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Статті в журналах з теми "Machine Diagnostic"
Duda, Arkadiusz, and Maciej Sułowicz. "A New Effective Method of Induction Machine Condition Assessment Based on Zero-Sequence Voltage (ZSV) Symptoms." Energies 13, no. 14 (July 9, 2020): 3544. http://dx.doi.org/10.3390/en13143544.
Повний текст джерелаFrosini, Lucia. "Novel Diagnostic Techniques for Rotating Electrical Machines—A Review." Energies 13, no. 19 (September 27, 2020): 5066. http://dx.doi.org/10.3390/en13195066.
Повний текст джерелаVeselovska, Nataliia. "DEVELOPMENT OF ALGORITHMIC SUPPORT FOR PRACTICAL IMPLEMENTATION OF TESTING AND DIAGNOSTIC COMPLEX OF CNC MACHINES." Vibrations in engineering and technology, no. 1(104) (April 29, 2022): 71–80. http://dx.doi.org/10.37128/2306-8744-2022-1-9.
Повний текст джерелаVISHNEVSKY, A. A., I. I. ARTOBOLEVSKY, and M. L. BYKHOVSKY. "Principles of Diagnostic Machine Construction1." Acta Medica Scandinavica 176, no. 2 (April 24, 2009): 129–35. http://dx.doi.org/10.1111/j.0954-6820.1964.tb00919.x.
Повний текст джерелаЗимовець, Вікторія Ігорівна, Олександр Сергійович Приходченко та Микита Ігорович Мироненко. "ІНФОРМАЦІЙНО-ЕКСТРЕМАЛЬНИЙ КЛАСТЕР-АНАЛІЗ ВХІДНИХ ДАНИХ ПРИ ФУНКЦІОНАЛЬНОМУ ДІАГНОСТУВАННІ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 4 (25 грудня 2019): 105–15. http://dx.doi.org/10.32620/reks.2019.4.12.
Повний текст джерелаNikitin, Yury, Pavol Božek, and Jozef Peterka. "Logical–Linguistic Model of Diagnostics of Electric Drives with Sensors Support." Sensors 20, no. 16 (August 8, 2020): 4429. http://dx.doi.org/10.3390/s20164429.
Повний текст джерелаFariz Qafarov, Fariz Qafarov, Elnarə Səlimova Elnarə Səlimova, and Aybəniz Əmirova Aybəniz Əmirova. "VIBRATION PROCESSES AND THEIR RELATIONSHIP WITH DEFECTS." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 11, no. 07 (November 5, 2021): 81–86. http://dx.doi.org/10.36962/pahtei1107202181.
Повний текст джерелаBartels, P. H., D. Thompson, H. G. Bartels, and R. Shoemaker. "Machine Vision System for Diagnostic Histopathology." Pathology - Research and Practice 185, no. 5 (December 1989): 635–46. http://dx.doi.org/10.1016/s0344-0338(89)80209-2.
Повний текст джерелаHRANIAK, Valerii, and Oleh HRYSHCHUK. "DEVELOPMENT OF THE CONCEPT OF BUILDING DIAGNOSTIC SYSTEMS OF ROTATING ELECTRICAL MACHINES UNDER THE CONDITIONS OF LIMITED INFORMATIONALITY OF DIAGNOSTIC SIGNS." Herald of Khmelnytskyi National University. Technical sciences 311, no. 4 (August 2022): 70–77. http://dx.doi.org/10.31891/2307-5732-2022-311-4-70-77.
Повний текст джерелаSzabó, József Zoltán. "Forgógépek üzem közbeni mozgásának próbapadi és ipari vizsgálata." Jelenkori Társadalmi és Gazdasági Folyamatok 7, no. 1-2 (January 1, 2012): 73–79. http://dx.doi.org/10.14232/jtgf.2012.1-2.73-79.
Повний текст джерелаДисертації з теми "Machine Diagnostic"
Kříž, Petr. "Online vibrační diagnostika vřetene frézovacího stroje DATRON." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2019. http://www.nusl.cz/ntk/nusl-402508.
Повний текст джерелаZhong, Binglin. "Model building and machine fault diagnosis." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340889.
Повний текст джерелаTcheeko, Lot. "Didacticiel d'apprentissage du diagnostic d'erreurs en langage machine." Paris 6, 1990. http://www.theses.fr/1990PA066332.
Повний текст джерелаRaoult, Olivier Lux Augustin Mossière Jacques Demogeot Jacques. "Diagnostic de pannes des systèmes complexes." S.l. : Université Grenoble 1, 2008. http://tel.archives-ouvertes.fr/tel-00332209.
Повний текст джерелаSOAVE, Elia. "Diagnostics and prognostics of rotating machines through cyclostationary methods and machine learning." Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2490999.
Повний текст джерелаNegli ultimi decenni, l’analisi vibrazionale è stata sfruttata per il monitoraggio di molti sistemi meccanici per applicazioni industriali. Nonostante molte pubblicazioni abbiano dimostrato come la diagnostica vibrazionale possa raggiungere risultati soddisfacenti, lo scenario industriale odierno è in profondo cambiamento, guidato dalla necessità di ridurre tempi e costi produttivi. In questa direzione, la ricerca deve concentrarsi sul miglioramento dell’efficienza computazionale delle tecniche di analisi del segnale applicate a fini diagnostici. Allo stesso modo, il mondo industriale richiede una sempre maggior attenzione per la manutenzione predittiva, al fine di stimare l’effettivo danneggiamento del sistema evitando così inutili fermi macchina per operazioni manutentive. In tale ambito, negli ultimi anni l’attività di ricerca si sta spostando verso lo sviluppo di modelli prognostici finalizzati alla stima della vita utile residua dei componenti. Tuttavia, è importante ricordare come i due ambiti siano strettamente connessi, essendo la diagnostica la base su cui fondare l’efficacia di ciascun modello prognostico. Su questa base, questa tesi è stata incentrata su queste due diverse, ma tra loro connesse, aree al fine di identificare e predire possibile cause di cedimento su macchine rotanti per applicazioni industriali. La prima parte della tesi è concentrata sullo sviluppo di un nuovo indicatore di blind deconvolution per l’identificazione di difetti su organi rotanti sulla base della teoria ciclostazionaria. Il criterio presentato vuole andare a ridurre il costo computazionale richiesto dalla blind deconvolution tramite l’utilizzo della serie di Fourier-Bessel grazie alla sua natura modulata, maggiormente affine alla tipica firma vibratoria del difetto. L’indicatore proposto viene accuratamente confrontato con il suo analogo basato sulla classica serie di Fourier considerando sia segnali simulati che segnali di vibrazione reali. Il confronto vuole dimostrare il miglioramento fornito dal nuovo criterio in termini sia di minor numero di operazioni richieste dall’algoritmo che di efficacia diagnostica anche in condizioni di segnale molto rumoroso. Il contributo innovativo di questa parte riguarda la combinazione di ciclostazionarietà e serie di Furier-Bessel che porta alla definizione di un nuovo criterio di blind deconvolution in grado di mantenere l’efficacia diagnostica della ciclostazionarietà ma con un minor tempo computazionale per venire incontro alle richieste del mondo industriale. La second parte riguarda la definizione di un nuovo modello prognostico, appartenente alla famiglia degli hidden Markov models, costruito partendo da una distribuzione Gaussiana generalizzata. L’obbiettivo del metodo proposto è una miglior riproduzione della reale distribuzione dei dati, in particolar modo negli ultimi stadi del danneggiamento. Infatti, la comparsa e l’evoluzione del difetto comporta una modifica della distribuzione delle osservazioni fra i diversi stati. Di conseguenza, una densità di probabilità generalizzata permette la modificazione della forma della distribuzione tramite diversi valori dei parametri del modello. Il metodo proposto viene confrontato con il classico hidden Markov model di base Gaussiana in termini di qualità di riproduzione della distribuzione e predizione della sequenza di stati tramite l’analisi di alcuni test di rottura su cuscinetti volventi e sistemi complessi. L’innovatività di questa parte è data dalla definizione di un algoritmo iterativo per la stima dei parametri del modello nell’ipotesi di distribuzione Gaussiana generalizzata, sia nel caso monovariato che multivariato, partendo dalle osservazioni sul sistema fisico in esame.
Keuneke, Anne Marie. "Machine understanding of devices causal explanation of diagnostic conclusions /." The Ohio State University, 1989. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487671640057361.
Повний текст джерелаAbed, Aïcha. "Contribution à l'étude et au diagnostic de la machine asynchrone." Nancy 1, 2002. http://www.theses.fr/2002NAN10020.
Повний текст джерелаUsed in the majority of the electric drives, the asynchronous machine tends to supplant the machine with D. C. Current as well as the synchronous machine because of its many qualities, and mainly of its low cost and its robustness. Thus, a general reflexion is committed in modeling and diagnostic of induction machine defects. More particularly, we propose to study the rotor defects (broken bars in the rotor). In the first time, we develop two models of the asynchronous machine for the simulation of broken bars. We present in the continuation three methods to detect this fault. The principle of detection is based on the spectral analysis of the stator current in order to follow the evolution of the frequencies which are related to the fault. Lastly, a study of the defect in the presence of a classical vector control is presented, opening a new way towards a diagnostic in the case of speed variation. An experimental part is carried out to validate the exactitude of the theoretical results and to show the effectiveness of the developed methods
Bachir, Smaïl. "Contribution au diagnostic de la machine asynchrone par estimation paramétrique." Poitiers, 2002. http://www.theses.fr/2002POIT2306.
Повний текст джерелаKass, Souhayb. "Diagnostic vibratoire autonome des roulements." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI103.
Повний текст джерелаThe industrial and transportation sectors require more and more efficient and complex machines and installations increasing the risk of failure and disruption. This can lead to the immediate shutdown of a machine and disrupts the proper functioning of the entire production system. The diagnosis of industrial machines is essentially based on the monitoring of symptoms related to different degradation conditions. These symptoms can be derived from various sources of information, including vibration and acoustic signals. Nowadays, many effective techniques are well established, based on powerful tools offered by the theory of cyclostationary processes. The complexity of these tools requires an expert to use them and to interpret the results based on his/her experience. The continuous presence of the expert is expensive and difficult to achieve in practice. Condition indicators for rotating machines exist in the literature but they are conceived under the assumption of perfect operating conditions. They are limited, dispersed and generally not supported by theoretical frameworks. The main objective of this thesis is to reduce the use of human intervention by proposing strategies to design two optimal indicators that summarize diagnostic information into a scalar value. A distinction is made between two families in diagnosis: the case where prior information on the faults is known and the case where it is unknown. These indicators are designed to be used in an autonomous process without requiring human intervention, using statistical hypothesis tests. The capacity of these indicators is validated on real data and compared with other indicators from the literature in terms of detection performance
Hrbáček, Vlastimil. "Návrh provozních mezí pro diagnostický systém obráběcího stroje." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2020. http://www.nusl.cz/ntk/nusl-417438.
Повний текст джерелаКниги з теми "Machine Diagnostic"
Naidenova, Xenia, and Viktor Shagalov. Diagnostic test approaches to machine learning and commonsense reasoning systems. Hershey, PA: Information Science Reference, 2013.
Знайти повний текст джерелаMiller, Richard Kendall. Survey on X-ray machine vision and compute [sic] tomography. Madison, GA: Future Technology Surveys, 1989.
Знайти повний текст джерелаMachine learning in computer-aided diagnosis: Medical imaging intelligence and analysis. Hershey: Medical Information Science Reference, 2011.
Знайти повний текст джерела1970-, Gonzalez Fabio A., and Romero Eduardo 1963-, eds. Biomedical image analysis and machine learning technologies: Applications and techniques. Hershey, PA: Medical Information Science Reference, 2010.
Знайти повний текст джерела1970-, Gonzalez Fabio A., and Romero Eduardo 1963-, eds. Biomedical image analysis and machine learning technologies: Applications and techniques. Hershey, PA: Medical Information Science Reference, 2010.
Знайти повний текст джерелаMLMI 2010 (2010 Beijing, China). Machine learning in medical imaging: First International Workshop, MLMI 2010, held in conjunction with MICCAI 2010, Beijing, China, September 20, 2010 : proceedings. Berlin: Springer, 2010.
Знайти повний текст джерелаSibikin, Mihail, A. N. Chernenko, and Yuriya Voronkin. Technological equipment. Metal cutting machines. ru: INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1061257.
Повний текст джерелаTrigeassou, Jean-Claude. Electrical Machines Diagnosis. Hoboken, NJ, USA: John Wiley & Sons, Inc, 2011. http://dx.doi.org/10.1002/9781118601662.
Повний текст джерелаElectrical machines diagnosis. London: ISTE, 2011.
Знайти повний текст джерелаWilliams, J. Hywel. Condition-based maintenance and machine diagnostics. London: Chapman & Hall, 1994.
Знайти повний текст джерелаЧастини книг з теми "Machine Diagnostic"
Smith, Graham T. "Telescoping Ballbars and Other Diagnostic Instrumentation." In Machine Tool Metrology, 345–80. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25109-7_4.
Повний текст джерелаStrobel, Michael, and Hans-Werner Stedtfeld. "Machine Evaluation of Laxity." In Diagnostic Evaluation of the Knee, 258–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-74397-9_9.
Повний текст джерелаKukar, Matjaž, and Ciril Grošelj. "Machine Learning in Stepwise Diagnostic Process." In Artificial Intelligence in Medicine, 315–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48720-4_34.
Повний текст джерелаDavies, A., and J. H. Williams. "The condition monitoring of machine tools." In Condition Monitoring and Diagnostic Engineering Management, 44–48. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_8.
Повний текст джерелаSuzuki, Kenji. "Computerized Detection of Lesions in Diagnostic Images." In Machine Learning in Radiation Oncology, 101–31. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18305-3_7.
Повний текст джерелаBachschmid, Nicolò. "Some Examples of Incomplete Diagnostic Analyses of Industrial Machinery." In Mechanisms and Machine Science, 191–206. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99268-6_14.
Повний текст джерелаCleophas, Ton J., and Aeilko H. Zwinderman. "Logistic Regression for Assessing Novel Diagnostic Tests Against Control." In Machine Learning in Medicine, 45–52. Dordrecht: Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-94-007-6886-4_6.
Повний текст джерелаSun, Delin. "Development of New Diagnostic Techniques – Machine Learning." In Advances in Experimental Medicine and Biology, 203–15. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5562-1_10.
Повний текст джерелаLuelf, G., and R. Vogel. "Vibration analysis as tool for computerised machine monitoring." In Condition Monitoring and Diagnostic Engineering Management, 126–31. Dordrecht: Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0431-6_21.
Повний текст джерелаGuo, Qianjin, Haibin Yu, and Aidong Xu. "A New Intelligent Diagnostic Method for Machine Maintenance." In Advances in Machine Learning and Cybernetics, 760–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11739685_79.
Повний текст джерелаТези доповідей конференцій з теми "Machine Diagnostic"
Kachin, Oleg, and Sergey Kachin. "Diagnostic of moving machine parts." In 2012 7th International Forum on Strategic Technology (IFOST). IEEE, 2012. http://dx.doi.org/10.1109/ifost.2012.6357703.
Повний текст джерелаJanda, M., O. Vitek, and M. Skalka. "Noise diagnostic of induction machine." In 2010 XIX International Conference on Electrical Machines (ICEM). IEEE, 2010. http://dx.doi.org/10.1109/icelmach.2010.5608036.
Повний текст джерелаPan, Min-Chun, and Po-Ching Li. "Remote online machine fault diagnostic system." In NDE for Health Monitoring and Diagnostics, edited by Tribikram Kundu. SPIE, 2004. http://dx.doi.org/10.1117/12.537722.
Повний текст джерелаSingh, Ajay, Anand Shukla, and Suryansh Purwar. "Leveraging Machine Learning and Interactive Voice Interface for Automated Production Monitoring and Diagnostic." In SPE Annual Technical Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/210475-ms.
Повний текст джерелаXue-Nong Zhang. "Formal analysis of diagnostic notions." In 2012 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2012. http://dx.doi.org/10.1109/icmlc.2012.6359553.
Повний текст джерелаHu, Nian-Ze, Chih-Hui Simon Su, Cihun-Siyong Alex Gong, Cheng-Jung Lee, Yong-Sheng Chen, Ching-Hsiang Yang, Ching-Ying Yeh, Zheng-Han Shi, and Jieh-Tsyr Chuang. "Machine learning approach for robot diagnostic system." In 2019 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE). IEEE, 2019. http://dx.doi.org/10.1109/ecice47484.2019.8942793.
Повний текст джерелаQureshi, Fayyaz Karim, and Abdelhady A. Hady Mohamed. "Advanced Analytics and Diagnostic Rules Automatically Notify Operators About Developing Failures in Rotating and Reciprocating Machines." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211244-ms.
Повний текст джерелаLeung, Jacko T., and Peter W. Tse. "Smart Asset Maintenance System for Machine Fault Diagnosis: Its Effectiveness, Methodology, and Applications." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84300.
Повний текст джерелаLiu, Yongbin, Ruqiang Yan, and Robert X. Gao. "A Nonlinear Time Series Analysis Method for Health Monitoring of Rolling Bearings." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4118.
Повний текст джерелаMasalimov, Kamil Adipovich. "A machine learning based approach to autogenerate diagnostic models for CNC machines." In ASE '20: 35th IEEE/ACM International Conference on Automated Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3324884.3418915.
Повний текст джерелаЗвіти організацій з теми "Machine Diagnostic"
Liu, Xiaopei, Dan Liu, and Cong’e Tan. Gut microbiome-based machine learning for diagnostic prediction of liver fibrosis and cirrhosis: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, May 2022. http://dx.doi.org/10.37766/inplasy2022.5.0133.
Повний текст джерелаBruckner, Daniel. ML-o-Scope: A Diagnostic Visualization System for Deep Machine Learning Pipelines. Fort Belvoir, VA: Defense Technical Information Center, May 2014. http://dx.doi.org/10.21236/ada605112.
Повний текст джерелаXie, Bin. DiagSoftfailure: Automated Soft-Failure Diagnostic Tool Using Machine Learning for Network Users. Office of Scientific and Technical Information (OSTI), November 2019. http://dx.doi.org/10.2172/1575995.
Повний текст джерелаGuo, Longfei, Zhilei Cui, Jing Huang, Loh Wei Ping, and Shazlin Shaharudin. Applications of machine learning to the predictive and diagnostic capabilities of ACL injuries in athletes: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0045.
Повний текст джерелаSAINI, RAVINDER, AbdulKhaliq Alshadid, and Lujain Aldosari. Investigation on the application of artificial intelligence in prosthodontics. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, December 2022. http://dx.doi.org/10.37766/inplasy2022.12.0096.
Повний текст джерелаAlharbi, Shuaa S., and Haifa F. Alhasson. Toward the Identification of Applications of Artificial Intelligence for Dental Image Detection: Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0023.
Повний текст джерелаReifman, J., G. E. Graham, T. Y. C. Wei, K. R. Brown, and R. Y. Chin. Flexible human machine interface for process diagnostics. Office of Scientific and Technical Information (OSTI), May 1996. http://dx.doi.org/10.2172/224751.
Повний текст джерелаEhiabhi, Jolly, and Haifeng Wang. A Systematic Review of Machine Learning Models in Mental Health Analysis Based on Multi-Channel Multi-Modal Biometric Signals. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, February 2023. http://dx.doi.org/10.37766/inplasy2023.2.0003.
Повний текст джерелаHoward, Marylesa. Health Assessment and Performance Monitoring of Large Machine Diagnostics. Office of Scientific and Technical Information (OSTI), July 2022. http://dx.doi.org/10.2172/1877017.
Повний текст джерелаEdmonds, P. H., S. S. Medley, and K. M. Young. TPX diagnostics for tokamak operation, plasma control and machine protection. Office of Scientific and Technical Information (OSTI), August 1995. http://dx.doi.org/10.2172/100240.
Повний текст джерела