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Relevant bibliographies by topics / Fault decomposition

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Academic literature on the topic 'Fault decomposition'

Author: Grafiati

Published: 1 June 2024

Last updated: 19 July 2025

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Journal articles on the topic "Fault decomposition"

1

Gupta, Abhishek, and Ramesh Kumar Pachar. "A Hybrid Signal Processing Technique for Identification and Categorization of Faults in IEEE-9 Bus System." Advanced Engineering Forum 49 (May 31, 2023): 43–55. http://dx.doi.org/10.4028/p-jkw3p9.

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A hybrid signal processing technique (HSPT) is proposed in this manuscript for identification and categorization of faults in electrical transmission network. A fault indicator (FI) is suggested by decomposition of the currents by application of Alienation coefficient (ACF), Stockwell transform (ST) and Hilbert transform (HT) for identification of faults. An indicator for ground involvement during faulty condition (SGFI) is being suggested to detect the type of fault. The categorization of faults is done by utilizing faulty phase numbers and SGFI. It is found that the proposed technique is eff

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Manjunatha, G., and H. C. Chittappa. "Bearing Fault Classification using Empirical Mode Decomposition and Machine Learning Approach." Journal of Mines, Metals and Fuels 70, no. 4 (2022): 214. http://dx.doi.org/10.18311/jmmf/2022/30060.

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Industrial machinery often breakdowns due to faults in rolling bearing. Bearing diagnosis plays a vital role in condition monitoring of machinery. Operating conditions and working environment of bearings make them prone to single or multiple faults. In this research, signals from both healthy and faulty bearings are extracted and decomposed into empirical modes. By analyzing different empirical modes from 8 derived empirical modes for healthy and faulty bearings under different fault sizes, the first mode has the most information to classify bearing condition. From the first empirical mode eig

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3

Fang, Liang, and Hongchun Sun. "Study on EEMD-Based KICA and Its Application in Fault-Feature Extraction of Rotating Machinery." Applied Sciences 8, no. 9 (2018): 1441. http://dx.doi.org/10.3390/app8091441.

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A method is proposed to improve the feature extraction of vibration signals of rotating machinery. Firstly, the single-channel vibration signal is decomposed with ensemble empirical mode decomposition (EEMD). Then, the number of fault signals can be estimated with singular-value decomposition (SVD). Finally, the fault signals can be extracted with kernel-independent component analysis (KICA). The advantage of this method is that it can estimate the number of fault signals of single-channel vibration signals and can extract the fault features clearly. Compared with wavelets, empirical mode deco

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Zhang, Dingcheng, Dejie Yu, and Xing Li. "Optimal resonance-based signal sparse decomposition and its application to fault diagnosis of rotating machinery." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 24 (2016): 4670–83. http://dx.doi.org/10.1177/0954406216671542.

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The fault diagnosis of rotating machinery is quite important for the security and reliability of the overall mechanical equipment. As the main components in rotating machinery, the gear and the bearing are the most vulnerable to faults. In actual working conditions, there are two common types of faults in rotating machinery: the single fault and the compound fault. However, both of them are difficult to detect in the incipient stage because the weak fault characteristic signals are usually submerged by strong background noise, thus increasing the difficulty of the weak fault feature extraction

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Tong, Shuiguang, Yidong Zhang, Jian Xu, and Feiyun Cong. "Pattern recognition of rolling bearing fault under multiple conditions based on ensemble empirical mode decomposition and singular value decomposition." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 232, no. 12 (2017): 2280–96. http://dx.doi.org/10.1177/0954406217715483.

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In rotating machinery, the malfunctions of rolling bearings are one of the most common faults. To prevent machine breakdown, the pattern recognition of rolling bearing faults has been a pivotal issue for fault identification and classification. This study proposes a new feature extraction method based on ensemble empirical mode decomposition (EEMD) and singular value decomposition (SVD) for fault classification. The proposed E–S method (EEMD combined with SVD using feature parameters) intends to enhance the faults identification capability in different working conditions, including various fau

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Jing, Liuming, Lei Xia, Tong Zhao, and Jinghua Zhou. "An Improved Arc Fault Location Method of DC Distribution System Based on EMD-SVD Decomposition." Applied Sciences 13, no. 16 (2023): 9132. http://dx.doi.org/10.3390/app13169132.

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The influence of the control strategy of the power electronic converter obscures the fault characteristics of DC distribution networks. The existence of arc faults over an extended period of time poses a grave threat to the security of power grids and may result in electric shock, fire, and other catastrophes. In recent years, the method of fault localization based on the traveling wave method has been a popular topic of research in the field of DC distribution system protection. In this paper, the fault localization principle of the traveling wave method is described in depth, and the propaga

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Liao, Zhiqiang, Xuewei Song, Baozhu Jia, and Peng Chen. "Automatic Bearing Fault Feature Extraction Method via PFDIC and DBAS." Mathematical Problems in Engineering 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/6655081.

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Determining the embedded dimension of a singular value decomposition Hankel matrix and selecting the singular values representing the intrinsic information of fault features are challenging tasks. Given these issues, this work presents a singular value decomposition-based automatic fault feature extraction method that uses the probability-frequency density information criterion (PFDIC) and dual beetle antennae search (DBAS). DBAS employs embedded dimension and singular values as dynamic variables and PFDIC as a two-stage objective to optimize the best parameters. The optimization results work

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Hu, Pan, Cunsheng Zhao, Jicheng Huang, and Tingxin Song. "Intelligent and Small Samples Gear Fault Detection Based on Wavelet Analysis and Improved CNN." Processes 11, no. 10 (2023): 2969. http://dx.doi.org/10.3390/pr11102969.

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Traditional methods for identifying gear faults typically require a substantial number of faulty samples, which in reality are challenging to obtain. To tackle this challenge, this paper introduces a sophisticated approach for intelligent gear fault identification, utilizing discrete wavelet decomposition and an enhanced convolutional neural network (CNN) optimized for scenarios with limited sample data. Initially, the features of the sample signal are extracted and enhanced using discrete wavelet decomposition. Subsequently, the refined signal is transformed into a two-dimensional image throu

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Dou, Chun Hong. "Fault Feature Extraction for Gearboxes Using Empirical Mode Decomposition." Advanced Materials Research 383-390 (November 2011): 1376–80. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1376.

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The paper uses empirical mode decomposition to extract the fault feature of gearboxes. Traditional techniques fail to process the non-stationary and nonlinear signals. Empirical mode decomposition is a powerful tool for the non-stationary and nonlinear signal analysis and has attracted considerable attention recently. First, a simulation signal is used to measure the performance of the empirical mode decomposition method. Then, the empirical mode decomposition method is applied to analyze the signals captured from the gearbox with multiple faults and successfully extracts the multiple fault in

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Zhao, Nanyang, Zhiwei Mao, Donghai Wei, Haipeng Zhao, Jinjie Zhang, and Zhinong Jiang. "Fault Diagnosis of Diesel Engine Valve Clearance Based on Variational Mode Decomposition and Random Forest." Applied Sciences 10, no. 3 (2020): 1124. http://dx.doi.org/10.3390/app10031124.

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Diesel engines, as power equipment, are widely used in the fields of the automobile industry, ship industry, and power equipment. Due to wear or faulty adjustment, the valve train clearance abnormal fault is a typical failure of diesel engines, which may result in the performance degradation, even valve fracture and cylinder hit fault. However, the failure mechanism features mainly in the time domain and angular domain, on which the current diagnosis methods are based, are easily affected by working conditions or are hard to extract accurate enough from, as the diesel engine keeps running in t

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Dissertations / Theses on the topic "Fault decomposition"

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Arkan, Muslum. "Stator fault diagnosis in induction motors." Thesis, University of Sussex, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310244.

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Needham, Donald Michael. "A formal approach to hazard decomposition in Software Fault Tree Analysis." Thesis, Monterey, California: Naval Postgraduate School, 1990. http://hdl.handle.net/10945/28230.

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As digital control systems are used in life-critical applications, assessment of the safety of these control systems becomes increasingly important. One means of formally performing this assessment is through fault tree analysis. Software Fault Tree Analysis (SFTA) starts with a system-level hazard that must be decomposed in a largely-human-intensive manner until specific modules of the software system are indicated. These modules can then be formally analyzed using statement templates. The focus of this thesis is to approach the decomposition of a system-level hazard from a formalized standpo

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Ye, Fanchao. "Fault decomposition characteristics and application feasibility assessment of C4F7N-CO2-O2 mixed insulating gas." Electronic Thesis or Diss., Orléans, 2023. http://www.theses.fr/2023ORLE1030.

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Dans ce travail de doctorat, une étude théorique et expérimentale systématique a été menée sur l'isolation du mélange de gaz C4F7N-CO2-O2 respectueux de l'environnement et ses caractéristiques de décomposition et de biosécurité sous défauts électriques et thermiques. Sur la base de la méthode de dynamique moléculaire ReaxFF, le processus de décomposition thermique du mélange gazeux sous différentes teneurs en O2 et températures est simulé. En combinant les résultats simulés avec des essais de décomposition thermique, le processus cinétique de décomposition thermique du mélange gazeux et le méc

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BUZZONI, Marco. "Development and validation of Blind Deconvolution and Empirical Mode Decomposition techniques for impulsive fault diagnosis in rotating machines." Doctoral thesis, Università degli studi di Ferrara, 2018. http://hdl.handle.net/11392/2478776.

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La diagnosi di difetti in macchine rotanti basata sull’analisi vibrazionale ha raggiunto una soddisfacente fase di maturità, essendo disponibili numerose metodologie consolidate per la rilevazione e l’identificazione di difetti. Tuttavia, diverse problematiche restano ancora aperte; questa tesi ne prende in considerazione due. Da un lato, la ciclostazionarietà non è stata ancora utilizzata esplicitamente per progettare criteri di deconvoluzione cieca per la diagnosi di macchine rotanti, sebbene l'importanza di applicare la ciclostazionarietà per scopi diagnostici sia stata ampiamente riconosci

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KEHLENBACH, JOSUA. "Fault diagnosis of axlebox roller bearings of high speed rail vehicles based on empirical mode decomposition and machine learning." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299774.

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Axlebox bearings are one of the most critical components of a rail vehicle with regard to safety. An axlebox bearing that breaks during operation can be dangerous for the passengers and expensive for the operator. In-service failure of axlebox bearings has been the cause of many catastrophic accidents. Thus, it is of utmost importance to predict bearing failures as early as possible. This will increase reliability and safety of the vehicle as well as reduce the vehicle maintenance cost. Monitoring of roller bearings is an active research eld, and many methods have been proposed by other resear

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Kroenke, Samantha E. "A Study of the Herald-Phillipstown Fault in the Wabash Valley using Drillhole and 3-D Seismic Reflection Data." OpenSIUC, 2011. https://opensiuc.lib.siu.edu/theses/676.

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In June 2009, a 2.2 square mile 3-D high resolution seismic reflection survey was shot in southeastern Illinois in the Phillipstown Consolidated oilfield. A well was drilled in the 3-D survey area to tie the seismic to the geological data with a synthetic seismogram from the sonic log. The objectives of the 3-D seismic survey were three-fold: 1.) To image and interpret faulting of the Herald-Phillipstown Fault using drillhole-based geological and seismic cross-sections and structural contour maps created from the drillhole data and seismic reflection data, 2.) To test the effectiveness of i

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Maree, J. P. (Johannes Philippus). "Fault detection for the Benfield process using a closed-loop subspace re-identification approach." Diss., University of Pretoria, 2008. http://hdl.handle.net/2263/29844.

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Closed-loop system identification and fault detection and isolation are the two fundamental building blocks of process monitoring. Efficient and accurate process monitoring increases plant availability and utilisation. This dissertation investigates a subspace system identification and fault detection methodology for the Benfield process, used by Sasol, Synfuels in Secunda, South Africa, to remove CO2 from CO2-rich tail gas. Subspace identification methods originated between system theory, geometry and numerical linear algebra which makes it a computationally efficient tool to estimate system

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Abboud, Layane. "Time Reversal techniques applied to wire fault detection and location in wire networks." Phd thesis, Supélec, 2012. http://tel.archives-ouvertes.fr/tel-00771964.

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In this thesis we present new approaches in the domains of soft fault detection and location in complex wire networks, based on the properties of time reversal. When addressing the detection of soft faults, the idea is to adapt the testing signal to the network under test, instead of being predefined for all the tested networks, as opposed to standard reflectometry techniques. We prove that this approach, which we name the Matched Pulse approach (MP), is beneficial whenever the system is more complex, i.e., its response is richer in echoes, which is opposed to common understanding. The MP anal

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Picchi, Daniel da Costa. "Avaliação da técnica de decomposição por componentes ortogonais para identificação de faltas de alta impedância." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/18/18153/tde-13122018-134842/.

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Este trabalho apresenta o estado da arte das técnicas mais aplicadas para localização de faltas e modelagem de faltas de alta impedância e propõe a utilização de uma recente técnica baseada na decomposição dos sinais em componentes ortogonais. Este estudo avalia a aplicabilidade da técnica proposta utilizando dados reais de um sistema de distribuição de energia brasileiro, além de apresentar os conceitos teóricos sobre a decomposição em componentes ortogonais.<br>This work presents the state of the art of the most used techniques for locating and modelling high impedance faults and proposes th

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Jalboub, Mohamed K. "Investigation of the application of UPFC controllers for weak bus systems subjected to fault conditions. An investigation of the behaviour of a UPFC controller: the voltage stability and power transfer capability of the network and the effect of the position of unsymmetrical fault conditions." Thesis, University of Bradford, 2012. http://hdl.handle.net/10454/5699.

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In order to identify the weakest bus in a power system so that the Unified Power Flow Controller could be connected, an investigation of static and dynamic voltage stability is presented. Two stability indices, static and dynamic, have been proposed in the thesis. Multi-Input Multi-Output (MIMO) analysis has been used for the dynamic stability analysis. Results based on the Western System Coordinate Council (WSCC) 3-machine, 9-bus test system and IEEE 14 bus Reliability Test System (RTS) shows that these indices detect with the degree of accuracy the weakest bus, the weakest line and the volta

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Books on the topic "Fault decomposition"

1

Needham, Donald Michael. A formal approach to hazard decomposition in Software Fault Tree Analysis. Naval Postgraduate School, 1990.

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2

McCullers, William T. III. Probabilistic analysis of fault trees using pivotal decomposition. 1985.

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Book chapters on the topic "Fault decomposition"

1

Breedveld, Peter C. "Decomposition of Multiports." In Bond Graphs for Modelling, Control and Fault Diagnosis of Engineering Systems. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-47434-2_1.

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Ragot, José, Didier Maquin, and Frédéric Kratz. "Observability and Redundancy Decomposition Application to Diagnosis." In Issues of Fault Diagnosis for Dynamic Systems. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-3644-6_3.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Statistics Decomposition and Monitoring in Original Variable Space." In Intelligent Control and Learning Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_6.

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AbstractThe traditional process monitoring method first projects the measured process data into the principle component subspace (PCS) and the residual subspace (RS), then calculates $$\mathrm T^2$$ T 2 and $$\mathrm SPE$$ S P E statistics to detect the abnormality. However, the abnormality by these two statistics are detected from the principle components of the process. Principle components actually have no specific physical meaning, and do not contribute directly to identify the fault variable and its root cause. Researchers have proposed many methods to identify the fault variable accurately based on the projection space. The most popular is contribution plot which measures the contribution of each process variable to the principal element (Wang et al. 2017; Luo et al. 2017; Liu and Chen 2014). Moreover, in order to determine the control limits of the two statistics, their probability distributions should be estimated or assumed as specific one. The fault identification by statistics is not intuitive enough to directly reflect the role and trend of each variable when the process changes.

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Daigle, Matthew J., Anibal Bregon, and Indranil Roychoudhury. "Diagnosis of Hybrid Systems Using Structural Model Decomposition." In Fault Diagnosis of Hybrid Dynamic and Complex Systems. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74014-0_8.

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Chanthery, Elodie, Anna Sztyber, Louise Travé-Massuyès, and Carlos Gustavo Pérez-Zuñiga. "Process Decomposition and Test Selection for Distributed Fault Diagnosis." In Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55789-8_78.

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Maslennikow, Oleg, Juri Kaniewski, and Roman Wyrzykowski. "Fault tolerant QR-decomposition algorithm and its parallel implementation." In Euro-Par’98 Parallel Processing. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0057933.

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Narendra, Amreet, Purushottam Mathur, and Himadri Lala. "Fault Diagnosis in Transmission Line Using Empirical Mode Decomposition." In Advances in Intelligent Systems and Computing. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-6349-8_15.

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Xing, J. P., and T. R. Lin. "Bearing Fault Diagnosis Based on the Variational Mode Decomposition Technique." In Engineering Assets and Public Infrastructures in the Age of Digitalization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-48021-9_75.

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Kim, J. H., and S. M. Reddy. "Fault-Tolerant LU-Decomposition in a Two-Dimensional Systolic Array." In Concurrent Computations. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-5511-3_29.

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Lin, Jinshan. "Fault Feature Extraction of Gearboxes Using Ensemble Empirical Mode Decomposition." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23214-5_63.

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Conference papers on the topic "Fault decomposition"

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Zhao, Yuntao, and Zhenglu Li. "Adaptive Variational Mode Decomposition for Bearing Fault Diagnosis." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865109.

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Teng, Chao, Zuogang Shang, Xuechun Bai, and Ruqiang Yan. "Successive Difference Mode Decomposition for Rotating Machine Fault Diagnosis." In 2024 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD). IEEE, 2024. https://doi.org/10.1109/icsmd64214.2024.10920554.

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Varwandkar, Suresh. "Phase Network Decomposition for Fault Analysis Without Symmetrical Components." In 2024 23rd National Power Systems Conference (NPSC). IEEE, 2024. https://doi.org/10.1109/npsc61626.2024.10987055.

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Zeng, Mengjie, Ningyun Lu, Tingqian Tu, Keyi Zhou, Ruizhi Qin, and Bin Jiang. "Deep Feature Decomposition Autoencoder: Intelligent Fault Diagnosis for Unknown Faults in Landing Gear Systems." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865542.

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Zhao, Jiadong, and Fan Peng. "Application of SSA-VMD decomposition in parallel arc fault detection." In 2024 3rd International Conference on Energy, Power and Electrical Technology (ICEPET). IEEE, 2024. http://dx.doi.org/10.1109/icepet61938.2024.10626889.

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Song, Yang, Yijia Lu, and Tianyong Hu. "Data-Driven Fault Detection in Gas Regulators Using Empirical Mode Decomposition." In 2024 4th International Conference on Electrical Engineering and Control Science (IC2ECS). IEEE, 2024. https://doi.org/10.1109/ic2ecs64405.2024.10927728.

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Peng, Yu, Yuhang Liu, Ruishi Lin, Gang Xiang, Shenhang Wang, and Zhiming Yang. "Intermittent fault diagnosis based on EMD decomposition and convolutional neural network." In 2024 IEEE 2nd International Conference on Electrical, Automation and Computer Engineering (ICEACE). IEEE, 2024. https://doi.org/10.1109/iceace63551.2024.10898531.

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Fan, Xianfeng, and Ming J. Zuo. "Gearbox Fault Detection Using Empirical Mode Decomposition." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-59349.

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Local faults in a gearbox cause impacts and the collected vibration signal is often non-stationary. Identification of impulses within the non-stationary vibration signal is key to fault detection. Recently, the technique of Empirical Mode Decomposition (EMD) was proposed as a new tool for analysis of non-stationary signal. EMD is a time series analysis method that extracts a custom set of bases that reflects the characteristic response of a system. The Intrinsic Mode Functions (IMFs) within the original data can be obtained through EMD. We expect that the change in the amplitude of the special

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Qiang, Li, Chen Xin, Xiao Dengyi, et al. "Subtle Fault Prediction Technique Based on the Integration of Deep Learning and Seismic Spectral Decomposition." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211631-ms.

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Abstract Faults often control the movement and aggregation of oil and gas. With the development of oil fields, the role of subtle faults is becoming more and more important. The accuracy of fault interpretation directly affects the direction of exploration and development. However, due to the limitation of the seismic resolution, it is hard to identify these faults according to routine methods such as coherence, variance, curvature, etc. To overcome such kind of challenge and better match the demand for fine fault identification, a method integrated deep learning and spectral decomposition was

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Karakatic, Saso, Dusan Fister, Omer Faruk Beyca, and Iztok Fister. "Optimized Class Decomposition for Fault Detection." In 2021 IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2021. http://dx.doi.org/10.1109/cinti53070.2021.9668488.

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Reports on the topic "Fault decomposition"

1

Sargsyan, Khachik, Khachik Sargsyan, Cosmin Safta, et al. Fault Resilient Domain Decomposition Preconditioner for PDEs. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1494624.

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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext

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