Gotowe bibliografie tematyczne / Fault identification and classification

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Fault identification and classification”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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Artykuły w czasopismach na temat "Fault identification and classification"

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Mahaweerawat, Atchara, Peraphon Sophatsathit, Chidchanok Lursinsap, and Petr Musilek. "MASP – An Enhanced Model of Fault Type Identification in Object-Oriented Software Engineering." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (2006): 312–22. http://dx.doi.org/10.20965/jaciii.2006.p0312.

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To remain competitive in the dynamic world of software development, organizations must optimize the use of their limited resources to deliver quality products on time and within budget. This requires prevention of fault introduction and quick discovery and repair of residual faults. In this paper, a new model for predicting and identifying of faults in object-oriented software systems is introduced. In particular, faults due to the use of inheritance and polymorphism are considered as they account for significant portion of faults in object-oriented systems. The proposed MASP model acts as a f
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Liu, Chunyang, Weiwei Zou, Zhilei Hu, et al. "Bearing Health State Detection Based on Informer and CNN + Swin Transformer." Machines 12, no. 7 (2024): 456. http://dx.doi.org/10.3390/machines12070456.

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In response to the challenge of timely fault identification in the spindle bearings of machine tools operating in complex environments, this study proposes a method based on a combination of infrared imaging with an Informer and a CNN + Swin Transformer. The aim is to achieve real-time monitoring of bearing faults, precise fault localization, and classification of fault severity. To accomplish this, an angular contact ball bearing was chosen as the research subject. Initially, an infrared image dataset was constructed, encompassing various fault positions and degrees, by simulating different f
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Pérez-Ruiz, Juan Luis, Igor Loboda, Iván González-Castillo, Víctor Manuel Pineda-Molina, Karen Anaid Rendón-Cortés, and Luis Angel Miró-Zárate. "A comparative study of data-driven and physics-based gas turbine fault recognition approaches." Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability 235, no. 4 (2021): 591–609. http://dx.doi.org/10.1177/1748006x21989648.

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The present paper compares the fault recognition capabilities of two gas turbine diagnostic approaches: data-driven and physics-based (a.k.a. gas path analysis, GPA). The comparison takes into consideration two differences between the approaches, the type of diagnostic space and diagnostic decision rule. To that end, two stages are proposed. In the first one, a data-driven approach with an artificial neural network (ANN) that recognizes faults in the space of measurement deviations is compared with a hybrid GPA approach that employs the same type of ANN to recognize faults in the space of esti
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Cheng, Guanyuan, and Shaojian Song. "Fault Detection and Identification in MMCs Based on DSCNNs." Energies 16, no. 8 (2023): 3427. http://dx.doi.org/10.3390/en16083427.

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Fault detection and location is one of the critical issues in engineering applications of modular multilevel converters (MMCs). At present, MMC fault diagnosis based on neural networks can only locate the open-circuit fault of a single submodule. To solve this problem, this paper proposes a fault detection and localization strategy based on a depthwise separable convolutional (DSC) neural network. By inputting the bridge arm circulating current and the submodule capacitor voltage into two serially connected neural networks, not only can this method achieve the classification of submodule open-
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Mashayekhi, V., S. Hasani Borzadaran, and M. Hoseintabar Marzebali. "Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks." International Transactions on Electrical Energy Systems 2022 (February 16, 2022): 1–13. http://dx.doi.org/10.1155/2022/4224356.

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Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the lon
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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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Xian, Xiaoyu, Haichuan Tang, Yin Tian, Qi Liu, and Yuming Fan. "Performance Analysis of Different Machine Learning Algorithms for Identifying and Classifying the Failures of Traction Motors." Journal of Physics: Conference Series 2095, no. 1 (2021): 012058. http://dx.doi.org/10.1088/1742-6596/2095/1/012058.

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Abstract This paper addresses electric motor fault diagnosis using supervised machine learning classification. A total of 15 distinct fault types are classified and multilabel strategies are used to classify concurrent faults. we explored, developed, and compared the performance of different types of binary (fault/non-fault), multi-class (fault type) and multi-label (single fault versus combination fault) classifiers. To evaluate the effectiveness of fault identification and classification, we used different supervised machine learning methods, including Random forest classification, support v
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Lodhi, Raja, and Rajkumar Sharma. "A Practical Approach of Software Fault Prediction Using Error Probabilities and Machine Learning Approaches." International Journal of Research 11, no. 5 (2024): 124–37. https://doi.org/10.5281/zenodo.11195244.

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<em>identification of software faults associated with software. The identification of faults is usually carried out using the task of classification. The task of classification utilises the code attributes and other features to predict the fault instances. The detection of software faults is prominently affected by a poor classification decision and hence an improved decision-making model is required to predict the patterns using the attributes collected out from the datasets. In the first part of the research, the study proposes a Bayes Decision classifier associated with the finding of error
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Bagbaba, Ahmet Cagri, Felipe Augusto da Silva, Matteo Sonza Reorda, Said Hamdioui, Maksim Jenihhin, and Christian Sauer. "Automated Identification of Application-Dependent Safe Faults in Automotive Systems-on-a-Chips." Electronics 11, no. 3 (2022): 319. http://dx.doi.org/10.3390/electronics11030319.

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ISO 26262 requires classifying random hardware faults based on their effects (safe, detected, or undetected) within integrated circuits used in automobiles. In general, this classification is addressed using expert judgment and a combination of tools. However, the growth of integrated circuit complexity creates a huge fault space; hence, this form of fault classification is error prone and time consuming. Therefore, an automated and systematic approach is needed to target hardware fault classification in automotive systems on chips (SoCs), considering the application software. This work focuse
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Dhaked, Dheeraj Kumar, Lokesh Kumar Raman, Dinesh Birla, and Maheep Dwivedi. "Identification and classification of faults using fuzzy logic controller in transmission line." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 417–30. http://dx.doi.org/10.47974/jim-1672.

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This manuscript discusses the identification and categorization of faults in transmission network system with fuzzy logic controller (FLC) with high-speed digital protective relay, which can be used for real-time data analysis. The FLC uses signals for the extraction of original signals for multi-resolution analysis. The proposed FLC technique detects the fault occurrence based on fault index. The stoutness of technique is demonstrated on transmission line models under different fault situations on MATLAB Simulink platform for the post-fault current of all three phases the end of line. In the
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Rozprawy doktorskie na temat "Fault identification and classification"

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Venkatesh, Vidya. "Fault Classification and Location Identification on Electrical Transmission Network Based on Machine Learning Methods." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5582.

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Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases w
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Li, Zhongliang. "Data-driven fault diagnosis for PEMFC systems." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM4335/document.

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Cette thèse est consacrée à l'étude de diagnostic de pannes pour les systèmes pile à combustible de type PEMFC. Le but est d'améliorer la fiabilité et la durabilité de la membrane électrolyte polymère afin de promouvoir la commercialisation de la technologie des piles à combustible. Les approches explorées dans cette thèse sont celles du diagnostic guidé par les données. Les techniques basées sur la reconnaissance de forme sont les plus utilisées. Dans ce travail, les variables considérées sont les tensions des cellules. Les résultats établis dans le cadre de la thèse peuvent être regroupés en
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Romano, Donato. "Machine Learning algorithms for predictive diagnostics applied to automatic machines." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22319/.

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In questo lavoro di tesi è stato analizzato l'avvento dell'industria 4.0 all'interno dell' industria nel settore packaging. In particolare, è stata discussa l'importanza della diagnostica predittiva e sono stati analizzati e testati diversi approcci per la determinazione di modelli descrittivi del problema a partire dai dati. Inoltre, sono state applicate le principali tecniche di Machine Learning in modo da classificare i dati analizzati nelle varie classi di appartenenza.
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Syal, Manan. "Untestable Fault Identification Using Implications." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/46173.

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Untestable faults in circuits are defects/faults for which there exists no test pattern that can either excite the fault or propagate the fault effect to an observable point, which could be either a Primary output (PO) or a scan flip-flop. The current state-of-the-art automatic test pattern generators (ATPGs) spend a lot of time in trying to generate a test sequence for the detection of untestable faults, before aborting on them, or identifying them as untestable, given enough time. Thus, it would be beneficial to quickly identify faults that are redundant/untestable, so that tools such as ATP
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Waly, Hashem. "Automated Fault Identification - Kernel Trace Analysis." Thesis, Université Laval, 2011. http://www.theses.ulaval.ca/2011/28246/28246.pdf.

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Fan, Wen. "ADVANCED FAULT AREA IDENTIFICATION AND FAULT LOCATION FOR TRANSMISSION AND DISTRIBUTION SYSTEMS." UKnowledge, 2019. https://uknowledge.uky.edu/ece_etds/144.

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Fault location reveals the exact information needed for utility crews to timely and promptly perform maintenance and system restoration. Therefore, accurate fault location is a key function in reducing outage time and enhancing power system reliability. Modern power systems are witnessing a trend of integrating more distributed generations (DG) into the grid. DG power outputs may be intermittent and can no longer be treated as constants in fault location method development. DG modeling is also difficult for fault location purpose. Moreover, most existing fault location methods are not applicab
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Felldin, Markus. "Machine Learning Methods for Fault Classification." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-183132.

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This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future.<br>Detta examensarbete, utfört på Ericsson AB, ämnar
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Mufti, Muid Ur-Rahman. "Fault detection and identification using fuzzy wavelets." Diss., Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/16472.

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Wang, Tsang-Yi Varshney Pramod K. "Distributed fault-tolerant classification using coding theory." Related Electronic Resource: Current Research at SU : database of SU dissertations, recent titles available full text, 2003. http://wwwlib.umi.com/cr/syr/main.

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Covella, Vito Vincenzo. "Multi-node Fault Classification using Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/22867/.

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An HPC system, a system with much more computational power than general computing systems, is a complex system made up of different sections and many computing nodes. In such systems failures can arise for different reasons: because of the interactions among the components, because of the specific technologies used or because of bugs in the software. In order to reach Exascale performances and guarantee availability and reliability it is important to detect and recover from these anomalies. In this thesis we propose a fault classification method based on machine learning. Other researchers
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Książki na temat "Fault identification and classification"

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1924-, Hansbo Sven, Svenska geotekniska föreningen Laboratoriekommittén, and Statens råd för byggnadsforskning (Sweden), eds. Soil classification and identification. Swedish Council for Building Research, 1989.

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America, Culinary Institute of, ed. Cheese: Identification, classification, utilization. Delmar Cengage Learning, 2011.

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Potiron, Katia, Amal El Fallah Seghrouchni, and Patrick Taillibert. From Fault Classification to Fault Tolerance for Multi-Agent Systems. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5046-6.

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Potiron, Katia. From Fault Classification to Fault Tolerance for Multi-Agent Systems. Springer London, 2013.

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author, Winternitz Pavel, ed. Classification and identification of Lie algebras. American Mathematical Society, 2014.

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Smith, Andrew. Thet arantula classification and identification guide. Fitzgerald, 1986.

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Griffiths, Barry E. Integrated communication, navigation, and identification avionics resource allocation. Air Force Human Resources Laboratory, Air Force Systems Command, 1986.

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K, Stansby P., ed. Improved wave force classification using system identification. University of Sheffield, Dept. ofAutomatic Control and Systems Engineering, 1991.

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Veatch, Michael H. Integrated communication, navigation, and identification avionics: Impact analysis : executive summary. Air Force Human Resources Laboratory, Air Force Systems Command, 1985.

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Simani, Silvio, Cesare Fantuzzi, and Ronald Jon Patton. Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques. Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-3829-7.

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Części książek na temat "Fault identification and classification"

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Hubana, Tarik, Mirza Šarić, and Samir Avdaković. "New Approach for Fault Identification and Classification in Microgrids." In Advanced Technologies, Systems, and Applications IV -Proceedings of the International Symposium on Innovative and Interdisciplinary Applications of Advanced Technologies (IAT 2019). Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-24986-1_3.

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Adel, Afia, Ouelmokhtar Hand, Gougam Fawzi, Touzout Walid, Rahmoune Chemseddine, and Benazzouz Djamel. "Gear Fault Detection, Identification and Classification Using MLP Neural Network." In Lecture Notes in Mechanical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4835-0_18.

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Wang, Jing, Jinglin Zhou, and Xiaolu Chen. "Simulation Platform for Fault Diagnosis." In Intelligent Control and Learning Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_4.

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AbstractThe previous chapters have described the mathematical principles and algorithms of multivariate statistical methods, as well as the monitoring processes when used for fault diagnosis. In order to validate the effectiveness of data-driven multivariate statistical analysis methods in the field of fault diagnosis, it is necessary to conduct the corresponding fault monitoring experiments. Therefore this chapter introduces two kinds of simulation platform, Tennessee Eastman (TE) process simulation system and fed-batch Penicillin Fermentation Process simulation system. They are widely used a
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Ghosh, Prasad Ranjan, Subodh Kumar Mohanty, and Srikanta Mohapatra. "Fault Zone Identification and Fault Classification in a Series Compensated Transmission Line Using Decision Tree." In Advances in Power Systems and Energy Management. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7504-4_15.

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Axenie, Cristian, Radu Tudoran, Stefano Bortoli, Mohamad Al Hajj Hassan, Alexander Wieder, and Goetz Brasche. "SPICE: Streaming PCA Fault Identification and Classification Engine in Predictive Maintenance." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43887-6_27.

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Sharma, Deepika, Shoyab Ali, and Gaurav Kapoor. "A Fast Fault Identification and Classification Scheme for Series Compensated Transmission Lines." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3246-4_56.

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Hu, Shuang-yan, De-qin Shi, Xiao-shan Song, Lin-bo Fang, Wei-jun Yang, and Qi Tong. "Fault Diagnosis of Analog Circuits Based on Multi Classification SVDD Aliasing Region Identification." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70990-1_17.

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Bhavani, Kathula Kanaka Durga, and Venkatesh Yepuri. "Fault Classification and Its Identification in Overhead Transmission Lines Using Artificial Neural Networks." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6690-5_29.

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Joshi, Minesh K., and R. R. Patel. "Fault Detection, Classification, and Identification of Shunt Compensated Distribution System Through Wavelet Transforms." In Emerging Technologies in Electrical Engineering for Reliable Green Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9235-5_19.

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Mahela, Om Prakash, Vishnu Dutt Sharma, Baseem Khan, and Sunil Agarwal. "Identification of Transmission Line Faults Using Voltage-Based Stockwell Transform Features and Decision Rules Supported Fault Classification." In Artificial Intelligence-Based Energy Management Systems for Smart Microgrids. CRC Press, 2022. http://dx.doi.org/10.1201/b22884-12.

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Streszczenia konferencji na temat "Fault identification and classification"

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Premalatha, K., R. Janaranjani, S. Bhuvaneswari, and M. Karthik. "Transformer Fault Identification and Classification of Using Machine Learning." In 2024 2nd International Conference on Self Sustainable Artificial Intelligence Systems (ICSSAS). IEEE, 2024. https://doi.org/10.1109/icssas64001.2024.10760901.

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Nekrasov, Ivan, Vladimir Bukhtoyarov, and Galina Gorovaya. "Classification of Technical Condition of Pumping Unit Using Intelligent Fault Identification." In 2024 International Russian Automation Conference (RusAutoCon). IEEE, 2024. http://dx.doi.org/10.1109/rusautocon61949.2024.10694187.

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Agarwal, Muskan, Kanwarpartap Singh Gill, Sonal Malhotra, and Swati Devliyal. "Logistic Regression Utilization for Electrical Fault Identification And Classification Using Machine Learning." In 2024 Asia Pacific Conference on Innovation in Technology (APCIT). IEEE, 2024. http://dx.doi.org/10.1109/apcit62007.2024.10673522.

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Bharathi, M., A. Amsaveni, and K. Sowmiya. "Enhanced Fault Identification and Classification of Wind Turbine Blades using Machine Learning." In 2024 7th International Conference on Signal Processing and Information Security (ICSPIS). IEEE, 2024. https://doi.org/10.1109/icspis63676.2024.10812622.

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Chen, Musheng, Qing Huang, and Junhua Wu. "Research on Fault Identification and Classification Based on Spearman Analysis and XGBoost." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11022091.

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Sondoule, Arya Arun, Hritesh Saini, MD Sajid Alam, Premalata Jena, and Narayana Prasad Padhy. "Advanced Fault Identification, Classification, and Localization Technique for Enhanced Protection in DC Zonal Microgrids." In 2024 IEEE 11th Power India International Conference (PIICON). IEEE, 2024. https://doi.org/10.1109/piicon63519.2024.10995147.

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Davi, Moisés J. B. B., Talita M. O. A. Cunha, Emanuel P. G. Oliveira, and Mário Oleskovicz. "Exploring Machine Learning-Based Solutions for Fault Classification and Region Identification in Onshore Wind Farm Collector Systems." In 2024 21st International Conference on Harmonics and Quality of Power (ICHQP). IEEE, 2024. https://doi.org/10.1109/ichqp61174.2024.10768736.

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Xue, Wang, Yu Haiyang, and Wang Cong. "Fault classification and location identification for microgrids utilizing maximal overlap discrete wavelet packet transform based on machine learning." In 2024 IEEE 6th International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2024. https://doi.org/10.1109/iccasit62299.2024.10828007.

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ABADÍA, JOSÉ JOAQUÍN PERALTA, HENRIEKE FRITZ, KOSMAS DRAGOS, and KAY SMARSLY. "SENSOR FAULT DIAGNOSIS COUPLING DEEP LEARNING AND WAVELET TRANSFORMS." In Structural Health Monitoring 2021. Destech Publications, Inc., 2022. http://dx.doi.org/10.12783/shm2021/36327.

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Sensor networks facilitate collecting measurement data necessary for decision making regarding structural maintenance and rehabilitation in structural health monitoring (SHM) systems. Nevertheless, the reliability of decision making in SHM systems depends on the proper operation of the sensors. Sensors may exhibit faults, entailing faulty data and incorrect judgment of structural conditions. Therefore, fault diagnosis (FD), comprising detection, isolation, identification, and accommodation of sensor faults, has been introduced in SHM systems, enabling timely detection of faulty data while adva
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Zhongsheng Wang, Hongkai Jiang, and Yiyan Xu. "Early Fault Classification Identification and Fault Self-recovery on Aero-engine." In 2008 7th World Congress on Intelligent Control and Automation. IEEE, 2008. http://dx.doi.org/10.1109/wcica.2008.4593220.

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Raporty organizacyjne na temat "Fault identification and classification"

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Zio, Enrico, and Nicola Pedroni. Uncertainty characterization in risk analysis for decision-making practice. Fondation pour une culture de sécurité industrielle, 2012. http://dx.doi.org/10.57071/155chr.

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This document provides an overview of sources of uncertainty in probabilistic risk analysis. For each phase of the risk analysis process (system modeling, hazard identification, estimation of the probability and consequences of accident sequences, risk evaluation), the authors describe and classify the types of uncertainty that can arise. The document provides: a description of the risk assessment process, as used in hazardous industries such as nuclear power and offshore oil and gas extraction; a classification of sources of uncertainty (both epistemic and aleatory) and a description of techn
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Hugue, M. M. Fault Type Enumeration and Classification. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada242541.

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Lovett, Alex, Chyau Shen, Wil Otaguro, and Tom Glesne. Microdoppler: NonCooperative Target Classification/Identification. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada375771.

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Houston, Brian H., Tim Yoder, and Larry Carin. Harbor Threat Detection, Classification, and Identification. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada612415.

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Houston, Brian H., and Larry Carin. Harbor Threat Detection, Classification, and Identification. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada541163.

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Wu, Alex, and Myriam Abramson. Image Classification for Web Genre Identification. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada599790.

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Shabalina, A., A. Carpenter, M. Rahman, C. Tennant, and L. Vidyaratne. Machine Learning Based Cavity Fault Classification and Prediction. Office of Scientific and Technical Information (OSTI), 2020. http://dx.doi.org/10.2172/1735851.

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Bayba, Andrew J., David N. Siegel, Kwok Tom, and Derwin Washington. Health Assessment and Fault Classification of Roller Element Bearings. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada568919.

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Reed, Aaaron T. Bayesian Belief Networks for Fault Identification in Aircraft Gas Turbines. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada378859.

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Madani, Farshad. Opportunity Identification for New Product Planning: Ontological Semantic Patent Classification. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.6116.

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