converters under normal and faulty conditions, precisely targeting open-circuit and short-circuit faults in converter switches. To achieve this, an adaptive threshold approach is introduced for effective fault detection. The adaptive threshold value is calculated based on measured voltage and current signals, along with their corresponding reference signals from the primary control system. The research is structured into two parts: the first part addresses sliding mode control aspects, ensuring regulated output voltages, output currents, and capacitor voltage for sustained converter operation. The second part investigates fault diagnosis, analyzing the impact of defective DC-DC converters on the overall electrical system functionality. The proposed algorithm's performance is evaluated and validated through simulations in MATLAB/Simulink environment. Furthermore, based on the results’ comparison, the proposed approach of the sliding mode controller and adaptive threshold contributes to enhancing the reliability of DC-DC converters and enables effective fault detection and isolation.

5

Faults detection in gas turbine using hybrid adaptive network based fuzzy inference systems

63%

Hadroug N. , Hafaifa A. , Kouzou A. , Chaibet A.

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tom Vol. 17, No. 4

3--17

EN

The main aim of the present paper is the implementation of a fault detection strategy to ensure the fault detection in a gas turbine which is presenting a complex system. This strategy is based on an adaptive hybrid neuro fuzzy inference technique which combines the advantages of both techniques of neuron networks and fuzzy logic, where, the objective is to maintain the desired performance of the studied gas turbine system in the presence of faults. On the other side, the representation of fuzzy knowledge in the learning neural networks has to be accurate to provide significant improvements for modeling of the studied system dynamic behavior. The results presented in this paper proves clearly that the proposed detection technique allows the perfect detection of the studied gas turbine malfunctions, furthermore it shows that the use of the proposed technique based on the Adaptive Neuro-Fuzzy Interference System (ANFIS) approach which uses the adaptive learning mechanism of neuron networks and fuzzy inference techniques, can be a promising technique to be applied in several industrial application for faults detection.

6

Porównanie cząstkowych modeli parametrycznych w zadaniu detekcji uszkodzeń sieci gazowej

51%

Syfert M. , Jankowska A. , Łabęda-Grudziak Z. , Tabor Ł.

Pomiary Automatyka Kontrola

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2012

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tom R. 58, nr 1

3-8

PL

Zreferowano badania detekcji uszkodzeń gazociągu z użyciem cząstkowych modeli parametrycznych. Stosując trzy metody modelowania: addytywne modele regresyjne (najnowszą z badanych technik), sztuczne sieci neuronowe oraz układy rozmyte typu TSK opracowano aproksymacje ciśnień w węzłach sieci. Modele testowano w zadaniu detekcji wycieku oraz uszkodzenia czujnika pomiarowego. Wszystkie modele zapewniały dużą dokładność aproksymacji ciśnienia w poprawnych stanach pracy, wykazując także bardzo skuteczną detekcję uszkodzeń czujników pomiarowych ciśnień, natomiast w sytuacji symulowanych wycieków ich przydatność w detekcji była znacznie mniejsza.

EN

The results of faults detection [1, 2, 3, 4, 5] in a gas system network (Fig. 1) with use of parametric partial models [6, 7, 8] are presented in the paper. This is a new approach to the task with use of exploratory data analysis [10, 11, 17] and partial models. Three techniques were used to build models of pressure in network nodes: additive regression (ADD - new method of modelling [10, 11, 12, 13, 14, 15]), artificial neural networks (ANN) [16, 17, 18] and TSK fuzzy logic modelling [8, 16, 17]. The measured pressures in adjacent nodes as well cumulative flows in the main line (from global analytical model [9]) of gasoline were the inputs of the models. For the analysed stations (in parts A and B marked in Fig. 1) a set of test failures in the form of leaks and damage of pressure sensors is given in Tab. 1.Using trial and error method, by evaluating the effectiveness of fault detection, there were obtained structures of models of different complexity for individual modelling techniques: ADD - presented by equations (1) and (2), ANN- (3) and (4), TSK- (5) and (6). The model order is not greater than 2. The exemplary results of leak detection with use of particular models are shown in Figs. 3, 5, 7 and of sensor fault detection in Figs. 4, 6, 8. In the conclusions there is summarised the relative accuracy of models (in Table 2), the relative normalized values of the studied residues of leaks - Tab.3 and the pressure sensor failures - Tab. 4. All models provided highly precise pressure approximation in non-fault states, but TSK and ADD models turned out to be the more accurate. Additionally, all of them were effective in case of pressure sensor fault detection, however, in case of simulated leakages their usefulness was much lower.