Wyniki wyszukiwania - BazTech

1

Fault Diagnosis of Suspension System Based on Spectrogram Image and Vision Transformer

Arun Balaji, Venkatesh Naveen S, Sugumaran V

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 1

art. no. 174860

EN

The suspension system in an automobile is essential for comfort and control. Implementing a monitoring system is crucial to ensure proper function, prevent accidents, maintain performance, and reduce both downtime and costs. Traditionally, diagnosing faults in suspension systems has relied on specialized setups and vibration analysis. The conventional approach typically involves either wavelet analysis or a machine learning approach. While these methods are effective, they often demand specialized expertise and time consumable. Alternatively, using deep learning for suspension system fault diagnosis enables faster and more precise real-time fault detection. This study explores the use of vision transformers as an innovative approach to fault diagnosis in suspension systems, utilizing spectrogram images. The process involves extracting spectrogram images from vibration signals, which serve as inputs for the vision transformer model. The test results demonstrate that the proposed fault diagnosis system achieves an impressive accuracy rate of 98.12% in identifying faults.

2

Discrete wavelet transform and energy distribution for effective bearing fault detection and analysis

Beldjaatit Chahinez, Sebbagh Toufik, Guentri Hocine

Przegląd Elektrotechniczny

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2023

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R. 99, nr 7

24--28

EN

Condition monitoring and problem diagnostics have drawn more attention recently in the industrial sector. One of the most crucial parts of rotating machinery are rolling-element bearings. Bearing faults are a common cause of machinery failures. To identify failing bearings early, vibration condition monitoring of rotating machinery has emerged as the preferred technique. Several signal analysis techniques can extract useful information from vibration data. The non-stationary analysis signals that are typically associated with machine defects cannot be handled by frequency-based approaches. Non-stationary signals are analyzed effectively by applying time-frequency techniques. The use of wavelet transform has increased in bearing monitoring research for the last 20 years to obtain correlated time-frequency information. This paper presents a discrete wavelet transform (DWT) and energy distribution-based bearing defect diagnostic technique. The "db3" wavelet form of DWT is used to decompose vibration signals under both normal and faulty (inner race-fault and outer race-fault) bearing conditions at various frequency ranges. Due to the default, the energy distribution for every decomposition level is calculated to detect which frequency band contains the harmonics. The results obtained from healthy and defective bearings are compared. The wavelet coefficient with the highest value of the energy distribution is employed in the Fourier analysis to pinpoint the site of the fault. The monitoring results demonstrate that the suggested approach is effective in finding and analyzing faults.

PL

Monitorowanie stanu i diagnostyka problemów przyciągnęły ostatnio więcej uwagi w sektorze przemysłowym. Jedną z najbardziej kluczowych części maszyn wirujących są łożyska toczne. Usterki łożysk są częstą przyczyną awarii maszyn. W celu wczesnej identyfikacji uszkodzonych łożysk, monitorowanie stanu wibracji maszyn wirujących stało się preferowaną techniką. Kilka technik analizy sygnału może wydobyć użyteczne informacje z danych o drganiach. Niestacjonarne sygnały analizy, które są zwykle związane z uszkodzeniami maszyn, nie mogą być obsługiwane przez podejścia oparte na częstotliwości. Sygnały niestacjonarne są skutecznie analizowane poprzez zastosowanie technik czasowoczęstotliwościowych. Zastosowanie transformaty falkowej wzrosło w badaniach nad monitorowaniem łożysk przez ostatnie 20 lat w celu uzyskania skorelowanej informacji czasowo-częstotliwościowej. W niniejszej pracy przedstawiono dyskretną transformatę falkową (DWT) oraz technikę diagnostyczną opartą na rozkładzie energii. Forma falkowa "db3" DWT jest używana do dekomponowania sygnałów drganiowych w warunkach łożyska zarówno normalnego, jak i wadliwego (wewnętrznego i zewnętrznego) w różnych zakresach częstotliwości. Ze względu na domyślność, rozkład energii dla każdego poziomu dekompozycji jest obliczany w celu wykrycia, które pasmo częstotliwości zawiera harmoniczne. Wyniki uzyskane z łożysk zdrowych i uszkodzonych są porównywane. Współczynnik falkowy o największej wartości rozkładu energii jest wykorzystywany w analizie Fouriera w celu określenia miejsca uszkodzenia. Wyniki monitorowania pokazują, że proponowane podejście jest skuteczne w wyszukiwaniu i analizie uszkodzeń.

3

Hierarchical multiscale fluctuation dispersion entropy for fuel injection system fault diagnosis

Shi Qingguo, Hu Yihuai, Yan Guohua

Polish Maritime Research

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2023

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nr 1

98--111

EN

Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite different and important. Fault diagnosis in the fuel injection system is crucial to ensure the power, economy and emission of ME diesel engines, so we introduce hierarchical multiscale fluctuation dispersion entropy (HMFDE) and a support matrix machine (SMM) to realise it. We also discuss the influence of parameter changes on the entropy calculation’s accuracy and efficiency. The system simulation model is established and verified by Amesim software, and then HMFDE is used to extract a matrix from the features of a high pressure signal in a common rail pipe, under four working conditions. Compared with vectorised HMFDE, the accuracy of fault diagnosis using SMM is nearly 3% higher than that using a support vector machine (SVM). Experiments also show that the proposed method is more accurate and stable when compared with hierarchical multiscale dispersion entropy (HMDE), hierarchical dispersion entropy (HDE), multiscale fluctuation dispersion entropy (MFDE), multiscale dispersion entropy (MDE) and multiscale sample entropy (MSE). Therefore, the proposed method is more suitable for the modelling data. This research provides a new direction for matrix learning applications in fault diagnosis in marine two-stroke diesel engines.

4

Fault diagnosis of me marine diesel engine fuel injector with novel IRCMDE method

Shi Qingguo, Hu Yihuai, Yan Guohua

Polish Maritime Research

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2023

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nr 3

96--110

EN

As an important component of the fuel injection system, the fuel injector is crucial for ensuring the power, economy, and emissions for a whole ME (machine electronically-controlled) marine diesel engine. However, injectors are most prone to failures such as reduced pressure at the opening valve, clogged spray holes and worn needle valves, because of the harsh working conditions. The failure characteristics are non-stationary and non-linear. Therefore, to efficiently extract fault features, an improved refined composite multi-scale dispersion entropy (IRCMDE) is proposed, which uses the energy distribution of sampling points as weights for coarse-grained calculation, then fast correlation-based filter (FCBF) and support vector machine (SVM) are used for feature selection and fault classification, respectively. The experimental results from a MAN B&W 6S35ME-B9 marine diesel engine show that the proposed algorithm can achieve 92.12% fault accuracy for injector faults, which is higher than multiscale dispersion entropy (MDE), refined composite multiscale dispersion entropy (RCMDE) and multiscale permutation entropy (MPE). Moreover, the experiment has also proved that, due to the double-walled structure of the high-pressure fuel pipe, the fuel injection pressure signal is more accurate than the vibration signal in reflecting the injector operating conditions.

5

Fault diagnosis of bearings based on SSWT, bayes optimisation and CNN

Yan Guohua, Hu Yihuai, Shi Qingguo

Polish Maritime Research

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2023

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nr 3

132--141

EN

Bearings are important components of rotating machinery and transmission systems, and are often damaged by wear, overload and shocks. Due to the low resolution of traditional time-frequency analysis for the diagnosis of bearing faults, a synchrosqueezed wavelet transform (SSWT) is proposed to improve the resolution. An improved convolutional neural network fault diagnosis model is proposed in this paper, and a Bayesian optimisation method is applied to automatically adjust the structure and hyperparameters of the model to improve the accuracy of bearing fault diagnosis. Experimental results from the accelerated life testing of bearings show that the proposed method is able to accurately identify various types of bearing fault and the different status of these faults under complex running conditions, while achieving very good generalisation ability.

6

Deep learning-based fault diagnosis for marine centrifugal fan

Li Congyue, Hu Yihuai, Jiang Jiawei, Yan Guohua

Polish Maritime Research

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2023

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nr 1

112--120

EN

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.

7

Fault diagnosis of induction motors: an architecture for real-time assessment as a cyber-physical system

Pal Ranjan Sasti Charan, Dewangan Nagesh, Mohanty Amiya Ranjan

Engineering Transactions

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2023

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Vol. 71, iss. 1

23--42

EN

Induction motors (IMs) have a crucial and significant role in various industrial sectors. With the prolonged operation of IMs, faults tend to develop that can be classified into five major categories, i.e., broken rotor bars, stator winding faults, air-gap eccentricity, bearing faults, and load torque fluctuations. If the faults go undetected, it may lead to catastrophic failure. Hence, the predictive-based condition monitoring technique has evolved as a real-time fault diagnosis that exploits the revolutionary idea of cyber-physical system (CPS). Furthermore, motor current signature analysis (MCSA) is a non-invasive fault diagnosis technique of a motor that can be used to investigate the presence of five fault types. However, the major constraint that industries face today is the on-field implementation of MCSA-based fault diagnosis involving CPS-based architecture, executed in an automated manner. Hence, the present article depicts algorithms that aim at real-time monitoring of IMs through a CPS framework. The proposed methodology is automated, does not involve any human intervention, and has been validated with real-time experiments, depicting its effectiveness and practicality.

8

Unraveling Induction Motor State through Thermal Imaging and Edge Processing : A Step towards Explainable Fault Diagnosis

Piechocki Mateusz, Pajchrowski Tomasz, Kraft Marek, Wolkiewicz Marcin, Ewert Paweł

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 3

art. no. 170114

EN

Equipment condition monitoring is essential to maintain the reliability of the electromechanical systems. Recently topics related to fault diagnosis have attracted significant interest, rapidly evolving this research area. This study presents a non-invasive method for online state classification of a squirrel-cage induction motor. The solution utilizes thermal imaging for non-contact analysis of thermal changes in machinery. Moreover, used convolutional neural networks (CNNs) streamline extracting relevant features from data and malfunction distinction without defining strict rules. A wide range of neural networks was evaluated to explore the possibilities of the proposed approach and their outputs were verified using model interpretability methods. Besides, the top-performing architectures were optimized and deployed on resource-constrained hardware to examine the system's performance in operating conditions. Overall, the completed tests have confirmed that the proposed approach is feasible, provides accurate results, and successfully operates even when deployed on edge devices.

9

Research on Fault Diagnosis of Highway Bi-LSTM Based on Attention Mechanism

Li Xueyi, Su Kaiyu, He Qiushi, Wang Xiangkai, Xie Zhijie

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 2

art. no. 162937

EN

Deep groove ball bearings are widely used in rotary machinery. Accurate for bearing faults diagnosis is essential for equipment maintenance. For common depth learning methods, the feature extraction of inverse time domain signal direction and the attention to key features are usually ignored. Based on the long short term memory(LSTM) network, this study proposes an attention-based highway bidirectional long short term memory (AHBi-LSTM) network for fault diagnosis based on the raw vibration signal. By increasing the Attention mechanism and Highway, the ability of the network to extract features is increased. The bidirectional LSTM network simultaneously extracts the raw vibration signal in positive and inverse time-domains to better extract the fault features. Six deep groove ball bearings with different health conditions were used to validate the AHBi-LSTM method in an experiment. The results showed that the accuracy of the proposed method for bearing fault diagnosis was over 98%, which was 8.66% higher than that of the LSTM model. The AHBi-LSTM model is also better than other relevant models for bearing fault diagnosis.

10

Fault diagnosis of machines operating in variable conditions using artificial neural network not requiring training data from a faulty machine

Pawlik Paweł, Kania Konrad, Przysucha Bartosz

Eksploatacja i Niezawodność

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2023

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Vol. 25, no. 3

art. no. 168109

EN

The fault diagnosis for maintenance of machines operating in variable conditions requires special dedicated methods. Variable load or temperature conditions affect the vibration signal values. The article presents a new approach to diagnosing rotating machines using an artificial neural network, the training of which does not require data from the damaged machine. This is a new approach not previously found in the literature. Until now, neural networks have been used for machine diagnosis in the form of classifiers, where data from individual faults were required. A new diagnostic parameter rDPNS (Relative Differences Product of Network Statistics) as a function of the machine's shaft order was proposed as a kind of new order spectrum independent of the machine's operating conditions. The presented work analyses the use of the proposed method to diagnose misalignment and unbalance. The results of an experiment carried out in the laboratory demonstrated the effectiveness of the proposed method.

11

Fault diagnosis of high-speed rotating machines using MATLAB

Joshi Mahesh B., Pujari Kamlesh S.

Diagnostyka

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2023

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Vol. 24, No. 2

art. no. 2023208

EN

Industrial high-speed rotating machines entail constant and consistent monitoring to prevent downtime, affecting quantity and quality. Complex machines need advanced intelligent fault diagnosis showing minimal errors. This work offers a MATLAB-based fault diagnosis for sugar industry machines. The vibration behavior of physical industrial machines is obtained, and the signals are provided to a MATLAB program to identify the fault. The information helps to suggest remedies to include in the maintenance schedule. The ease and comprehensible nature of the method reduce time and enhance the reliability of condition monitoring for industrial machines.

12

Diagnosis of ITSC fault in the electrical vehicle powertrain system through signal processing analysis

Ouamara Dehbia, Boukhnifer Moussa, Chaibet Ahmed, Maidi Ahmed

Diagnostyka

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2023

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Vol. 24, No. 1

art. no. 2023113

EN

The three-phase induction motor is well suited for a wide range of mobile drives, specifically for electric vehicle powertrain. During the entire life cycle of the electric motor, some types of failures can occur, with stator winding failure being the most common. The impact of this failure must be considered from the incipient as it can affect the performance of the motor, especially for electrically powered vehicle application. In this paper, the intern turn short circuit of the stator winding was studied using Fast Fourier transform (FFT) and Shor-Time Fourier transform (STFT) approaches. The residuals current between the estimated currents provided by the extended Kalman filter (EKF) and the actual ones are used for fault diagnosis and identification. Through FFT, the residual spectrum is sensitive to faults and gives the extraction of inter-turn short circuit (ITSC) related frequencies in the phase winding. In addition, the FFT is used to obtain information about when and where the ITSC appears in the phase winding. Indeed, the results allow to know the faulty phase, to estimate the fault rate and the fault occurrence frequency as well as their appearance time.

13

Machine learning methods for diagnosing the causes of die-casting defects

Okuniewska Alicja, Perzyk Marcin, Kozłowski Jacek

Computer Methods in Materials Science

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2023

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Vol. 23, No. 2

45--56

EN

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.

14

Study of an open-switch fault detection algorithm for a three-phase interleaved DC–DC boost converter in a photovoltaic system

Boudjellal Bilal, Benslimane Tarak

Archives of Electrical Engineering

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2023

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Vol. 72, nr 3

661--676

EN

This paper presents a novel fault detection algorithm for a three-phase interleaved DC–DC boost converter integrated in a photovoltaic system. Interleaved DC–DC converters have been used widely due to their advantages in terms of efficiency, ripple reductions, modularity and small filter components. The fault detection algorithm depends on the input current waveform as a fault indicator and does not require any additional sensors in the system. To guarantee service continuity, a fault tolerant topology is achieved by connecting a redundant switch to the interleaved converter. The proposed fault detection algorithm is validated under different scenarios by the obtained results.

15

Stator Winding Fault Detection of Permanent Magnet Synchronous Motors Based on the Short-Time Fourier Transform

Pietrzak Przemysław, Wolkiewicz Marcin

Power Electronics and Drives

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2022

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Vol. 7 (42)

112--133

EN

In modern drive systems, the high-efficient permanent magnet synchronous motors (PMSMs) have become one of the most substantial components. Nevertheless, such machines are exposed to various types of faults. Hence, on-line condition monitoring and fault diagnosis of PMSMs have become necessary. One of the most common PMSM faults is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that allow fault detection at its early stage. The article presents the results of experimental studies obtained from fast Fourier transform (FFT) and short-time Fourier transform (STFT) analyses of the stator phase current, stator phase current envelope and stator phase current space vector module. The superiority of the proposed method over the classical approach based on the stator current analysis using FFT is highlighted. The proposed solution is experimentally verified under various motor operating conditions. The application of STFT analysis discussed so far in the literature has been limited to the fault diagnosis of induction motors and the narrow range of the analysed motor operating conditions. Moreover, there are no works in the field of motor diagnostics dealing with STFT analysis for stator windings based on the stator current envelope and the stator current space vector module.

16

A novel fault diagnosis method for marine blower with vibration signals

Yan Guohua, Hu Yihuai, Jiang Jiawei

Polish Maritime Research

|

2022

|

nr 2

77--86

EN

The vibration signals on marine blowers are non-linear and non-stationary. In addition, the equipment in marine engine room is numerous and affects each other, which makes it difficult to extract fault features of vibration signals in the time domain. This paper proposes a fault diagnosis method based on the combination of Ensemble Empirical Mode Decomposition (EEMD), an Autoregressive model (AR model) and the correlation coefficient method. Firstly, a series of Intrinsic Mode Function (IMF) components were obtained after the vibration signal was decomposed by EEMD. Secondly, effective IMF components were selected by the correlation coefficient method. AR models were established and the power spectrum was analysed. It was verified that blower failure can be accurately diagnosed. In addition, an intelligent diagnosis method was proposed based on the combination of EEMD energy and a Back Propagation Neural Network (BPNN), with a correlation coefficient method to get effective IMF components, and the energy components were calculated, normalised as a feature vector. Finally, the feature vector was sent to the BPNN for training and state recognition. The results indicated that the EEMD-BPNN intelligent fault diagnosis method is suitable for higly accurate fault diagnosis of marine blowers.

17

A convolutional neural network-based method of inverter fault diagnosis in a ship’s DC electrical system

Yan Guohua, Hu Yihuai, Shi Qingguo

Polish Maritime Research

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2022

|

nr 4

105--114

EN

Multi-energy hybrid ships are compatible with multiple forms of new energy, and have become one of the most important directions for future developments in this field. A propulsion inverter is an important component of a hybrid DC electrical system, and its reliability has great significance in terms of safe navigation of the ship. A fault diagnosis method based on one-dimensional convolutional neural network (CNN) is proposed that considers the mutual influence between an inverter fault and a limited ship power grid. A tiled voltage reduction method is used for one-to-one correspondence between the inverter output voltage and switching combinations, followed by a combination of a global average pooling layer and a fully connected layer to reduce the model overfitting problem. Finally, fault diagnosis is verified by a Softmax layer with good anti-interference performance and accuracy.

18

An application of the Pythagorean fuzzy sets in the fault diagnosis

Nguyen Hoang

Journal of KONBiN

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2022

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Vol. 52, iss. 4

63--74

EN

In this paper, a comprehensive review and critical analyses of methods based on the ordinary fuzzy set, Atanassov’s intuitionistic fuzzy set, and its extensions have been conducted to show their limitations and defects. Then, a novel similarity measure based on the generalized score function has been introduced that incorporates the significance (importance) of information, making it more intuitive to compare them. The proposed method is employed for the fault diagnosis of steam turbine generator unit under Pythagorean fuzzy environment. Ten fault types of rotating machines are established as failure patterns in nine different vibration frequency ranges, expressed in terms of Pythagorean fuzzy numbers. The superiority of the proposed method in dealing with uncertain and vague information is shown by comparing it with some existing measures in numerical examples.

PL

W artykule dokonano kompleksowego przeglądu i analiz krytycznych metod opartych na klasycznym zbiorze rozmytym lub intuicjonistycznym zbiorze rozmytym Atanassova i ich rozszerzeniach w celu wykazania ich ograniczeń i wad. Następnie wprowadzono na podstawie miary wiedzy, nową miarę podobieństwa, która uwzględnia znaczenie (ważność) informacji, czyniąc je bardziej intuicyjnymi przy ich porównywaniu. Zaproponowaną metodę weryfikuje się w przypadku diagnozowania uszkodzeń zespołu turbogeneratora w rozmytym środowisku. Dziesięć typów uszkodzeń turbogeneratora jest określanych jako wzorce uszkodzeń wyrażonych za pomocą liczb rozmytych Pitagorasa opisujących ich symptomy w dziewięciu różnych zakresach częstotliwości drgań. Poprzez porównanie z niektórymi istniejącymi miarami w kilku przykładach liczbowych pokazano przewagę proponowanej metody w opisaniu niedokładnych i niepewnych informacji.

19

Fault-tolerant tracking control for a non-linear twin-rotor system under ellipsoidal bounding

Kukurowski Norbert, Mrugalski Marcin, Pazera Marcin, Witczak Marcin

International Journal of Applied Mathematics and Computer Science

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2022

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Vol. 32, no. 2

171--183

EN

A novel fault-tolerant tracking control scheme based on an adaptive robust observer for non-linear systems is proposed. Additionally, it is presumed that the non-linear system may be faulty, i.e., affected by actuator and sensor faults along with the disturbances, simultaneously. Accordingly, the stability of the robust observer as well as the fault-tolerant tracking controller is achieved by using the ℋ∞ approach. Furthermore, unknown actuator and sensor faults and states are bounded by the uncertainty intervals for estimation quality assessment as well as reliable fault diagnosis. This means that narrow intervals accompany better estimation quality. Thus, to cope with the above difficulty, it is assumed that the disturbances are over-bounded by an ellipsoid. Consequently, the performance and correctness of the proposed fault-tolerant tracking control scheme are verified by using a non-linear twin-rotor aerodynamical laboratory system.

20

Rolling Bearing Fault Diagnosis Method Based on Improved Variational Mode Decomposition and Information Entropy

Ge Liang, Fan Wen, Xiao Xiaoting, Gan Fangji, Lai Xin, Deng Hongxia, Huang Qi

Engineering Transactions

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2022

|

Vol. 70, iss. 1

23--51

EN

Due to the complex randomness and nonlinearity of rolling bearing vibration signal, it is challenging to extract fault features effectively. By analyzing the vibration mechanism of rolling bearing, it is found that the vibration signal of local damage defects of rolling bearing has the characteristics of periodic impact and amplitude modulation. The variational mode decomposition (VMD) algorithm has a good advantage in dealing with nonlinear and nonstationary signals and decomposing a signal into different modes. However, VMD has the problem of parameter selection, which directly affects the performance of VMD processing, and causes mode aliasing. Therefore, a rolling bearing fault diagnosis method based on improved VMD is proposed. A new fitness function combining differential evolution (DE) algorithm with gray wolf optimization (GWO) algorithm is proposed to form a new hybrid optimization algorithm, named DEGWO. The simulation results show that the improved VMD method based on DEGWO can adaptively remove the noise according to the characteristics of the signal and restore the original characteristics of the vibration signal. Finally, in order to verify the advantages of the research, the information entropy is extracted from the data of 1000 samples in the bearing database of Case Western Reserve University as the feature set, which is input into support vector machine (SVM) for fault diagnosis test. The results show that the diagnostic accuracy of this method is 96.5%, which effectively improved the accuracy of rolling bearing fault diagnosis.