Готові списки джерел за темами / Automative diagnosis

Добірка наукової літератури з теми "Automative diagnosis"

Автор: Grafiati

Опубліковано: 8 червня 2024

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Статті в журналах з теми "Automative diagnosis":

Gaffet, Alexandre, Pauline Ribot, Elodie Chanthery, Nathalie Barbosa Roa, and Christophe Merle. "Data-Driven Capability-based Health Monitoring Method for Automative Manufacturing." PHM Society European Conference 6, no. 1 (June 29, 2021): 12. http://dx.doi.org/10.36001/phme.2021.v6i1.2811.

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Testing equipments are a crucial part of production quality control in the automotive industry. Their health needs to be controlled carefully to avoid quality issues and false alarms that reduce production efficiency, potentially leading to huge losses. The main challenge for this control is the large number of features leaning for automated reasoning. A data-based Health Monitoring System could be a solution. In manufacturing industries, a widely accepted index for evaluating process performance is the capability. It combines statistical measures for normal distributions in order to verify the ability of a process to produce an output within the specification limits. In this article we propose a capability-based prognosis and diagnosis method based on test data. Capability is calculated and compared to a known threshold. If the index value exceeds the threshold, then a diagnosis phase is initiated to find out which parts of the equipment are faulty. Data temporality is also taken into account. Data trends are used for prognosis.Test data are splited into periods. To respect the normality assumption of the capability, it is proposed to use a Gaussian Mixture Model (GMM) classification to extract all normal distributions found in one data period. Two approaches are discussed for selecting the number of clusters used for the classification. The first approach is based on the well-known Bayesian Information Criterion (BIC). The second approach uses a multi-criteria aggregation function learned by using machine learning on a synthetically gene-rated dataset. Some of the criteria used in the aggregation are inference based. Others are classical statistics extracted from the classes obtained by the GMM.For each of these classes the capability index is calculated and used for diagnosis and prognosis purposes. This method is applied on real data from In-Circuit Testing (ICT) machines for electronic components at a Vitesco factory in France.

Beyaz, Salih, Şahika Betül Yaylı, and Uğur Doktur. "Derin öğrenme ile otomatik kalça kırığı tanısı." TOTBİD Dergisi 22, no. 1 (January 1, 2022): 32–39. http://dx.doi.org/10.5578/totbid.dergisi.2022.07.

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Chen, Junying, Dongfang Li, Qingcai Chen, Wenxiu Zhou, and Xin Liu. "Diaformer: Automatic Diagnosis via Symptoms Sequence Generation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4432–40. http://dx.doi.org/10.1609/aaai.v36i4.20365.

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Automatic diagnosis has attracted increasing attention but remains challenging due to multi-step reasoning. Recent works usually address it by reinforcement learning methods. However, these methods show low efficiency and require task-specific reward functions. Considering the conversation between doctor and patient allows doctors to probe for symptoms and make diagnoses, the diagnosis process can be naturally seen as the generation of a sequence including symptoms and diagnoses. Inspired by this, we reformulate automatic diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but effective automatic Diagnosis model based on Transformer (Diaformer). We firstly design the symptom attention framework to learn the generation of symptom inquiry and the disease diagnosis. To alleviate the discrepancy between sequential generation and disorder of implicit symptoms, we further design three orderless training mechanisms. Experiments on three public datasets show that our model outperforms baselines on disease diagnosis by 1%, 6% and 11.5% with the highest training efficiency. Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.

Wang, Xiaoyu, Stephen D. J. McArthur, Scott M. Strachan, John D. Kirkwood, and Bruce Paisley. "A Data Analytic Approach to Automatic Fault Diagnosis and Prognosis for Distribution Automation." IEEE Transactions on Smart Grid 9, no. 6 (November 2018): 6265–73. http://dx.doi.org/10.1109/tsg.2017.2707107.

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Iliyasu, Abdullah M., Chastine Fatichah, and Khaled A. Abuhasel. "Evidence Accumulation Clustering with Possibilitic Fuzzy C-Means base clustering approach to disease diagnosis." Automatika 57, no. 3 (January 2016): 822–35. http://dx.doi.org/10.7305/automatika.2016.10.1427.

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Mabrek, Abdelhakim, and Kamel E. Hemsas. "Induction motor inter-turn fault modeling and simulation using SSFR test for diagnosis purpose." Automatika 57, no. 4 (October 2016): 948–59. http://dx.doi.org/10.7305/automatika.2017.10.1805.

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Urrutia Iturritza, Miren, Phuthumani Mlotshwa, Jesper Gantelius, Tobias Alfvén, Edmund Loh, Jens Karlsson, Chris Hadjineophytou, et al. "An Automated Versatile Diagnostic Workflow for Infectious Disease Detection in Low-Resource Settings." Micromachines 15, no. 6 (May 28, 2024): 708. http://dx.doi.org/10.3390/mi15060708.

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Laboratory automation effectively increases the throughput in sample analysis, reduces human errors in sample processing, as well as simplifies and accelerates the overall logistics. Automating diagnostic testing workflows in peripheral laboratories and also in near-patient settings -like hospitals, clinics and epidemic control checkpoints- is advantageous for the simultaneous processing of multiple samples to provide rapid results to patients, minimize the possibility of contamination or error during sample handling or transport, and increase efficiency. However, most automation platforms are expensive and are not easily adaptable to new protocols. Here, we address the need for a versatile, easy-to-use, rapid and reliable diagnostic testing workflow by combining open-source modular automation (Opentrons) and automation-compatible molecular biology protocols, easily adaptable to a workflow for infectious diseases diagnosis by detection on paper-based diagnostics. We demonstrated the feasibility of automation of the method with a low-cost Neisseria meningitidis diagnostic test that utilizes magnetic beads for pathogen DNA isolation, isothermal amplification, and detection on a paper-based microarray. In summary, we integrated open-source modular automation with adaptable molecular biology protocols, which was also faster and cheaper to perform in an automated than in a manual way. This enables a versatile diagnostic workflow for infectious diseases and we demonstrated this through a low-cost N. meningitidis test on paper-based microarrays.

Mohammed, Mehmood Ali, Murtuza Ali Mohammed, and Vazeer Ali Mohammed. "Impact of Artificial Intelligence on the Automation of Digital Health System." International Journal of Software Engineering & Applications 13, no. 6 (November 30, 2022): 23–29. http://dx.doi.org/10.5121/ijsea.2022.13602.

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Automating digital systems in healthcare plays a significant role in transforming the quality-of-care services delivered to patients across the board. This role is anticipated to be accomplished by the development and implementation of artificial intelligence in healthcare which has the potential to impact the provision of healthcare services. This paper sought to investigate the impact of adopting and implementing artificial intelligence on the automation of digital health systems within the different levels of healthcare. The general objective of the research study was to investigate the impact of artificial intelligence in the automation of digital health systems. The specific goals were to understand the concept of artificial intelligence and how it automates digital strategies, to determine the AI systems that have been developed and implemented in the healthcare systems, to establish the factors that influence the adoption of AI in healthcare, and to find out the outcomes of implementing AI in digital health systems. The research employed the descriptive research design. The study population included healthcare workers, policymakers, IT specialists, and management teams in the healthcare sector in the State of Kentucky. The sampling technique for the study was the purposive sampling technique. The study collected data using semi-structured interviews administered through Google Teams and Zoom. Data analysis was analyzed using the computer-assisted software for analyzing qualitative data, NVivo. The findings were that AI as a technological concept has the potential to impact the automation of digital health systems and is key to automating health services such as the diagnosis and treatment of illnesses and management of claims and payments. The study recommended that policy supports the application of artificial intelligence in healthcare, thus enabling the automation of several healthcare services and thus improving the delivery of care.

Hoesterey, Steffen, and Linda Onnasch. "Operators over-rely even more when automated decision support is the exception and not the norm." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1070–74. http://dx.doi.org/10.1177/1071181322661502.

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Previous findings indicate that operator’s reliance towards a static automated aid increases with the degree of automation (DOA) especially when decision-making is affected. In this data reexamination of a previously conducted study, operators’ automation verification was investigated comparing a static automation supporting decision selection with an automation which in most trials only narrowed down possible diagnoses. Thus, in the majority of trials information sampling was essential for task completion in the latter condition. However, in a few trials the automation provided a diagnosis, too – giving participants the rare opportunity to fully rely on the automation. The question was investigated how participants behave in the exceptional occasions in which reliance is possible compared to participants who always have the opportunity to rely. Results show that when reliance was possible as an exception, participants verified their aid significantly less compared to the group who could rely throughout all trials. Implications for approaches of flexible automation are discussed.

Palla, Gabriella, Claudio Ughi, Graziano Cesaretti, Alessandro Ventura, and Giuseppe Maggiore. "“Automatic” diagnosis ofviral enteritis." Journal of Pediatrics 130, no. 6 (June 1997): 1013. http://dx.doi.org/10.1016/s0022-3476(97)70302-0.

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Дисертації з теми "Automative diagnosis":

Sangha, Mahavir Singh. "Intelligent fault diagnosis for automative engines and real data evaluation." Thesis, Liverpool John Moores University, 2008. http://researchonline.ljmu.ac.uk/5867/.

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Wang, Xiaoyu. "A data analytic approach to automatic fault diagnosis and prognosis for distribution automation." Thesis, University of Strathclyde, 2017. http://digitool.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=28772.

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Distribution Automation (DA) is deployed to reduce outages and to rapidly reconnect customers following network faults. Recent developments in DA equipment have enabled the logging of load and fault event data, referred to as pick-up activity. This pick-up activity provides a picture of the underlying circuit activity occurring between successive DA operations over a period of time and has the potential to be accessed remotely for off-line or on-line analysis. The application of data analytics and automated analysis of this data supports reactive fault management and post fault investigation into anomalous network behavior. It also supports predictive capabilities that identify when potential network faults are evolving and offers the opportunity to take action in advance in order to mitigate any outages. This thesis details the design of a novel decision support system to achieve automatic fault diagnosis and prognosis for DA schemes. It combines detailed data from a specific DA device with SCADA data, by utilising rule-based, data science techniques (e.g. data mining and clustering techniques) to deliver the diagnostic and prognostic functions. These are applied to 11kV distribution network data captured from Pole Mounted Auto-Reclosers (PMARs) as provided by a leading UK network operator. This novel automated analysis system diagnoses the condition of device faults, the nature of a circuit's previous fault activity, identifies underlying anomalous circuit activity, and highlights indications of problematic events gradually evolving into a full scale circuit fault using prognostic functionality. The novel contributions also include the characterisation and identification of semi-permanent faults and a re-usable methodology and approach for applying data analytics to any DA device data sets in order to provide diagnostic decisions and mitigate potential fault scenarios.

Mourot, Gilles. "Contribution au diagnostic des systèmes industriels par reconnaissance des formes." Vandoeuvre-les-Nancy, INPL, 1993. http://www.theses.fr/1993INPL026N.

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Le travail présenté est consacré au diagnostic des systemes industriels par reconnaissance des formes. Les principes du diagnostic par reconnaissance des formes sont présentés dans le chapitre I. La première partie décrit les deux grandes étapes d'un système de diagnostic par reconnaissance des formes, à savoir l'apprentissage du système de diagnostic et de son exploitation en ligne, en insistant sur le caractère adaptatif d'un tel système. Les deux parties suivantes sont consacrées à l'étude de la capacité de la reconnaissance des formes à résoudre le problème du diagnostic de systemes industriels. Pour cela, une revue des différentes méthodes est présentée en mettant l'accent sur les considérations théoriques et pratiques auxquelles un utilisateur est confronte. Les critères pris en compte sont les propriétés et les domaines d'application des méthodes de reconnaissance des formes, l'influence de la taille de l'ensemble d'apprentissage et du nombre de variables, ainsi que leurs robustesses. A ces critères s'ajoutent des contraintes supplémentaires liées à la complexité des méthodes et à leurs couts d'utilisation. Dans le deuxième chapitre, nous étudions le problème de la constitution de l'ensemble d'apprentissage dans le cas non supervise. Cette étape est cruciale pour les performances du système de diagnostic et elle est particulièrement difficile à résoudre dans ce contexte. Après avoir positionné le problème en présentant une revue des différentes procédures et méthodes utilisées, nous proposons une procédure d'apprentissage non supervisé pour pallier les principaux inconvénients des méthodes décrites. Cette procédure est basée principalement sur la classification floue de l'ensemble des données, sur la validation floue et l'interprétation de la classification floue obtenue. Une revue des différentes méthodes floues de classification et de validation est présentée ainsi qu'une étude théorique et pratique de leurs propriétés

Axvik, Linda. "Automatic Diagnosis of Breast Tumoursin Ultrasound Images." Thesis, KTH, Tillämpad fysik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233752.

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Deosthale, Eeshan Vijay. "Model-Based Fault Diagnosis of Automatic Transmissions." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1542631227815892.

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Zhong, Binglin. "Model building and machine fault diagnosis." Thesis, Cardiff University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340889.

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Wazaefi, Yanal. "Automatic diagnosis of melanoma from dermoscopic images of melanocytic tumors : Analytical and comparative approaches." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM4106.

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Le mélanome est la forme la plus grave de cancer de la peau. Cette thèse a contribué au développement de deux approches différentes pour le diagnostic assisté par ordinateur du mélanome : approche analytique et approche comparative.L'approche analytique imite le comportement du dermatologue en détectant les caractéristiques de malignité sur la base de méthodes analytiques populaires dans une première étape, et en combinant ces caractéristiques dans une deuxième étape. Nous avons étudié l’impacte d’un système du diagnostic automatique utilisant des images dermoscopique de lésions cutanées pigmentées sur le diagnostic de dermatologues. L'approche comparative, appelé concept du Vilain Petit Canard (VPC), suppose que les naevus chez le même patient ont tendance à partager certaines caractéristiques morphologiques ainsi que les dermatologues identifient quelques groupes de similarité. VPC est le naevus qui ne rentre dans aucune de ces groupes, susceptibles d'être mélanome
Melanoma is the most serious type of skin cancer. This thesis focused on the development of two different approaches for computer-aided diagnosis of melanoma: analytical approach and comparative approach. The analytical approach mimics the dermatologist’s behavior by first detecting malignancy features based on popular analytical methods, and in a second step, by combining these features. We investigated to what extent the melanoma diagnosis can be impacted by an automatic system using dermoscopic images of pigmented skin lesions. The comparative approach, called Ugly Duckling (UD) concept, assumes that nevi in the same patient tend to share some morphological features so that dermatologists identify a few similarity clusters. UD is the nevus that does not fit into any of those clusters, likely to be suspicious. The goal was to model the ability of dermatologists to build consistent clusters of pigmented skin lesions in patients

Ng, Hoi Sum. "Petri nets for fault diagnosis and distribution automation." Thesis, University of Strathclyde, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366530.

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Ribot, Pauline. "Vers l'intégration diagnostic/pronostic pour la maintenance des systèmes complexes." Phd thesis, Université Paul Sabatier - Toulouse III, 2009. http://tel.archives-ouvertes.fr/tel-00450835.

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L'efficacité de la maintenance des systèmes industriels est un enjeu économique majeur pour leur exploitation commerciale. Les principales difficultés et sources d'inefficacité résident dans le choix des actions de maintenance. Un mauvais choix peut mener à une maintenance non satisfaisante et un surcoût dû à l'indisponibilité du système. Cette thèse propose une architecture générique de supervision pour aider à la prise de décisions d'actions de maintenance pour un système complexe. Cette architecture intègre des capacités de diagnostic et de pronostic permettant de connaître l'état actuel et l'état futur du système. La fonction de diagnostic détermine les composants en faute à l'origine des défaillances. La fonction de pronostic calcule la durée avant la prochaine défaillance du système. Nous présentons un cadre de modélisation générique formel pour un système complexe qui capture l'ensemble des connaissances nécessaires aux fonctions de diagnostic et de pronostic. Il permet de caractériser un couplage diagnostic/pronostic original. Une fonction générique et adaptative de pronostic est définie à l'aide d'un modèle de Weibull afin d'évaluer de façon probabiliste la durée de vie résiduelle du système. Des critères de performance pour l'architecture de supervision proposée reposant sur des propriétés du diagnostic et du pronostic sont caractérisés. Une méthodologie de retour sur conception est proposée dans le but d'assurer la performance de la fonction de diagnostic en garantissant la diagnosticabilité du système. L'application de ce travail de recherche aux systèmes aéronautiques s'inscrit dans le cadre du projet ARCHISTIC en collaboration avec Airbus et l'ENIT.

Li, Li. "Model-based automatic performance diagnosis of parallel computations /." view abstract or download file of text, 2007. http://proquest.umi.com/pqdweb?did=1335366371&sid=1&Fmt=2&clientId=11238&RQT=309&VName=PQD.

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Thesis (Ph. D.)--University of Oregon, 2007.
Typescript. Includes vita and abstract. Includes bibliographical references (leaves 119-123). Also available for download via the World Wide Web; free to University of Oregon users.

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Книги з теми "Automative diagnosis":

Kumar, Arun. Easy Oracle automation: Oracle 10g automatic storage, memory and diagnostic features. [Kittrell, N.C.]: Rampant TechPress, 2004.

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G, Maher Kevin M., Gordon Jacques, and Chilton Book Company, eds. Chilton's automatic transmission/transaxle diagnosis and repair. West Chester, PA: W.G. Nichols, 1998.

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Luca, Ferrarini, and Veber Carlo, eds. Modeling, control, simulation, and diagnosis of complex industrial and energy systems. Research Triangle Park, NC: Instrumentation Systems, and Automation Society, 2009.

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Yang, Bo-Suk. Introduction to intelligent machine fault diagnosis and prognosis. New York: Nova Science Publishers, 2009.

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Inc, Medical Data International, ed. Clinical diagnostics instrumentation and automation market. Irvine, Calif: MDI, 1995.

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Simpson, William Randolph. System test and diagnosis. Boston: Kluwer Academic, 1994.

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Claes, Lundsteen, Piper J. 1948-, Workshop on the Automation of Cytogenetics., and Commission of the European Communities., eds. Automation of cytogenetics. Berlin: Springer-Verlag, 1989.

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Brejcha, Mathias F. Automatic transmissions and transaxles: Theory, operation, diagnosis and service. 4th ed. Albany: Delmar Publishers, 1997.

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Langstrom, Carla Tedd. Methods & instrumentation for medical automation. Washington, D.C: Abbe Publishers Association, 1985.

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Keravnou, E. T. Competent expert systems: Acase study in fault diagnosis. New York: MacGraw-Hill, 1986.

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Частини книг з теми "Automative diagnosis":

Parsa, Saeed. "Fault Localization Tools: Diagnosis Matrix and Slicing." In Software Testing Automation, 333–64. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-22057-9_8.

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Arabi, Punal M., T. S. Naveen, N. Vamsha Deepa, and Deepak Samanta. "Automatic Diagnosis of Dental Diseases." In Communications in Computer and Information Science, 363–75. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8657-1_28.

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Ilonen, J., P. Paalanen, J. K. Kamarainen, T. Lindh, J. Ahola, H. Kälviäinen, and J. Partanen. "Toward Automatic Motor Condition Diagnosis." In Image Analysis, 970–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11499145_98.

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Chaurasiya, Sudhir Kumar, Nitish Kumar, Satya Bhushan Verma, Anshita Raj, and Shobhit Sinha. "A review on lung disease diagnosis using machine learning." In Automation and Computation, 153–60. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003333500-18.

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Husain, O. A. N. "Automation in Cytology." In Current Status of Diagnostic Cytology, 91–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84951-0_8.

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Lyu, Xi, Manrong She, Dan Pan, Xiaojun Wu, Kejin Chen, and Zhizhong Li. "Fault Diagnosis: Human Performance in the Digital and Automation Context." In Human-Automation Interaction, 265–88. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10780-1_14.

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Durand, B., L. Bouget, and S. Bouget. "Measurement Automation and Diagnosis in Spinning." In Mechatronic Design in Textile Engineering, 107–31. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0225-4_9.

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Kabitzsch, Klaus, and Volodymyr Vasyutynskyy. "Tele-Diagnosis at Networked Automation Systems." In Fieldbus Technology, 209–14. Vienna: Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-6421-1_28.

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Morales, Matías A., Rosa L. Figueroa, and Jael E. Cabrera. "Automatic Search of Nursing Diagnoses." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 607–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25085-9_72.

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Jelali, Mohieddine. "Diagnosis of Stiction-Related Actuator Problems." In Control Performance Management in Industrial Automation, 265–300. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4546-2_11.

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Тези доповідей конференцій з теми "Automative diagnosis":

Turner, Cameron J. "Diagnosis via NURBs Metamodel." In ASME 2010 International Mechanical Engineering Congress and Exposition. ASMEDC, 2010. http://dx.doi.org/10.1115/imece2010-38323.

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In vivo tissue condition diagnosis is a challenging engineering problem. The goal is to develop a technology that can eliminate tissue removal and external examination and enable less invasive surgical techniques to be used with a precision provided by a knowledge of the tissue within the body. Particularly challenging is the task of automating the diagnosis of the tissue condition. In this work, a metamodeling technique based on Non-Uniform Rational B-splines is used to analyze and automate the diagnosis of human tissue conditions. The resulting diagnoses are compared to results from medical doctors and the challenges in such data analysis are discussed. The technique has implications for both biomedical and electromechical system fault diagnosis and diagnostics.

Wu, Zhenhua, and Sheng-Jen Hsieh. "Design and Validation of Fault Diagnoser Based on Finite State Automaton and Sequential Function Chart for PLC Based Manufacturing System." In ASME/ISCIE 2012 International Symposium on Flexible Automation. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/isfa2012-7159.

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In ISFA-2010, we proposed a fault diagnoser design based on finite state automaton and sequential function chart to diagnose programmable logic controller (PLC) based manufacturing systems. A deficiency for that paper is that it only laid down a theoretical framework but lacking a detailed implementation and experiment validation. This paper aims at remedying the deficiency with detailed system design to diagnose discrete event system (DES) faults. Systematic diagnosis approach including modeling the plant, mapping output states with sensor readings, and designing diagnoser, was illustrated in this paper. The proposed diagnoser was implemented using Visual Basic to diagnose typical faults a PLC controlled dual robot arm. Experiment validation illustrated that the proposed diagnoser can detect and isolate DES equipment faults with a high fault detection rate of 93%, and detection rate of 80% when including product tolerance fault. The proposed diagnoser can achieve maximum fault detection delay of 9 seconds on the equipment faults. For the future direction, we are planning to extend the proposed diagnoser design to detect probabilistic faults in PLC based automated systems.

DePold, Hans R., and F. Douglas Gass. "The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics." In ASME 1998 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/98-gt-101.

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Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools. Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations. This paper presents recent developments in technology and strategies in engine condition monitoring including: 1) application of statistical analysis and artificial neural network filters to improve data quality; 2) neural networks for trend change detection, and classification to diagnose performance change; and 3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.

Wang, Cen, Noboru Yoshikane, Daniel Elson, and Takehiro Tsuritani. "Automation of Fast Configuration Error Diagnosis in Optical Transport Networks – Natural Language Processing is All You Need." In Optical Fiber Communication Conference. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ofc.2023.m3g.6.

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We train language models to automate the diagnosis of OTN configuration errors, and the diagnostic accuracy is up to 97.56%. We additionally demonstrate the effectiveness of the models on a real OTN system.

Chianese, Giovanni, Pasquale Franciosa, Jonas Nolte, Darek Ceglarek, and Stanislao Patalano. "Photodiode-Based In-Process Monitoring of Part-to-Part Gap and Weld Penetration Depth in Remote Laser Welding of Copper-to-Steel Battery Tab Connectors." In ASME 2021 16th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/msec2021-63321.

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Abstract This paper addresses in-process monitoring of part-to-part gap and weld penetration depth using photodiode-based signals during Remote Laser Welding (RLW) of battery tab connectors. Photodiode-based monitoring has been largely implemented for structural welds due to its relatively low cost and ease of automation. However, the application of photodiode-based monitoring to RLW of thin foils of dissimilar metals for battery tab connectors remains an unexplored area of research and will be addressed in this paper. Motivated by the high variability during the welding process of thin foils of dissimilar metals, this paper aims to evaluate the photodiode-based signals to determine if variations in weld quality can be isolated and diagnosed. The main focus is in diagnosing defective weld conditions caused by part-to-part gap variations and/or excessive weld penetration depth. Photodiode-based signals have been collected during RLW of copper-to-steel thin foils lap joint (Ni-plated copper 300 μm to Ni-plated steel 300 μm). The methodology is based on the evaluation of the energy intensity and scatter level of the signals. The energy intensity gives information about the amount of radiation emitted during the welding process, and the scatter level is associated to the accumulated and un-controlled variations. Findings indicated that part-to-part gap variations can be diagnosed by observing the step-change in the plasma signal, with no significant contribution given by the back-reflection. Results further suggested that over-penetration corresponds to significant increment of the scatter level in the sensor signals. Opportunities for automatic isolation and diagnosis of defective welds based on supervised machine learning will be discussed throughout the paper.

Yuan, Quan, Jun Chen, Chao Lu, and Haifeng Huang. "The Graph-based Mutual Attentive Network for Automatic Diagnosis." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/469.

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The automatic diagnosis has been suffering from the problem of inadequate reliable corpus to train a trustworthy predictive model. Besides, most of the previous deep learning based diagnosis models adopt the sequence learning techniques (CNN or RNN), which is difficult to extract the complex structural information, e.g. graph structure, between the critical medical entities. In this paper, we propose to build the diagnosis model based on the high-standard EMR documents from real hospitals to improve the accuracy and the credibility of the resulting model. Meanwhile, we introduce the Graph Convolutional Network into the model that alleviates the sparse feature problem and facilitates the extraction of structural information for diagnosis. Moreover, we propose the mutual attentive network to enhance the representation of inputs towards the better model performance. The evaluation conducted on the real EMR documents demonstrates that the proposed model is more accurate compared to the previous sequence learning based diagnosis models. The proposed model has been integrated into the information systems in over hundreds of primary health care facilities in China to assist physicians in the diagnostic process.

Chen, Xiyang, Kewei Zhang, and Yucheng Peng. "Research on Multi Diagnosis Methods for Hydro-Generator Sets." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58163.

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Hydro-Generator Sets Condition Monitoring and Predictive Maintenance activity has increased dramatically over the past few years. The Fault Diagnostic System is the key technique for the Predictive Maintenance. This paper discusses the Fault Diagnostic System function structure, system design and inferential strategy of Multi-Fault Diagnostic System of the Hydro-Generator Sets. The developing of the power system to big unit requires the higher automation and reliability of the power station. Electrical power systems are constantly exposed to faults and disturbances. This may lead to damage or it may pose a threat to reliable power system operation if a faulty cannot be quickly isolated from the system operation. In accordance with diversity and complexity of Hydro-Generator Sets faults, this paper brings forward a type of fault diagnosis method based on Multi-Diagnosis methods. The Multi-Diagnosis system is constituted of two Sub-Diagnosis systems: one is On-Line Sub-Diagnosis system that based on Bayesian Network (BN) just for the modeling with Bayesian Network has been a powerful tool to solve many uncertainty problems and also with the ability of predicting the future diagnosis; the other is Off-Line Sub-Diagnosis System that based on Model of a hydraulic Turbine-Generator Rotor-Bearing. In order to apply the Bayesian Network model to the engineering fields, we have to solve the problem of constructing the Bayesian Network. Then it suggests a method of constructing Bayesian Network based on the Fault Trees that widely used by the engineers. Base on the construction method, we will construct the Bayesian Network quickly, and Bayesian Network is more suitable for Hydro-Generator Sets fault diagnosis. In accordance with the On-Line Diagnosis Sub-System, it adopts Case-Based Reasoning to make the decision of final diagnosis result or further diagnosis. However, the method mentioned above is limited because of its bottleneck of the knowledge acquisition. The model strategy of the Rotor-Bearing system of Hydro-Generator is discussed and a multi-degree-freedom nonlinear model is developed. It proposes the simulation in accordance with the three fields such as: waterpower, electric and machine. Mechanical, electrical and hydraulic forces acting on rotor externally can be taken into account during the model calculating process. The transient responses of the system are calculated by combined used the transfer matrix method. This paper brings forward a prototype of Hydro-Generator Sets Fault Diagnostic System in order to make a more efficient fault diagnostic decision.

Tamilselvan, Prasanna, and Pingfeng Wang. "A Hybrid Inference Approach for Health Diagnostics With Unexampled Faulty States." In ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/detc2012-70806.

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System health diagnostics provides diversified benefits such as improved safety, improved reliability and reduced costs for the operation and maintenance of engineered systems. Successful health diagnostics requires the knowledge of system failures. However, with an increasing complexity it is extraordinarily difficult to have a well-tested system so that all potential faulty states can be realized and studied at product testing stage. Thus, real time health diagnostics requires automatic detection of unexampled faulty states through the sensory signals to avoid sudden catastrophic system failures. This paper presents a hybrid inference approach (HIA) for structural health diagnosis with unexampled faulty states, which employs a two-fold inference process comprising of preliminary statistical learning based anomaly detection and artificial intelligence based health state classification for real time condition monitoring. The HIA is able to identify and isolate the unexampled faulty states through interactively detecting the deviation of sensory data from the known health states and forming new health states autonomously. The proposed approach takes the advantages of both statistical approaches and artificial intelligence based techniques and integrates them together in a unified diagnosis framework. The performance of proposed HIA is demonstrated with a power transformer and roller bearing health diagnosis case studies, where Mahalanobis distance serves as a representative statistical inference approach.

Zhao, Yue, Francesco Di Maio, Enrico Zio, Qin Zhang, and Chunling Dong. "Genetic Algorithm Optimization of a Dynamic Uncertain Causality Graph (DUCG) for Fault Diagnosis in Nuclear Power Plants." In 2016 24th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/icone24-60199.

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Fault diagnostics is important for the safe operation of Nuclear Power Plants (NPPs). In recent years, data-driven approaches like neural networks, fuzzy and neuro-fuzzy approaches, support vector machine, K-nearest neighbors classifiers and inference methodologies, have been proposed and implemented to tackle the problem. Among these methodologies, Dynamic Uncertain Causality Graph (DUCG) has been proved effective in many practical cases. However, the causal graph construction behind the DUCG is complicated and, in many cases, redundant on the symptoms needed to correctly classify the fault. In this paper, we propose a method to simplify causal graph construction in an automatic way. The method consists in transforming the expert knowledge-based DCUG into a Fuzzy Decision Tree (FDT) by extracting from the DUCG a Fuzzy Rule Base (FRB). Genetic algorithm (GA) is, then, used for the optimization of the FDT, by performing a wrapper search around the FDT: the set of symptoms selected during the iterative search are taken as the best set of symptoms for the diagnosis of the faults that can occur in the system. The effectiveness of the approach is shown with respect to a DUCG model initially built to diagnose 23 faults originally using 262 symptoms of Unit-1 in the Ningde NPP of the China Guangdong Nuclear Power Corporation (CGNPC). Final results show that the FDT whose symptoms and diagnosis strategy has been optimized by GA, can drive the construction of DUCG and lower the computational burden without loss of accuracy in diagnosis.

Nakajima, Keyaki, Eiichirou Tanaka, Kazunari Okabe, Hitoshi Takebe, Kazuteru Nagamura, Kiyotaka Ikejo, Shinji Hashimura, Keiichi Muramatsu, Keiichi Watanuki, and Ryozo Nemoto. "Development of the Easy Set-Up and In Situ Automatic Gear Diagnostic System Using a Laser Beam." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47252.

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We developed a method which can diagnose damage on a gear tooth surface by using laser beam without a rotary encoder. This method is as follows: 1) The tooth bottom, the tooth tip and their two medians are detected by the differentials of the laser reflection data. 2) The gear rotation speed is calculated with these four positions, and interpolated according to the rotation fluctuation. 3) By using the calculated gear rotation speed, the measured data can be converted corresponding to the gear rotation angle. Thus we diagnose gear tooth surface damage without being influenced by rotational fluctuation. We did diagnosis experiments and we made contour maps show diagnosis accuracy. From these maps, we got the following conclusions: 1) The accuracy of damage diagnosis is the same level regardless of the presence or absence of a rotary encoder. 2) The cycle of rotational fluctuation hardly affects the accuracy. 3) Bigger fluctuation amplitude makes the range accuracy worse, however the position accuracy improves.

Звіти організацій з теми "Automative diagnosis":

Beshouri. PR-309-11202-R01 Field Demonstration Test of Advanced Engine and Compressor Diagnostics for CORE. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2013. http://dx.doi.org/10.55274/r0010569.

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Pipeline engines fitted with modem automation and control systems serve as ideal candidates for On Board Diagnostics (OBD) and Diagnostic Monitoring (DM). OBD is very effective for real time performance monitoring, pollution prevention and safety monitoring in the engine room. Diagnostic Monitoring (DM) extends the capabilities of OBD by using extensive historical data to characterize unit specific characteristics thereby dealing with engines as individuals. The work to date has focused on the development of a methodology to support real-time multiparameter analysis and crosscheck of engine data to quickly, accurately and precisely diagnose engine faults. The methodology uses a spreadsheet interface in combination with Mean Value Engine Models and an analytic table to perform the diagnosis. Field gathered data was then used Advanced Engine Technologies Corporation (AETC) then developed a playback simulator to replay data provided by PRCI members to test the methodology and confirm the ability to automatically detect engine and compressor faults.

Turtoi, Daria Claudia, Vlad Dumitru Brata, Abdulrahman Ismaiel, Dinu Iuliu Dumitrascu, Valentin Militaru, Mihai Alexandru Munteanu, Alexandru Botan, Dan Alexandru Toc, Traian Adrian Duse, and Stefan Lucian Popa. Artificial Intelligence for the Automatic Diagnosis of Gastritis: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, August 2023. http://dx.doi.org/10.37766/inplasy2023.8.0120.

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Butzbaugh, Joshua, Abraham SD Tidwell, and Chrissi Antonopoulos. Automatic Fault Detection & Diagnostics: Residential Market Analysis. Office of Scientific and Technical Information (OSTI), September 2020. http://dx.doi.org/10.2172/1670423.

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Bae, Yeonjin, Borui Cui, Jaewan Joe, Piljae Im, Veonica Adetola, Liang Zhang, Matt Leach, and Teja Kuruganti. Review: Sensor Impact on Building Controls and Automatic Fault Detection and Diagnosis (AFDD). Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1671427.

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Zhang, N. Automation and integration of polymerase chain reaction with capillary electrophoresis for high throughput genotyping and disease diagnosis. Office of Scientific and Technical Information (OSTI), February 1999. http://dx.doi.org/10.2172/348906.

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Popa, Stefan-Lucian, Bogdan Stancu, Abdulrahman Ismaiel, Daria Claudia Turtoi, Vlad Dumitru Brata, Traian Adrian Duse, Roxana Bolchis, et al. Enteroscopy Versus Video Capsule Endoscopy for Automatic Diagnosis of Small Bowel Disorders. A Comparative Analysis of Artificial Intelligence Applications. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, October 2023. http://dx.doi.org/10.37766/inplasy2023.10.0038.

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Dr. Carl Stern and Dr. Martin Lee. Automatic component calibration and error diagnostics for model-based accelerator control. Phase I final report. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/765670.

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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.

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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.

Paule, Bernard, Flourentzos Flourentzou, Tristan de KERCHOVE d’EXAERDE, Julien BOUTILLIER, and Nicolo Ferrari. PRELUDE Roadmap for Building Renovation: set of rules for renovation actions to optimize building energy performance. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541614638.

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In the context of climate change and the environmental and energy constraints we face, it is essential to develop methods to encourage the implementation of efficient solutions for building renovation. One of the objectives of the European PRELUDE project [1] is to develop a "Building Renovation Roadmap"(BRR) aimed at facilitating decision-making to foster the most efficient refurbishment actions, the implementation of innovative solutions and the promotion of renewable energy sources in the renovation process of existing buildings. In this context, Estia is working on the development of inference rules that will make it possible. On the basis of a diagnosis such as the Energy Performance Certificate, it will help establishing a list of priority actions. The dynamics that drive this project permit to decrease the subjectivity of a human decisions making scheme. While simulation generates digital technical data, interpretation requires the translation of this data into natural language. The purpose is to automate the translation of the results to provide advice and facilitate decision-making. In medicine, the diagnostic phase is a process by which a disease is identified by its symptoms. Similarly, the idea of the process is to target the faulty elements potentially responsible for poor performance and to propose remedial solutions. The system is based on the development of fuzzy logic rules [2],[3]. This choice was made to be able to manipulate notions of membership with truth levels between 0 and 1, and to deliver messages in a linguistic form, understandable by non-specialist users. For example, if performance is low and parameter x is unfavourable, the algorithm can gives an incentive to improve the parameter such as: "you COULD, SHOULD or MUST change parameter x". Regarding energy performance analysis, the following domains are addressed: heating, domestic hot water, cooling, lighting. Regarding the parameters, the analysis covers the following topics: Characteristics of the building envelope. and of the technical installations (heat production-distribution, ventilation system, electric lighting, etc.). This paper describes the methodology used, lists the fields studied and outlines the expected outcomes of the project.