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1

Fault diagnosis and fault tolerant control of asynchronous motor speed sensor combined with different speed estimators

Li Qiubo

Diagnostyka

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2025

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Vol. 26, No. 2

art. no. 2025202

EN

Accurate speed and flux estimation are important conditions for achieving high-performance and low-cost control. Therefore, this study first constructs a mathematical model and space vector pulse width modulation control method for asynchronous motors. Then, a simple speed estimator, an extended Kalman filter speed estimator, and a full-order magnetic flux observer speed estimator are established in the speed estimation module. Finally, based on the Euler voting algorithm, a fault diagnosis and fault-tolerant control method for speed sensors is designed. The results showed that under low-speed conditions, the average mean square errors of the speed estimators of the simple, extended Kalman filter, and full-order magnetic flux observer were 0.7969, 0.9134, and 2.2526, respectively. The first two speed estimators had better performance, while under medium to high-speed conditions, the latter two speed estimators had a lower average mean square error and better performance. When various faults occurred, the research method could quickly determine the best performing speed estimator for feedback and effectively display the speed fluctuations caused by the faults. Finally, it smoothly switched to the speed sensorless mode and controlled the speed error within -5r/min-5r/min.

2

Fault diagnosis analysis of gas turbine engine based on fuzzy algorithm

Peng Yana, Hai Wenjiong, Xue Peng

Diagnostyka

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2025

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Vol. 26, No. 1

art. no. 2025109

EN

To solve the problems of low efficiency and difficult feature extraction in traditional fault diagnosis methods, this study proposes an optimized Fuzzy C-Means clustering algorithm for diagnosing and analyzing gas turbine engine faults. This algorithm mainly introduces subtraction clustering, penalty factors, and data weights on the basis of the original fuzzy C-means clustering algorithm, thereby improving the generalization ability of the algorithm model and the credibility of the results. The optimized fuzzy C-means clustering algorithm had the highest level of accuracy value, with a value of 95.67%, which was 11.79% higher than the average accuracy of other algorithms. Meanwhile the optimized Fuzzy C-Means clustering algorithm improved the accuracy values of KNN, BP, SVM, and fuzzy C-means clustering algorithms by 19.65%, 12.26%, 3.55%, and 11.70%. The training set accuracy of the optimized fuzzy C-means clustering algorithm under four engine states was at the highest level, with an average improvement of 15.5%, 25%, 24%, and 16% in accuracy. The optimized fuzzy C-means clustering algorithm achieved an accuracy of 90.39% in the test set, with an average improvement of 16.13% in accuracy. The membership classification results indicated that the optimized fuzzy C-means clustering algorithm had a membership degree of 1.

3

Application of vibration signal analysis method in the fault diagnosis of mechanical gearboxes

Liu Jincui, Wang Tieying

Diagnostyka

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2025

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Vol. 26, No. 1

art. no. 2025107

EN

The traditional mechanical gearbox diagnosis method is not enough to meet the operation requirements in terms of reliability and safety, so the purpose of this study is to collect and analyze the vibration signal of mechanical gearbox, and explore the application of vibration signal analysis method in fault diagnosis. A vibration sensor is installed in a gear box to collect real-time vibration data, and the data are denoised, filtered and aligned. The experimental results show that the waveform of the normal gear has obvious periodicity, while the high-frequency component of the faulty gear increases significantly. The diagnostic accuracy of fault types is wear, pitting, point wear, fracture wear and broken teeth. The envelope spectrum amplitude of normal bearings is maintained between 0.4-0.5. The innovation of the research lies in the development of a fault signal acquisition system based on advanced vibration sensing technology, which realizes real-time monitoring of gearbox status and accurate collection of vibration data, and optimizes the ability to extract fault characteristics from complex background noise by combining a variety of vibration signal processing technologies.

4

Identification and classification of turn short-circuit and demagnetization failures in PMSM using LSTM and GRU methods

Lale Timur, Yüksek Gökhan

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2025

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Vol. 73, nr 1

art. no. e151958

EN

In an extremely broad range of industrial applications, especially in electric vehicles, permanent magnet synchronous motors (PMSMs) play a vital role. Any failure in PMSMs may cause possible safety hazards, a drop in productivity, and expensive downtime. Therefore, their reliable operation is essential. Accurate failure identification and classification allow for addressing problems before they escalate, which helps ensure the seamless operation of PMSMs and reduces the likelihood of equipment failure. Therefore, in this paper, novel failure identification methods based on gated recurrent unit (GRU) and long short-term memory (LSTM) from recurrent neural network (RNN) methods are proposed for early identification of stator inter-turn short circuit failure (ISCF) and demagnetization failure (DF) occurring in PMSMs under multiple operating conditions. The proposed methods use three-phase current signals recorded from the experimental study under multiple operating conditions of the motor as input data. In the proposed methods, both feature extraction and classification are executed within a unified framework. The experimental outcomes obtained demonstrate that the proposed methods can identify a total of six unique motor conditions, including three ISCF variations and two DF variations, with high accuracy. The LSTM and GRU approaches predicted the identification of failures with 98.23% and 98.72% accuracy, respectively. Compared to existing methods, the success of the proposed approaches is satisfactory. In addition, LSTM and GRU-based failure identification methods are also compared in detail for accuracy, precision, sensitivity, specificity, and training time in this study.

5

Power transformer fault warning combining support vector machine and improved grey wolf optimization algorithm

Zhao Shuzong, Pattanadech Norasage

Archives of Electrical Engineering

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2025

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Vol. 74, nr 1

191--208

EN

To optimize the parameter setting of the support vector machine and improve the classification performance and computational efficiency of power transformer fault diagnosis, this study proposes an improved grey wolf optimization algorithm. By optimizing the global search and local optimization capabilities of the grey wolf algorithm and combining them with stacked denoising autoencoders, a new power transformer fault warning model is constructed. Firstly, the grey wolf optimization algorithm is optimized through four strategies: elite reverse learning, nonlinear control parameters, Lévy flight, and particle swarm optimization, which improve its global search and local optimization capabilities. Secondly, the stacked denoising autoencoder is utilized to extract high-level features of fault data, and the improved GWO algorithm and SVM are combined to complete fault classification. The results indicated that the proposed diagnostic model achieved a diagnostic accuracy of 0.979, a recall rate of 0.986, and an F1 value of 0.983 in benchmark performance testing. In practical applications, the average fault diagnosis accuracy of this model could reach up to 99.21%, and the average diagnosis time was only 0.08 s. The developed power transformer fault warning model can provide an efficient and reliable technical solution for fault diagnosis in the power system.

6

Amodel for processing and identifying engine vibration signals

Nguyen Phuong X., Tran Linh H.

Przegląd Elektrotechniczny

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2024

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R. 100, nr 5

263--266

EN

The advancement of the sensor technology becoming increasingly cost-effective and the progress in diagnostic and management research, users nowadays not only demand high reliability from their devices but also the ability for their equipment to self-diagnose errors and provide alerts. These devices often incorporate sensor systems capable of generating plenty of data points, that needed a carefully targeted algorithms for extracting features from the data for classification and prediction models. In this paper, we will develop a comprehensive model for identifying vibration signals. We will extract features from the bearing data provided by Case Western Reserve University Bearing Data Center, then use a deep-learning based convolutional neural network to learn to be a classification model of the motor states based on the vibration signals. The numerical results show that the method can offer the promising accuracy at 85.8%.

PL

Wraz z postępem w technologii czujników, która staje się coraz bardziej tańsza do użycia w badaniach diagnostycznych, użytkownicy wymagają obecnie nie tylko wysokiej niezawodności swoich urządzeń, ale także zdolności ich sprzętu do samodiagnostyki błędów i generowania alertów. Nowej generacje urządzeń zawierają systemy czujników zdolne do generowania mnóstwo danych, co wymagało starannie dobranych algorytmów do wyodrębniania cech charakterystycznych na potrzeby modeli klasyfikacji i predykcji. W tym artykule przedstawimy model do identyfikacji sygnałów drganiowych. Korzystaliśmy z danych pomiarowych łożysk dostępnych w Centrum baz danych łożysk Uniwersytetu Case Western Reserve. Z tych danych pomiarowych, wygenerowano ich spectrogramy do postaci obrazów a następnie wykorzystano splotową sieć neuronową opartą na głębokim uczeniu się do tworzenia model klasyfikacji stanów silnika w oparciu o sygnały wibracyjne. Wyniki liczbowe pokazują, że metoda ta może zapewnić obiecującą dokładność na poziomie 85,8%.

7

Condition Monitoring and Fault Diagnosis of Permanent Magnet Synchronous Motor Stator Winding Using the Continuous Wavelet Transform and Machine Learning

Pietrzak Przemysław, Wolkiewicz Marcin

Power Electronics and Drives

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2024

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Vol. 9 (44)

106--121

EN

Applying the condition monitoring technology to industrial processes can help detect faults in time, minimise their impact and reduce the cost of unplanned downtime. Since the introduction of the Industry 4.0 paradigm, many companies have been investing in the development of such technology for drive systems. Permanent magnet synchronous motors (PMSMs) have recently been used in many industries. Therefore, the issues of condition monitoring of PMSM drives are important. This study proposes and compares diagnostic schemes based on the stator phase currents (SPCSCs) signal for condition monitoring and fault diagnosis of PMSM stator winding faults. The continuous wavelet transform (CWT) is used for the extraction of the symptoms of interturn short circuits in PMSM stator winding. Machine learning algorithms are applied to automate the detection and classification of the faults. The concept for an original and intelligent PMSM stator winding condition monitoring system is proposed.

8

Fault diagnosis of marine diesel engine based on multi-scale time domain decomposition and convolutional neural network

Li Congyue, Cui Dexin

Polish Maritime Research

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2024

|

nr 3

85--93

EN

Marine diesel engines work in an environment with multiple excitation sources. Effective feature extraction and fault diagnosis of diesel engine vibration signals have become a hot research topic. Time-domain synchronous averaging (TSA) can effectively handle vibration signals. However, the key phase signal required for TSA is difficult to obtain. During signal processing, it can result in the loss of information on fault features. In addition, frequency multiplication signal waveforms are mixed. To address this problem, a multi-scale time-domain averaging decomposition (MTAD) method is proposed and combined with signal-to-image conversion and a convolutional neural network (CNN), to perform fault diagnosis on a marine diesel engine. Firstly, the vibration signals are decomposed by MTAD. The MTAD method does not require the acquisition of the key phase signal and can effectively overcome signal aliasing. Secondly, the decomposed signal components are converted into 2-D images by signal-to-image conversion. Finally, the 2-D images are input into the CNN for adaptive feature extraction and fault diagnosis. Through experiments, it is verified that the proposed method has certain noise immunity and superiority in marine diesel engine fault diagnosis.

9

Development of a guaranteed minimum detectable sensor fault diagnosis scheme

Witczak Marcin, Pazera Marcin, Majdzik Paweł, Matysiak Ryszard

International Journal of Applied Mathematics and Computer Science

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2024

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Vol. 34, no. 3

409--423

EN

The paper deals with the estimation of sensor faults for dynamic systems as well as the assessment of the uncertainty of the resulting estimates. For that purpose, it is assumed that the external disturbances are bounded within an ellipsoidal domain. This allows considering both stochastic and deterministic process and measurement uncertainties. Under such an assumption, a fault diagnosis scheme is developed with a prescribed convergence rate and accuracy. To achieve fault estimation, a conversion into an equivalent descriptor system is utilized. The paper provides a full stability and convergence analysis of the estimator including observability analysis. As a result, a set of complementary fault uncertainty intervals is obtained, which are minimized in such a way as to obtain a minimum detectable sensor fault. The final part of the paper exhibits a numerical example concerning fault estimation of a multi-tank system. The obtained results clearly confirm the performance of the proposed estimator expressed in the minimum detectable fault intervals.

10

Approximate and analytic flow models for leak detection and identification

Tatara Marek S., Kowalczuk Zdzisław

International Journal of Applied Mathematics and Computer Science

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2024

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Vol. 34, no. 3

391--407

EN

The article presents a comprehensive quantitative comparison of four analytical models that, in different ways, describe the flow process in transmission pipelines necessary in the task of detecting and isolating leaks. First, the analyzed models are briefly presented. Then, a novel model comparison framework is introduced along with a methodology for generating data and assessing diagnostic effectiveness. The study presents basic assumptions, experimental conditions and scenarios considered. Finally, the quality of the model-based diagnostic estimators is assessed, focusing on their bias, standard deviation, and computational complexity. Here, several optimality criteria are used as detailed indicators of the quality and performance of the estimators in a multi-criteria Pareto optimality assessment.

11

Wasserstein Distance-EEMD Enhanced Multi-Head Graph Attention Network for Rolling Bearing Fault Diagnosis Under Different Working Conditions

Wang Xingbing, Yao Yunfeng, Gao Chen

Eksploatacja i Niezawodność

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2024

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Vol. 26, no. 2

art. no. 184037

EN

Traditional fault diagnosis models often overlook the interconnections between segments of vibration data, resulting in the loss of critical feature information. Additionally, the vibration signals of rolling bearings exhibit non-linear behaviors during operation. Therefore, an efficient fault diagnosis model tailored for rolling bearings is proposed in this paper. In the proposed model, the 1D vibration signals are first preprocessed using ensemble empirical mode decomposition (EEMD). This technique generates multiple intrinsic mode functions (IMF) as individual nodes. The percentage distance between each node is calculated using the Wasserstein distance (WD) to capture the relationships between nodes and use it as the edge weights to construct a node graph. This unique approach enhances the transformation of 1D vibration signals into a node graph representation, preserving important information. An improved multi-head graph attention network (MGAT) model is established to extract features and perform classification on the node graph. This MGAT model effectively utilizes the relationships between nodes and enhances the accuracy of fault diagnosis. The experimental results demonstrate that the proposed method achieves higher accuracy compared to similar modelswhile requiring less processing time. The proposed approach contributes significantly to the field of fault diagnosis for rolling bearings and provides a valuable tool for practical applications.

12

Reconstruction-based stacked sparse auto-encoder for nonlinear industrial process fault diagnosis

Weng Qihang, Ren Shaojun, Zhu Baoyu, Jin Yinfeng, Si Fengqi

Eksploatacja i Niezawodność

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2024

|

Vol. 26, no. 1

art. no. 175873

EN

The reconstruction-based (RB) approach can effectively suppress the misdiagnosis problem due to the smearing effect in fault isolation. However, the current exploration of the RB approach for large-scale nonlinear systems is still limited. Therefore, this paper proposes a reliable and effective fault diagnosis method based on a reconstruction-based stacked sparse autoencoder (RBSSAE) for high-dimensional industrial systems. In RBSSAE, a reconstruction-based index achieved by the Steffensen iterative method is developed to check whether the given variable(s) are responsible for the faults efficiently. However, the number of possible faulty variable combinations grows exponentially with the system dimensionor actual abnormal variables, causing an unbearable computational burden for variable combination optimization. Hence, the proposed RBSSAE utilizes a sequential floating forward selection approach to rapidly isolate the most decisive combination of fault variables, meeting a requirement of online fault diagnosis. Finally, the effectiveness of the RBSSAE is verified on a numerical example and a real industrial case. Comparisons with other state-of-the-art methods are also presented.

13

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

|

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.

14

Conditional generative adversarial network based data augmentation for fault diagnosis of diesel engines applied with infrared thermography and deep convolutional neural network

Wang Rongcai, Jia Xisheng, Liu Zichang, Dong Enzhi, Li Siyu, Cheng Zhonghua

Eksploatacja i Niezawodność

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2024

|

Vol. 26, no. 1

art. no. 175291

EN

This paper tries to introduce a new intelligent method for the early fault diagnosis of diesel engines. Firstly, infrared thermography (IRT) is introduced into diesel engine condition monitoring, then infrared images of diesel engines in four health states, such as normal condition, single-cylinder misfire, multi-cylinder misfire and air filter blockage, are collected and the region of interest (ROI) of infrared images are extracted. Next, conditional generative adversarial network (CGAN) is deployed to perform data augmentation on infrared image datasets. Then, deep convolutional neural network (DCNN) and Softmax regression (SR) classifier are used for automatically extracting infrared image fault features and pattern recognition, respectively. Finally, a comparison with three deep learning (DL) models is performed. The validation results show that the data augmentation method proposed in the paper can significantly improve the early fault diagnosis accuracy, and DCNN has the best fault diagnosis effect andresistance to temperature fluctuation interference among the four DL models.

15

Fault tolerant control and fault diagnosis methods integrated using intelligent controller for hybrid dynamic systems

Achbi Mohammed Said, Rouabah Boubakeur, Mahboub Mohamed Abdelbasset, Benarabi Bilal, Kafi Mohamed Redouane, Kechida Sihem

Diagnostyka

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2024

|

Vol. 25, No. 3

art. no. 2024312

EN

This research aims to provide a fault diagnosis approach for Hybrid Dynamic (SDHs), Systems and FaultTolerant Control synthesis, while also ensuring the smooth operation of industrial settings. This study is a part of the larger topic of Hybrid Dynamic System control and fault diagnosis. The primary focus is on modelling strategies designed expressly for Hybrid Dynamic Systems, with a concentration on combining continuous and event-driven components. Much work is devoted to developing a model that can incorporate both kinds of elements. A system model that can track several modes without explicit identification can be created by utilizing Neuro-Fuzzy Networks, providing a thorough overview. On the basis of this synthesized model, an AI-based fault diagnosis method is subsequently developed.

16

Fault diagnosis of computer numerical control machine tools table feed system based on digital twin and machine learning

Dong Ying, Li Yanhua

Diagnostyka

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2024

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Vol. 25, No. 4

art. no. 2024414

EN

The mechanical operations performed by the computer numerical control machine tools feed system are more prone to failure, and its not conducive to the accuracy and stability of computer numerical control machine tools. As a result, this study proposes a fault diagnosis model that combines a digital twin with a multiscale parallel one-dimensional convolutional neural network. A digital twin model of the table feed system was first constructed and simulation experiments of various working conditions were conducted to obtain the missing fault data in the actual physical space. On this basis, the study utilizes the acquired signals to train the proposed migration model for diagnosis. The model extracts different types of fault features from the analog and real signals, respectively, through an intermediate multi-scale convolution algorithm. In addition, the model reduces the distributional disparities between the real and analog signals by using the Wasserstein distance as a regular term to impose constraints on the machine learning process. The study conducted simulation experiments, and the results indicated that the fault periods of the simulated and actual signals of bearing outer ring faults were 0.198s and 0.196s, respectively, with a relative error of only 1.02%. The average fault periods of the actual and simulated signals of the bearing inner ring faults were 0.199s and 0.197s, respectively, with a relative deviation of only 0.48%. In addition, the classification accuracy of the proposed model can be maintained above 95%. Thus, the proposed model has good practical value.

17

Application of continuous wavelet transform and convolutional neural networks in fault diagnosis of PMSM stator windings

Pietrzak Przemysław, Wolkiewicz Marcin

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2024

|

Vol. 72, nr 5

art. no. e150202

EN

Efficiency, reliability, and durability play a key role in modern drive systems in line with the Industry 4.0 paradigm and the sustainability trend. To ensure this, highly efficient motors and appropriate systems must be deployed to monitor their condition and diagnose faults during the operation. For these reasons, in recent years, research has been increasingly focused on developing new methods for fault diagnosis of permanent magnet synchronous motors (PMSMs). This paper proposes a novel hybrid method for the automatic detection and classification of PMSM stator winding faults based on combining the continuous wavelet transform (CWT) analysis of the negative sequence component of the stator phase currents with a convolutional neural network (CNN). CWT scalogram images are used as the inputs of the CNN-based interturn short circuits fault classifier model. Experimental tests were conducted to verify the effectiveness of the proposed approach under various motor operating conditions and at an incipient stage of fault propagation. In addition, the effects of the input image format, CNN structure, and training process parameters on model accuracy and classification effectiveness were investigated. The results of the experimental tests confirmed the high effectiveness of fault detection (99.4%) and classification (97.5%), as well as other important advantages of the developed method.

18

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

|

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.

19

State Estimation MRAS and Identification of Stator Winding Phase Fault Detection of the PMSG in Wind Energy Based on the Sliding Mode Control

Bouslimani Samir, Meradi Samir, Drid Said, Chrifi-Alaoui Larbi, Bezziane Ali

Power Electronics and Drives

|

2023

|

Vol. 8 (43)

109-127

EN

This paper proposes a method for the diagnosis of stator inter-turn short-circuit fault for permanent magnet synchronous generators (PMSG). Inter-turn short-circuit currents are among the most critical in PMSG. For safety considerations, a fast detection is required when a fault occurs. This approach uses the parameter estimation of the per-phase stator resistance in closed-loop control of variable speed of wind energy conversion system (WECS). In the presence of an incipient short-circuit fault, the estimation of the resistance of the stator in the d-q reference frame does not make it possible to give the exact information. To solve this problem, a novel fault diagnosis scheme is proposed using parameter estimation of the per-phase stator resistance. The per-phase stator resistance of PMSG is estimated using the MRAS algorithm technique in real time. Based on a faulty PMSG model expressed in Park’s reference frame, the number of short-circuited turns is estimated using MRAS. Fault diagnosis is on line detected by analysing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the information on the estimated parameters can be used to improve the control performance. The simulation results demonstrate that the proposed method can estimate the faulty phase.

20

Hierarchical multiscale fluctuation dispersion entropy for fuel injection system fault diagnosis

Shi Qingguo, Hu Yihuai, Yan Guohua

Polish Maritime Research

|

2023

|

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.