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Journal articles on the topic 'Intelligent methods of diagnosis'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Wang, Huaqing, Peng Chen, and Shuming Wang. "Intelligent diagnosis methods for plant machinery." Frontiers of Mechanical Engineering in China 5, no. 1 (2009): 118–24. http://dx.doi.org/10.1007/s11465-009-0084-z.

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Zhu, Yong, Qingyi Wu, Shengnan Tang, Boo Cheong Khoo, and Zhengxi Chang. "Intelligent Fault Diagnosis Methods for Hydraulic Piston Pumps: A Review." Journal of Marine Science and Engineering 11, no. 8 (2023): 1609. http://dx.doi.org/10.3390/jmse11081609.

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As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high ef

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Wei-Fang Li, Wei-Fang Li, Hai-Xin You Wei-Fang Li, and Xiao-Yu Guo Hai-Xin You. "Intelligent Diagnosis Method for Orthopedic Diseases Based on Medical Images." 電腦學刊 34, no. 5 (2023): 205–12. http://dx.doi.org/10.53106/199115992023103405016.

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<p>This article proposes an orthopedic image recognition method based on an improved convolutional neural network to address the traditional diagnostic methods of orthopedic diseases and the diverse types of orthopedic diseases in the diagnostic process. This method uses a fixed convolutional number to extract key features of orthopedic diseases and reduce the number of features. The article takes the diagnosis of gout in bone diseases as an example and designs an evaluation method based on visualization technology and quantitative indicator calculation. The quantitative indicator calcul

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Lyfar, Volodymyr, Olena Lyfar, and Volodymyr Zynchenko. "METHODS OF INTELLIGENT DATA ANALYSIS USING NEURAL NETWORKS IN DIAGNOSIS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 2 (2024): 109–12. http://dx.doi.org/10.35784/iapgos.5746.

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The considered methods make it possible to develop the structure of diagnostic systems based on neural networks and implement decision support systems in classification diagnostic problems. The study uses general special methods of data mining and the principles of constructing an artificial intelligence system based on neural networks. The problems that arise when filling knowledge bases and training neural networks are highlighted. Methods for developing models of intelligent data processing for diagnostic purposes based on neural networks are proposed. The authors developed and verified an

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Arad, Fereshteh, Seyyed Mohammad Mousavi, Soodeh Hosseini, Maryam Amizade, and Ayyub Sheikhi. "Intelligent Diagnosis of Larynx Cancer Using Machine Learning Methods." Journal of Health and Biomedical Informatics 11, no. 2 (2024): 115–30. https://doi.org/10.34172/jhbmi.2024.18.

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Introduction: Larynx cancer can be benign or malignant based on various factors. This research aimed to provide a machine learning -based model to improve the diagnosis of individuals with larynx cancer . Method: In the first step, the voices of the people who visited the medical centers (including the sounds (A), (E), and (O)) were recorded and considered as a data set. In the second step, the data were classified into three classes (benign cancer, malignant cancer, and healthy) by a specialist. In the third step, the data cleaning was done. In the fourth step, the features related to sound w

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Shi, Rong Bo, Zhi Ping Guo, and Zhi Yong Song. "Research Based on State Monitoring of CNC Machine Tools Intelligent Security System." Applied Mechanics and Materials 427-429 (September 2013): 1328–32. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1328.

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Analyse the cause of fault in CNC Machine, research the corresponding solve scheme, and to realize the state can monitor equipment operation, improve equipment reliability, the development set of machine condition monitoring, fault warning, fault diagnosis and troubleshooting as one of the intelligent security system. Based on the CNC machine intelligence support system research, design, introduces the key technologies and methods. Screw lift state of motion monitoring, for example, trend analysis exercise state, intelligent fault diagnosis, in order to achieve protection of the intelligent CN

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Hu, Hao, and Ying Min Yan. "Fault Diagnosis Technology of Equipment System." Applied Mechanics and Materials 380-384 (August 2013): 1003–8. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1003.

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With the development of computer technology, the development of artificial intelligence technology, diagnostic techniques to change rapidly in the intelligent stage of development, this paper will mainly based on artificial intelligent fault diagnosis methods are described, mainly the application of BP network in fault diagnosis. And application principle to design a user-friendly display system. Make diagnosis data clearly show on the panel, and at the same time show the fault type and other necessary data. Then the bus data tracking, were analyzed. The system for a new system has complex lin

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Li, Zhi. "Overview of the faults and diagnosis methods of the hydraulic system of modern coal mining machines." E3S Web of Conferences 528 (2024): 02019. http://dx.doi.org/10.1051/e3sconf/202452802019.

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The high power, high efficiency, low failure rate, and intelligence of coal mining equipment are important directions for the development of coal mining machines both domestically and internationally. The implementation of efficient and low failure coal mine equipment is an important prerequisite for ensuring the smooth deployment of intelligent mines. At present, coal mining equipment generally consists of three parts: mechanical, electrical, and hydraulic. However, hydraulic pressure is a necessary system for coal mining equipment to complete high-power and intelligent operations. This artic

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9

Feng, Ying, and Yongqin Wang. "Intelligent Diagnostic System Development based on Artificial Intelligence Technology." Frontiers in Computing and Intelligent Systems 5, no. 3 (2023): 8–13. http://dx.doi.org/10.54097/fcis.v5i3.13814.

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Intelligent diagnosis is an important scenario in smart healthcare, with conversational diagnostic scenarios being the most common. The process of collecting symptom information through conversations with users and inferring diseases based on symptoms. Through a dialogue based diagnostic system, it can meet some of the medical consultation needs of residents, thereby freeing doctors from some basic consultations and greatly alleviating the shortage of medical resources. In the actual diagnosis process, the symptoms reported by patients are often insufficient to support accurate diagnosis. It i

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Huang, Xiaoge, Yiyi Zhang, Jiefeng Liu, Hanbo Zheng, and Ke Wang. "A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier." Polymers 10, no. 10 (2018): 1096. http://dx.doi.org/10.3390/polym10101096.

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Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the

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11

Chen, Yanjun, Min Zhou, Meizhou Zhang, and Meng Zha. "Knowledge-Graph-Driven Fault Diagnosis Methods for Intelligent Production Lines." Sensors 25, no. 13 (2025): 3912. https://doi.org/10.3390/s25133912.

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In order to enhance the management and application of fault knowledge within intelligent production lines, thereby increasing the efficiency of fault diagnosis and ensuring the stable and reliable operation of these systems, we propose a fault diagnosis methodology that leverages knowledge graphs. First, we designed an ontology model for fault knowledge by integrating textual features from various components of the production line with expert insights. Second, we employed the ALBERT–BiLSTM–Attention–CRF model to achieve named entity and relationship recognition for faults in intelligent produc

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Glumov, V. M., V. Yu Rutkovskii, and V. M. Sukhanov. "Methods of intelligent diagnosis for control of flexible moving craft." Automation and Remote Control 67, no. 12 (2006): 1863–77. http://dx.doi.org/10.1134/s0005117906120010.

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13

Tang, Shengnan, Shouqi Yuan, and Yong Zhu. "Deep Learning-Based Intelligent Fault Diagnosis Methods Toward Rotating Machinery." IEEE Access 8 (2020): 9335–46. http://dx.doi.org/10.1109/access.2019.2963092.

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14

Takács, Orsolya, and Annamária R. Várkonyi-Kóczy. "Anytime Soft Computing Methods for Intelligent Measurement, Diagnosis and Control." IFAC Proceedings Volumes 33, no. 28 (2000): 159–64. http://dx.doi.org/10.1016/s1474-6670(17)36827-1.

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15

Huang, Yo-Ping, Chao-Ying Huang, and Shen-Ing Liu. "Hybrid intelligent methods for arrhythmia detection and geriatric depression diagnosis." Applied Soft Computing 14 (January 2014): 38–46. http://dx.doi.org/10.1016/j.asoc.2013.09.021.

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Chen, Zhan Peng, Zhuo Wang, Li Min Jia, and Guo Qiang Cai. "Analysis and Comparison of Locomotive Traction Motor Intelligent Fault Diagnosis Methods." Applied Mechanics and Materials 97-98 (September 2011): 994–1002. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.994.

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Train operation safety is the most important and the most basic requirement. Locomotive traction motor is the train operation of traction power equipment, whose reliability relates directly to the train operation safety. And locomotive traction motor fault diagnosis is to ensure the reliability of the traction motor scooter important technique means. Through the locomotive pulling motor failure diagnosis method's research, the traction motor typical fault type has been summarized, the main intelligent diagnosis method principle has been narrated, the main principles of the intelligent diagnosi

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17

Kister, Klaudia, Jakub Laskowski, Magdalena Mazur, et al. "Intelligent Detection for Pancreatic Cancer Diagnosis: Future Directions." Journal of Education, Health and Sport 14, no. 1 (2023): 136–51. http://dx.doi.org/10.12775/jehs.2023.14.01.013.

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Introduction: Artificial intelligence is one of the most modern information systems that bases its operation on analogous functioning to the human mind. Particular hopes are placed in the treatment of diseases considered incurable. One such disease is pancreatic cancer, which has an extremely poor prognosis. Thanks to the use of machine learning and deep learning, artificial intelligence analyzes the available images taken during computed tomography and compares them with the introduced changes characteristic of pancreatic cancer. The advantage of the machines is the ability to analyze data, s

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18

Shi, Yadong, Hongda Hao, Rentong Liu, et al. "Drilling Overflow Diagnosis Based on the Fusion of Physical and Intelligent Algorithms." Processes 13, no. 2 (2025): 577. https://doi.org/10.3390/pr13020577.

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The diagnosis of overflow risk has always been an important area of research in drilling operations in the field of oil and gas engineering. In the face of the limitations and lag of traditional overflow diagnosis methods, the practical application effect of existing models and methods is not obvious, and there is no integration of the physical model and the intelligent algorithm model for overflow diagnosis, this paper proposes a method of adaptive weight fusion of physical model and intelligent algorithm model diagnosis results. Based on the fusion of the physical model and the intelligent a

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19

Li, Daoliang, Xin Li, Qi Wang, and Yinfeng Hao. "Advanced Techniques for the Intelligent Diagnosis of Fish Diseases: A Review." Animals 12, no. 21 (2022): 2938. http://dx.doi.org/10.3390/ani12212938.

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Aquatic products, as essential sources of protein, have attracted considerable concern by producers and consumers. Precise fish disease prevention and treatment may provide not only healthy fish protein but also ecological and economic benefits. However, unlike intelligent two-dimensional diagnoses of plants and crops, one of the most serious challenges confronted in intelligent aquaculture diagnosis is its three-dimensional space. Expert systems have been applied to diagnose fish diseases in recent decades, allowing for restricted diagnosis of certain aquaculture. However, this method needs a

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20

Yi-Hui Chen, Yi-Hui Chen. "A Computer-Aided Intelligent Fault Diagnosis Method for Axial Hydraulic Piston Pump." 電腦學刊 34, no. 2 (2023): 233–46. http://dx.doi.org/10.53106/199115992023043402018.

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<p>Axial hydraulic piston pump is widely used in industrial production due to its high pressure resistance and large displacement characteristics, but high pressure and large displacement are also the main causes of piston pump failure. Starting from the fault mechanism of the axial hydraulic piston pump, this paper analyzes and studies the signal characteristics of the fault, and establishes the fault signal acquisition and analysis model. Finally, it discusses the construction of the diagnosis system from both hardware and software, so that the processed typical fault signals can be se

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21

Wang, Zhifeng, Wenxing Yan, Chunyan Zeng, Yuan Tian, and Shi Dong. "A Unified Interpretable Intelligent Learning Diagnosis Framework for Learning Performance Prediction in Intelligent Tutoring Systems." International Journal of Intelligent Systems 2023 (February 20, 2023): 1–20. http://dx.doi.org/10.1155/2023/4468025.

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Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners’ current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric-based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large-scale learning data. While the deep learning-based

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22

Ma, Wenbo, and Hongjie Chen. "Application Scenarios and Forms of Artificial Intelligence in Physical Education." Advances in Education, Humanities and Social Science Research 9, no. 1 (2024): 13. http://dx.doi.org/10.56028/aehssr.9.1.13.2024.

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The application of artificial intelligence in physical education is analyzed. The manifestations of artificial intelligence in physical education are as follows: accurate diagnosis, process monitoring, personalized service and intelligent decision-making; The application forms are as follows: intelligent tutor system, automatic evaluation system and physical education robot. The prospect of artificial intelligence in physical education is put forward: facing modernization and building a learning society; Facing the world, changing the teaching concept; Facing the future, innovating physical ed

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23

Gao, Lixin, Zhiqiang Ren, Wenliang Tang, Huaqing Wang, and Peng Chen. "Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR." Sensors 10, no. 5 (2010): 4602–21. http://dx.doi.org/10.3390/s100504602.

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24

Wang, Lu, Gui You Lu, Dan Li, and Guo Bao Ding. "Research on Intelligent Fault Diagnosis Methods of Armored Vehicles Electrical System." Advanced Materials Research 753-755 (August 2013): 2175–78. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2175.

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For armored vehicles electrical system fault diagnosis of fault original data collection difficult situation, a new intelligent computing programs designed based on the fuzzy set theory and possibility distribution theory and fuzzy logic reasoning design, which realized the process of KA automatization through the combination of fault simulation technology and knowledge acquisition technology. The approach presented in this paper makes the work of knowledge acquisition (KA) engineer easier, and makes fast diagnosis fault location and fault reasons possible.

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25

Filbert, D. "Intelligent measurement methods in technical diagnosis and quality assurance - a comparison." Measurement 6, no. 2 (1988): 69–74. http://dx.doi.org/10.1016/0263-2241(88)90005-x.

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26

Bello, Opeyemi, Javier Holzmann, Tanveer Yaqoob, and Catalin Teodoriu. "Application Of Artificial Intelligence Methods In Drilling System Design And Operations: A Review Of The State Of The Art." Journal of Artificial Intelligence and Soft Computing Research 5, no. 2 (2015): 121–39. http://dx.doi.org/10.1515/jaiscr-2015-0024.

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AbstractArtificial Intelligence (AI) can be defined as the application of science and engineering with the intent of intelligent machine composition. It involves using tool based on intelligent behavior of humans in solving complex issues, designed in a way to make computers execute tasks that were earlier thought of human intelligence involvement. In comparison to other computational automations, AI facilitates and enables time reduction based on personnel needs and most importantly, the operational expenses.Artificial Intelligence (AI) is an area of great interest and significance in petrole

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Cao, Yunyu, Jinrui Tang, Shaohui Shi, Defu Cai, Li Zhang, and Ping Xiong. "Fault Diagnosis Techniques for Electrical Distribution Network Based on Artificial Intelligence and Signal Processing: A Review." Processes 13, no. 1 (2024): 48. https://doi.org/10.3390/pr13010048.

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This paper provides a comprehensive and systematic review of fault diagnosis methods based on artificial intelligence (AI) in smart distribution networks described in the literature. For the first time, it systematically combs through the main fault diagnosis objectives and corresponding fault diagnosis methods for a smart distribution network from the perspective of combined signal processing and artificial intelligence algorithms. The paper provides an in-depth analysis of the advantages and disadvantages of various signal processing techniques and intelligent algorithms in different fault d

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28

Sun, Zhe, and Zhangxing Chen. "Research Status and Development Direction of Formation Damage Prediction and Diagnosis Technologies." Applied Sciences 15, no. 3 (2025): 1169. https://doi.org/10.3390/app15031169.

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Formation damage remains a key challenge in oil and gas exploration and development, requiring effective prediction and diagnostic technologies to mitigate its impact. Despite decades of research, current techniques lack the accuracy and practicality demanded by modern oilfield operations and the future of intelligent oil and gas development. This study systematically reviews advancements in formation damage prediction and diagnostics, focusing on wellsite diagnosis, experimental methods, imaging techniques, analytical approaches, numerical modeling, and artificial intelligence applications. T

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Migal, Vasiliy, Shchasiana Arhun, Andrii Hnatov, Hanna Hnatova, and Pavlo Sokhin. "Intelligent diagnosics of vehicles." Vehicle and electronics. Innovative technologies, no. 22 (December 27, 2022): 72–80. http://dx.doi.org/10.30977/veit.2022.22.0.5.

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Problem. Diagnostics or troubleshooting is an integral part of the operation of automotive technology, and as automotive systems become more complex, the need for diagnostic skills increases, so diagnostic methods by the human senses should be considered an integral part of technical diagnostics at all stages of a vehicle life cycle. Methodology. Analytical methods are used to study the methods of diagnosing vehicles with the help of the intellectual abilities of the operator-diagnostician. Results. The paper shows that the intellectual abilities of the operator-diagnostician play an important

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Troshina, E. A., S. M. Zakharova, K. V. Tsyguleva, et al. "Application of artificial intelligence in ultrasound diagnostics of thyroid nodules." Clinical and experimental thyroidology 20, no. 1 (2024): 15–29. http://dx.doi.org/10.14341/ket12782.

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BACKGROUND: the use of artificial intelligence in ultrasound diagnosis of thyroid nodules is expected and quite promising. However, in order to understand this, it is necessary to see how a doctor works with its help, diagnosing diseases step by step, and how exactly this intelligence is implemented in practical healthcare. The current publication provides an overview of existing intelligent systems for supporting medical decisions in thyroidology, and describes in detail the capabilities of the Russian intelligent computer assistant for ultrasound diagnostics - a system for stratifying thyroi

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31

Alquran, Hiam, Yazan Al-Issa, Mohammed Alsalatie, Wan Azani Mustafa, Isam Abu Qasmieh, and Ala’a Zyout. "Intelligent Diagnosis and Classification of Keratitis." Diagnostics 12, no. 6 (2022): 1344. http://dx.doi.org/10.3390/diagnostics12061344.

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A corneal ulcer is an open sore that forms on the cornea; it is usually caused by an infection or injury and can result in ocular morbidity. Early detection and discrimination between different ulcer diseases reduces the chances of visual disability. Traditional clinical methods that use slit-lamp images can be tiresome, expensive, and time-consuming. Instead, this paper proposes a deep learning approach to diagnose corneal ulcers, enabling better, improved treatment. This paper suggests two modes to classify corneal images using manual and automatic deep learning feature extraction. Different

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32

Sun, Xiao Yan, Long Li, and Ping Ping Liu. "Research on Fault Diagnosis for Power Transmission Based on Mass Data Mining." Applied Mechanics and Materials 271-272 (December 2012): 1623–27. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1623.

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A Multi-Agent based transmission fault diagnosis system is researched in this paper. Many data digging analysis methods are employed, combined with data warehouse, OLAP and Multi-Agent technology. An intelligent decision supporting system for monitoring transmission network data is built. Data digging method is used to intelligently analyze and process fault data in the data warehouse, and Agent technology is used to realize data collection, pretreatment, inquiry, knowledge Automatic extraction, mining and other functions, which makes the whole mining process intellectual and intelligent. It a

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33

Liu, Jiali. "Diagnosis of Alzheimers Disease: Machine Learning and Deep Learning Approaches." Applied and Computational Engineering 166, no. 1 (2025): 1–5. https://doi.org/10.54254/2755-2721/2025.tj23770.

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Alzheimers disease (AD) is a progressive neurodegenerative disorder, and its exact causes and influencing factors remain unclear. Traditional diagnostic methods require substantial human effort, often lack sufficient accuracy, and are challenged by the subtlety of early symptoms, which can easily be misinterpreted as other age-related conditions such as senile depression. In recent years, the integration of machine learning (ML) and deep learning (DL) techniques has provided new possibilities for improving early diagnosis. This paper reviews the basic theory of AD, introduces diagnostic approa

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34

Sun, Bojun, Zixin Sheng, Peng Song, et al. "State-of-the-Art Detection and Diagnosis Methods for Rolling Bearing Defects: A Comprehensive Review." Applied Sciences 15, no. 2 (2025): 1001. https://doi.org/10.3390/app15021001.

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Rolling bearings are essential transmission and support components in aircraft engines, playing a critical role in ensuring their safe and stable operation. Rolling bearing faults have a significant impact and should not be ignored. The effective diagnosis of bearing faults has always been a critical requirement for ensuring reliable operation. With the increasing demands of modern manufacturing to reduce costs and improve quality, the development of advanced bearing fault detection methods has become indispensable. This paper presents the brief review of recent trends in research on bearing f

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Lv, Peng Liang, and Guo Shun Chen. "Multi-Agent Fault Diagnosis Methods Based on Information Fusion." Applied Mechanics and Materials 568-570 (June 2014): 141–45. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.141.

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In order to meet the current needs of complex technical equipment maintenance and support, the paper member with the structural characteristics of Multi-Agent System, which was introduced to the fault diagnosis method based on information fusion, the main research was distributed intelligent monitoring and diagnosis system framework based on information fusion, and analysis of information fusion method for the diagnosis of strategies for the a typical system feature, including the contents of the implementation of the method and research status, and points out its future research directions.

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Filatov, V. O., A. L. Yerokhin, O. V. Zolotukhin, and M. S. Kudryavtseva. "Methods of intellectual analysis of processes in medical information systems." Information extraction and processing 2020, no. 48 (2020): 92–98. http://dx.doi.org/10.15407/vidbir2020.48.092.

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Methods of data mining and intelligent analysis of processes are investigated for the develop¬ment of a mobile intelligent application “Emergency Medical Aid”, which effectively solves the problems of information support for medical purposes in a particular emergency situation for the user. With the help of Data Mining methods, a knowledge base for a personal assistant has been developed, which makes it possible to analyze indicators of a person’s condition and draw conclusions regarding the diagnosis in the field of emergency medicine. The knowledge base presented allows us to apply the infer

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Wang, Chong, Xinxing Chen, Xin Qiang, Haoran Fan, and Shaohua Li. "Recent advances in mechanism/data-driven fault diagnosis of complex engineering systems with uncertainties." AIMS Mathematics 9, no. 11 (2024): 29736–72. http://dx.doi.org/10.3934/math.20241441.

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<p>The relentless advancement of modern technology has given rise to increasingly intricate and sophisticated engineering systems, which in turn demand more reliable and intelligent fault diagnosis methods. This paper presents a comprehensive review of fault diagnosis in uncertain environments, focusing on innovative strategies for intelligent fault diagnosis. To this end, conventional fault diagnosis methods are first reviewed, including advances in mechanism-driven, data-driven, and hybrid-driven diagnostic models and their strengths, limitations, and applicability across various scena

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38

Verma, Archana. "Role of artificial intelligence in evaluating autism spectrum disorder." Scientific Temper 15, no. 02 (2024): 2404–9. http://dx.doi.org/10.58414/scientifictemper.2024.15.2.59.

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Autism spectrum disorder (ASD) is a neurological illness characterized by challenges with repetitive tasks, social interaction, and communication. Even if genetics is the primary cause, early detection is vital, and using ML presents a promising way to diagnose the condition more quickly and affordably. In an effort to improve and automate the diagnostic process, this research uses a variety of machine-learning techniques to pinpoint important ASD features. With the rapid growth of artificial intelligence techniques, it has become possible to use intelligent methods to carry out early large-sc

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Hu, Han Mei, Jun Lei Zhao, and Ping Wen Tu. "On the Diagnostic Methods of Bayesian-Network in Smart Grid." Applied Mechanics and Materials 71-78 (July 2011): 2424–28. http://dx.doi.org/10.4028/www.scientific.net/amm.71-78.2424.

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Aiming at the smart grid self-healing characteristics, puts forward a Bayesian network fault diagnosis method. According to the protection movement signal and the circuit breaker tripping signal, establish the face of components of the smart grid line fault diagnosis model. The fault diagnosis method is real-time and accuracy, and fault-tolerant ability etc. characteristics. This method not only satisfy intelligent power grid self-healing characteristics on fault diagnosis real-time, accuracy and automatic fault diagnosis of the requirements, but also provide the smart grid fault isolation and

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Gribova, Valeriya, Yevgeniy Shestopalov, Sergei Lebedev, et al. "OPTIMIZING PERFORMANCE OF A MULTIDISCIPLINARY REHABILITATION TEAM IN CARE DELIVERY TO STROKE SURVIVORS USING ARTIFICIAL INTELLIGENCE METHODS." Social Aspects of Population Health 70, no. 3 (2024): 1. http://dx.doi.org/10.21045/2071-5021-2024-70-3-1.

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Significance. At the present stage of the rehabilitation medicine development, it has become clear that decision support systems and artificial intelligence technologies should be actively implemented. These technologies can assist specialists in gaining a better understanding of impairments, activity levels, and engagement of individuals with stroke in rehabilitation. The creation of a unified tool will aid the multidisciplinary team in formulating a rehabilitation diagnosis and determining a more accurate rehabilitation potential, thereby ensuring high overall effectiveness of medical rehabi

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Hu, Bingbing, Jiahui Tang, Jimei Wu, and Jiajuan Qing. "An Attention EfficientNet-Based Strategy for Bearing Fault Diagnosis under Strong Noise." Sensors 22, no. 17 (2022): 6570. http://dx.doi.org/10.3390/s22176570.

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With the continuous development of artificial intelligence, data-driven fault diagnosis methods are gradually attracting widespread attention. However, in practical industrial applications, noise in the working environment is inevitable. This leads to the fact that the performance of traditional intelligent diagnosis methods is hardly sufficient to satisfy the requirements. In this paper, a developed intelligent diagnosis framework is proposed to overcome this deficiency. The main contributions of this paper are as follows: Firstly, a fault diagnosis model is established using EfficientNet, wh

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Zhang, Yuepeng, Jun Wu, Bo Gao, et al. "Fault Types and Diagnostic Methods of Manipulator Robots: A Review." Sensors 25, no. 6 (2025): 1716. https://doi.org/10.3390/s25061716.

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Manipulator robots hold significant importance for the development of intelligent manufacturing and industrial transformation. Manufacturers and users are increasingly focusing on fault diagnosis for manipulator robots. The voltage, current, speed, torque, and vibration signals of manipulator robots are often used to explore the fault characteristics from a frequency perspective, and temperature and sound are also used to represent the fault information of manipulator robots from different perspectives. Technically, manipulator robot fault diagnosis involving human intervention is gradually be

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Chen, Weiqiang, and Ali M. Bazzi. "Logic-Based Methods for Intelligent Fault Diagnosis and Recovery in Power Electronics." IEEE Transactions on Power Electronics 32, no. 7 (2017): 5573–89. http://dx.doi.org/10.1109/tpel.2016.2606435.

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Luo, Daming, Kanglei Du, and Ditao Niu. "Intelligent Diagnosis of Urban Underground Drainage Network: From Detection to Evaluation." Structural Control and Health Monitoring 2024 (May 6, 2024): 1–22. http://dx.doi.org/10.1155/2024/9217395.

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During the process of urban development, there is large-scale laying of underground pipeline networks and coordinated operation of both new and old networks. The underground concrete drainage pipes have become a focus of operation and maintenance due to their strong concealment and serious corrosion. The current manual inspections for subterranean concrete drainage pipelines involve high workloads and risks, which makes meeting the diagnostic needs of intricate urban pipeline networks challenging. Through advanced information technology, it has reached a consensus to intelligently perceive, ac

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Lee, Dong-Gun, Yonghun Jang, and Yeong-Seok Seo. "Intelligent Image Synthesis for Accurate Retinal Diagnosis." Electronics 9, no. 5 (2020): 767. http://dx.doi.org/10.3390/electronics9050767.

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Ophthalmology is a core medical field that is of interest to many. Retinal examination is a commonly performed diagnostic procedure that can be used to inspect the interior of the eye and screen for any pathological symptoms. Although various types of eye examinations exist, there are many cases where it is difficult to identify the retinal condition of the patient accurately because the test image resolution is very low because of the utilization of simple methods. In this paper, we propose an image synthetic approach that reconstructs the vessel image based on past retinal image data using t

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He, Jun, Ming Ouyang, Chen Yong, Danfeng Chen, Jing Guo, and Yan Zhou. "A Novel Intelligent Fault Diagnosis Method for Rolling Bearing Based on Integrated Weight Strategy Features Learning." Sensors 20, no. 6 (2020): 1774. http://dx.doi.org/10.3390/s20061774.

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Intelligent methods have long been researched in fault diagnosis. Traditionally, feature extraction and fault classification are separated, and this process is not completely intelligent. In addition, most traditional intelligent methods use an individual model, which cannot extract the discriminate features when the machines work in a complex condition. To overcome the shortcomings of traditional intelligent fault diagnosis methods, in this paper, an intelligent bearing fault diagnosis method based on ensemble sparse auto-encoders was proposed. Three different sparse auto-encoders were used a

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Gong, Chen, Zhang, et al. "A Novel Deep Learning Method for Intelligent Fault Diagnosis of Rotating Machinery Based on Improved CNN-SVM and Multichannel Data Fusion." Sensors 19, no. 7 (2019): 1693. http://dx.doi.org/10.3390/s19071693.

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Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of rotating machinery, especially for fault orientations and severity degree, is still a major challenge in the field of intelligent fault diagnosis. The traditional fault diagnosis methods rely on the manual feature extraction of engineers with prior knowledge. To effectively identify an incipient fault in rotating machinery, this paper proposes a novel method, namely improved the convolutional neural network-sup

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Chen, Yong, Siyuan Liang, Wanfu Li, Hong Liang, and Chengdong Wang. "Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review." Applied Sciences 9, no. 10 (2019): 2116. http://dx.doi.org/10.3390/app9102116.

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Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the electrical, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. Next, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven diagnostic algorithms are enumerated. The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intellig

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Li, Xinyu, Zihao Lei, Guangrui Wen, et al. "Intelligent Fault Diagnosis with Multi-scale Convolutional Dense Network." Journal of Physics: Conference Series 2184, no. 1 (2022): 012009. http://dx.doi.org/10.1088/1742-6596/2184/1/012009.

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Abstract With the continuous development of artificial intelligence technology, intelligent fault diagnosis approaches have been successfully developed and achieved promising performance in recent years. However, in the existing methods, the time domain characteristics of the signal are first ignored in the process of network construction, and at the same time, it is less considered in the aspects of multi-scale feature extraction and feature fusion. In order to solve the above problems, a multi-scale convolutional dense network (MCDN) was established. Specifically, the proposed framework main

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Huang, Peihao, Tao Wang, Lin Ding, Huhuang Yu, Yong Tang, and Dianle Zhou. "Comparative Analysis of Real-Time Fault Detection Methods Based on Certain Artificial Intelligent Algorithms for a Hydrogen–Oxygen Rocket Engine." Aerospace 9, no. 10 (2022): 582. http://dx.doi.org/10.3390/aerospace9100582.

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The real-time fault detection and diagnosis algorithm of a liquid rocket engine is the basis of online reconfiguration of guidance and the control system of a launch vehicle, which is directly related to the success or failure of space mission. Based on previous related works, this paper carries out comparative experimental studies of relevant intelligent algorithm models for real-time fault detection engineering application requirements of a liquid hydrogen–oxygen rocket engine. Firstly, the working state and detection parameters’ selection of a hydrogen–oxygen engine are analyzed, and the pr

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