Relevant bibliographies by topics / Probabilistic method for diagnosis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Probabilistic method for diagnosis'

Author: Grafiati
Published: 28 May 2022
Last updated: 18 July 2025

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Journal articles on the topic "Probabilistic method for diagnosis"

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Rhodes, P. C., and G. J. Karakoulas. "A probabilistic model-based method for diagnosis." Artificial Intelligence in Engineering 6, no. 2 (1991): 86–99. http://dx.doi.org/10.1016/0954-1810(91)90003-7.

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Nowakowski, Waldemar, Tomasz Ciszewski, and Zbigniew Łukasik. "Probabilistic method for railway traffic control systems diagnosis." WUT Journal of Transportation Engineering 124 (March 1, 2019): 133–40. http://dx.doi.org/10.5604/01.3001.0013.7182.

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Railway traffic control systems have a key role in ensuring the smooth operation of railway traffic. Therefore, the basic requirement, in addition to the implementation of necessary system functions, is continuous striving for ensuring the high level of reliability. Contemporary development of railway traffic control systems is associated with the application of modern information and communication technologies, which makes it possible to extend the functionality of these systems by the logging events and selfdiagnostics. However, there are no standards in this area, which considerably complic
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Ding, Shuo, Xiao Heng Chang, and Qing Hui Wu. "Application of Probabilistic Neural Networks in Fault Diagnosis of Three-Phase Induction Motors." Applied Mechanics and Materials 433-435 (October 2013): 705–8. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.705.

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In fault diagnosis of three-phase induction motors, traditional methods usually fail because of the complex system of three-phase induction motors. Short circuit is a very common stator fault in all the faults of three-phase induction motors. Probabilistic neural network is a kind of artificial neural network which is widely used due to its fast training and simple structure. In this paper, the diagnosis method based on probabilistic neural network is proposed to deal with stator short circuits. First, the principle and structure of probabilistic neural network is studied in this paper. Second
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Liu, Gu Qing, Shu Hua Yin, Xin Tian Wang, and Yan Qing Sun. "Improved Fault Diagnosis Method Based on Probabilistic Neural Network." Advanced Materials Research 433-440 (January 2012): 6084–88. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6084.

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In order to enhancing the accuracy of fault diagnosis system, an improved method based on the probabilistic neural network (PNN) is proposed, in which the synthetic attribute weights of faults are introduced that are obtained by integrating algebra view and information theory view of rough set. The synthetic attribute weights are utilized to training the classical PNN and dealing with the classification of faults so as to improving the PNN model. The new model is more accurate and can represent expertise. This novel approach is applied in digital data network to diagnose failures, and the resu
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Yu, Hongyang, Faisal Khan, and Vikram Garaniya. "A probabilistic multivariate method for fault diagnosis of industrial processes." Chemical Engineering Research and Design 104 (December 2015): 306–18. http://dx.doi.org/10.1016/j.cherd.2015.08.026.

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Jain, Bimal. "An investigation into method of diagnosis in clinicopathologic conferences (CPCs)." Diagnosis 3, no. 2 (2016): 61–64. http://dx.doi.org/10.1515/dx-2015-0034.

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AbstractAn analysis of 50 clinicopathologic conferences (CPCs) reveals the method of diagnosis in them to consist of construction of exhaustive differential diagnosis followed by evaluation of each disease in it by the likelihood inference approach. This method leads to 98% diagnostic accuracy in these CPCs. A probabilistic approach is found not to be employed for evaluation of a disease.
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Cui, Jian Guo, Bo Han Song, Shi Liang Dong, Hai Gang Liu, and Qing Zhao. "Aircraft Health Diagnosis Method Based on ARMA Model and Probabilistic Neural Network." Advanced Materials Research 225-226 (April 2011): 527–30. http://dx.doi.org/10.4028/www.scientific.net/amr.225-226.527.

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In order to diagnose the health state of Aircraft effectively, a new method based on ARMA Model and probabilistic neural network(PNN) is proposed in this paper. First, an ARMA model is built using the original acoustic emission signal of aircraft crucial components, then use the autoregressive approximation theory to estimate model parameters, and order of the model is calculated according to Akaike Information Criterion(AIC). Use the autoregressive parameters to build feature vectors, then the probabilistic neural network is used to carry out the recognition of these feature vectors, and the
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Wang, Xueyan. "A fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators." Mathematical Biosciences and Engineering 20, no. 5 (2023): 8933–53. http://dx.doi.org/10.3934/mbe.2023392.

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<abstract> <p>In recent years, automatic fault diagnosis for various machines has been a hot topic in the industry. This paper focuses on permanent magnet synchronous generators and combines fuzzy decision theory with deep learning for this purpose. Thus, a fuzzy neural network-based automatic fault diagnosis method for permanent magnet synchronous generators is proposed in this paper. The particle swarm algorithm optimizes the smoothing factor of the network for the effect of probabilistic neural network classification, as affected by the complexity of the structure and parameters
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Ai, Zeren, Hui Cao, Manqi Wang, and Kaiwen Yang. "Ship Ballast Water System Fault Diagnosis Method Based on Multi-Feature Fusion Graph Convolution." Journal of Physics: Conference Series 2755, no. 1 (2024): 012028. http://dx.doi.org/10.1088/1742-6596/2755/1/012028.

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Abstract To tackle the issues of limited fault data, inadequate information availability, and subpar fault diagnosis within the realm of ship ballast water system condition monitoring, this paper presents a novel fault diagnosis methodology known as the Probabilistic Similarity and Linear Similarity-based Graph Convolutional Neural Network (PCGCN) model. PCGCN initially converts the ship’s ballast water system dataset into two distinct graph structures: a probabilistic topology graph and a correlation topology graph. It delves into data similarity by employing T-SNE for probabilistic similarit
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Seyal, J. M., E. N. Clark, and P. W. Macfarlane. "Diagnosis of Acute Myocardial Ischaemia Using Probabilistic Methods." European Journal of Cardiovascular Prevention & Rehabilitation 9, no. 2 (2002): 115–21. http://dx.doi.org/10.1177/174182670200900207.

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Dissertations / Theses on the topic "Probabilistic method for diagnosis"

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Lam, Mary. "Benchmark of Probabilistic Methods for Fault Diagnosis." Thesis, KTH, Reglerteknik, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-106235.

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To be able to do the correct action when a fault is detected, the fault isolation part must be precise and run in real time during operation of the process. In many cases can it be difficult to decide exactly where the fault is localized. In those cases, the isolation algorithm must rank the faults according to their probability to be the cause to the behavior. The masters thesis project aims at probabilistic methods and algorithms for fault isolation in embedded systems. Different kind of Bayesian Networks have been compared in this report and the comparison has been done on a literature defi
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Зименко, Р. В., В`ячеслав Михайлович Нагорний, Вячеслав Михайлович Нагорный та Viacheslav Mykhailovych Nahornyi. "Комп'ютерна діагностика роторних машин у ймовірносній постановці". Thesis, Сумський державний університет, 2015. http://essuir.sumdu.edu.ua/handle/123456789/39779.

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Метою роботи було освоєння методики комп’ютерного діагностування стану роторної машини в ймовірносній постановці. Методика роботи полягала у вимірі вібрації установки, яка відтворювала основні елементи роторної машини [1]. Результати вимірювань являли: – сумарний рівень вібрації в трьох умовних станах установки (вихідному, попередньому і поточному); – часову реалізацію вібрації.
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Pernestål, Anna. "Probabilistic Fault Diagnosis with Automotive Applications." Doctoral thesis, Linköpings universitet, Fordonssystem, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-51931.

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The aim of this thesis is to contribute to improved diagnosis of automotive vehicles. The work is driven by case studies, where problems and challenges are identified. To solve these problems, theoretically sound and general methods are developed. The methods are then applied to the real world systems. To fulfill performance requirements automotive vehicles are becoming increasingly complex products. This makes them more difficult to diagnose. At the same time, the requirements on the diagnosis itself are steadily increasing. Environmental legislation requires that smaller deviations from spec
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Li, Zhengwei. "Adaptable, scalable, probabilistic fault detection and diagnostic methods for the HVAC secondary system." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43653.

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As the popularity of building automation system (BAS) increases, there is an increasing need to understand/analyze the HVAC system behavior with the monitoring data. However, the current constraints prevent FDD technology from being widely accepted, which include: 1)Difficult to understand the diagnostic results; 2)FDD methods have strong system dependency and low adaptability; 3)The performance of FDD methods is still not satisfactory; 4)Lack of information. This thesis aims at removing the constraints, with a specific focus on air handling unit (AHU), which is one of the most common HVAC co
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Lee, George J. (George Janbing) 1979. "CAPRI : a common architecture for distributed probabilistic Internet fault diagnosis." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40316.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Includes bibliographical references (p. 215-222).<br>This thesis presents a new approach to root cause localization and fault diagnosis in the Internet based on a Common Architecture for Probabilistic Reasoning in the Internet (CAPRI) in which distributed, heterogeneous diagnostic agents efficiently conduct diagnostic tests and c
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Huang, Xiyong. "Probabilistic fracture mechanics by boundary element method." Thesis, Imperial College London, 2010. http://hdl.handle.net/10044/1/6192.

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In this work, a new boundary element method is presented for the Probabilistic Fracture Mechanics analysis. The method developed allows the probabilistic analysis of cracked structure accomplished by the dual boundary element method (DBEM), in which the traction integral equation is used on one of the crack faces as opposed to the usual displacement integral equation. The stress intensity factors and their first order derivatives are evaluated for mode-I and mixed-mode fracture problems. A new boundary element formulation is derived and implemented to evaluate the design variables sensitivitie
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Moorman, John Everard. "A method for the probabilistic method assessment of structures containing defects." Thesis, University of Greenwich, 2005. http://gala.gre.ac.uk/6253/.

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This thesis begins by noting that the current safety justification of pressurised water reactor pressure vessels is deterministic. To enable probabilistic structural integrity safety cases of reactor pressure vessels to be carried out, a need for a new analysis of the statistical properties of fracture toughness is identified. Fracture is linked to the cracking of critically sized carbide, and a preliminary analysis showed that the distribution of carbide in steel could be described by an exponential distribution. Initially the failure of an individual fracture toughness test specimen is consi
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SUZUKI, Tatsuya, Koudai HAYASHI, and Shinkichi INAGAKI. "Fault Detection and Diagnosis of Manipulator Based on Probabilistic Production Rule." Institute of Electronics, Information and Communication Engineers, 2007. http://hdl.handle.net/2237/14985.

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Robertson, Bradford E. "A hybrid probabilistic method to estimate design margin." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50375.

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Weight growth has been a significant factor in nearly every space and launch vehicle development program. In order to account for weight growth, program managers allocate a design margin. However, methods of estimating design margin are not well suited for the task of assigning a design margin for a novel concept. In order to address this problem, a hybrid method of estimating margin is developed. This hybrid method utilizes range estimating, a well-developed method for conducting a bottom-up weight analysis, and a new forecasting technique known as executable morphological analysis. Executabl
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Johnson, Darin Bryant. "Topics In Probabilistic Combinatorics." OpenSIUC, 2009. https://opensiuc.lib.siu.edu/dissertations/63.

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This paper is a compilation of results in combinatorics utilizing the probabilistic method. Below is a brief description of the results highlighted in each chapter. Chapter 1 provides basic definitions, lemmas, and theorems from graph theory, asymptotic analysis, and probability which will be used throughout the paper. Chapter 2 introduces the independent domination number. It is then shown that in the random graph model G(n,p) with probability tending to one, the independent domination number is one of two values. Also, the the number of independent dominating sets of given cardinality is ana
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Books on the topic "Probabilistic method for diagnosis"

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Fong, Rebecca Pui Shan. Probabilistic fault diagnosis. National Library of Canada, 2003.

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Alon, Noga, and Joel H. Spencer. The Probabilistic Method. John Wiley & Sons, Inc., 2008. http://dx.doi.org/10.1002/9780470277331.

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Alon, Noga, and Joel H. Spencer. The Probabilistic Method. John Wiley & Sons, Inc., 2000. http://dx.doi.org/10.1002/0471722154.

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Alon, Noga. The probabilistic method. 3rd ed. John Wiley, 2008.

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Alon, Noga. The probabilistic method. 2nd ed. Wiley, 2000.

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Alon, Noga. The probabilistic method. Wiley, 1992.

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Alon, Noga. The probabilistic method. 3rd ed. John Wiley, 2008.

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Alon, Noga. The Probabilistic Method. John Wiley & Sons, Ltd., 2005.

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H, Spencer Joel, ed. The probabilistic method. John Wiley & Sons, Inc., 2016.

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Molloy, Michael, and Bruce Reed. Graph Colouring and the Probabilistic Method. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-04016-0.

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Book chapters on the topic "Probabilistic method for diagnosis"

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Fatès, Nazim, Régine Marchand, and Irène Marcovici. "A Decentralised Diagnosis Method with Probabilistic Cellular Automata." In Cellular Automata and Discrete Complex Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-42250-8_5.

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Zhu, Zhen, and Wanchun Dou. "QoS-Based Probabilistic Fault-Diagnosis Method for Exception Handling." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20539-2_25.

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Ding, Steven X. "Probabilistic Models and Randomised Algorithms." In Advanced methods for fault diagnosis and fault-tolerant control. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62004-5_16.

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Liu, Zheng, and Hao Wang. "Research on Process Diagnosis of Severe Accidents Based on Deep Learning and Probabilistic Safety Analysis." In Springer Proceedings in Physics. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1023-6_54.

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AbstractSevere accident process diagnosis provides data basis for severe accident prognosis, positive and negative effect evaluation of Severe Accident Management Guidelines (SAMGs), especially to quickly diagnose Plant Damage State (PDS) for operators in the main control room or personnel in the Technical Support Center (TSC) based on historic data of the limited number of instruments during the operation transition from Emergency Operation Procedures (EOPs) to SAMGs. This diagnosis methodology is based on tens of thousands of simulations of severe accidents using the integrated analysis program MAAP. The simulation process is organized in reference to Level 1 Probabilistic Safety Analysis (L1 PSA) and EOPs. According to L1 PSA, the initial event of accidents and scenarios from the initial event to core damage are presented in Event Trees (ET), which include operator actions following up EOPs. During simulation, the time uncertainty of operations in scenarios is considered. Besides the big data collection of simulations, a deep learning algorithm, Convolutional Neural Network (CNN), has been used in this severe accident diagnosis methodology, to diagnose the type of severe accident initiation event, the breach size, breach location, and occurrence time of the initial event of LOCA, and action time by operators following up EOPs intending to take Nuclear Power Plant (NPP) back to safety state. These algorithms train classification and regression models with ET-based numerical simulations, such as the classification model of sequence number, break location, and regression model of the break size and occurrence time of initial event MBLOCA. Then these trained models take advantage of historic data from instruments in NPP to generate a diagnosis conclusion, which is automatically written into an input deck file of MAAP. This input deck originated from previous traceback efforts and provides a numerical analysis basis for predicting the follow-up process of a severe accident, which is conducive to severe accident management. Results of this paper show a theoretical possibility that under limited available instruments, this traceback and diagnosis method can automatically and quickly diagnose PDS when operation transit from EOPs to SAMGs and provide numerical analysis basis for severe accident process prognosis.
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Wang, Yuxin, Tianwei Zhang, Wei Zhou, and Bin Ru. "Avionics System Fault Diagnosis Methods Based on the Probabilistic Causal Network." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54233-6_39.

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Lin, Cheng, and Ruiming Fang. "Research on Fault Diagnosis Method of Steam Turbine Generator Rotor Abnormal Vibration Based on Probabilistic Neural Networks." In Springer Proceedings in Physics. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3686-7_10.

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Suojanen, Marko, Kristian G. Olesen, and Steen Andreassen. "A method for diagnosing in large medical expert systems based on causal probabilistic networks." In Artificial Intelligence in Medicine. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0029461.

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Timashev, Sviatoslav, and Anna Bushinskaya. "Method of Assessing the Probabilistic Characteristics of Crack Growth Under the Joint Influence of Random Loads and Different Types of Corrosion Processes." In Diagnostics and Reliability of Pipeline Systems. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25307-7_8.

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Ibargüengoytia, Pablo H., L. Enrique Sucar, and Eduardo Morales. "Probabilistic Model-Based Diagnosis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/10720076_61.

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Molloy, Michael. "The Probabilistic Method." In Algorithms and Combinatorics. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-12788-9_1.

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Conference papers on the topic "Probabilistic method for diagnosis"

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Hao, Jian, Xu Li, Yi Lin, Cong Liu, Yao Zhong, and Ruilei Gong. "Novel Diagnostic Method for GIS Mechanical Defects Based on Energy Entropy and Improved Probabilistic Neural Network." In 2024 IEEE 8th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 2024. https://doi.org/10.1109/ei264398.2024.10991803.

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Zhao, Liang, and Yuejian Chen. "Machine Fault Diagnosis Based on Probabilistic Bayesian Active Learning." In 2024 Global Reliability and Prognostics and Health Management Conference (PHM-Beijing). IEEE, 2024. https://doi.org/10.1109/phm-beijing63284.2024.10874768.

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Zhang, Yuting, and Boyuan Yang. "BFD-DDPM: Denoising Diffusion Probabilistic Models for Bearing Fault Diagnosis." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10865068.

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Poncet, Andreas, Thomas P. von Hoff, and Konrad S. Stadler. "Probabilistic Diagnosis of Thermal Plants Condition." In 2006 International Conference on Probabilistic Methods Applied to Power Systems. IEEE, 2006. http://dx.doi.org/10.1109/pmaps.2006.360277.

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Shao, Jiye, Rixin Wang, Jingbo Gao, and Minqiang Xu. "Probabilistic Model-Based Fault Diagnosis of the Rotor System." In ASME 2007 Power Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/power2007-22072.

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The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observat
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Baek, Dae Seong, Chengjun Li, Jung Soo Nam, et al. "A Study on Condition Monitoring and Diagnosis of Injection Molding Process Using Probabilistic Neural Network Method." In ASME 2014 International Manufacturing Science and Engineering Conference collocated with the JSME 2014 International Conference on Materials and Processing and the 42nd North American Manufacturing Research Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/msec2014-4058.

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The objective of this research is the development of condition diagnosis model for injection molding process based on wavelet packet decomposition (WPD), feature extraction from cavity pressure, nozzle pressure and screw position signals and probability neural network (PNN) method. The node energies from the WPD of cavity and nozzle pressure signals are identified. In addition, five (5), seven (7) and two (2) critical features are extracted from the cavity pressure, nozzle pressure and screw position signals via the new feature extraction algorithm. The node energies and critical features are
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Zhang, Chuanfang, Kaixiang Peng, Jie Dong, Liang Ma, and Xueyi Zhang. "A Novel Fault Diagnosis Method Based on Multi-class Probabilistic SVDD." In 2022 IEEE 11th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2022. http://dx.doi.org/10.1109/ddcls55054.2022.9858547.

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Romessis, C., and K. Mathioudakis. "Implementation of Stochastic Methods for Industrial Gas Turbine Fault Diagnosis." In ASME Turbo Expo 2005: Power for Land, Sea, and Air. ASMEDC, 2005. http://dx.doi.org/10.1115/gt2005-68739.

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Implementation of stochastic diagnostic methods for diagnosis of sensor or component faults is presented. Two industrial gas turbines are considered as test cases, one twin and one single shaft arrangement. Methods based on Probabilistic Neural Networks (PNN) and Bayesian Belief Networks (BBN), are implemented. The ability for successful diagnosis is demonstrated on specific cases of sensor malfunctions, as well as on two types of compressor deterioration, fouling and variable vane mistuning. The examined diagnostic problem and the methods of PNN for sensor fault diagnosis and BBN for the diag
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Gu, Shuxin, Jun Ni, Jingxia Yuan, and Jay Lee. "Machine Fault Diagnosis Through a Recursive Optimal Pairwise Linear Discriminant Function Method." In ASME 1998 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1998. http://dx.doi.org/10.1115/imece1998-1068.

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Abstract A Recursive Optimal Pairwise Linear Discriminant Function (ROPLDF) method based on pattern recognition theory is developed for multiple class fault diagnosis. This approach does not need a priori failure probabilistic knowledge. Optimal pairwise linear discriminant function (OPLDF) is implemented to enhance the performance of the classification, and a recursive method is developed to allow the on-line updating of the coefficients of the OPLDF. Comparison results show that the proposed approach has better performance than other conventional approaches, such as the multiple linear discr
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Romessis, C., and K. Mathioudakis. "Bayesian Network Approach for Gas Path Fault Diagnosis." In ASME Turbo Expo 2004: Power for Land, Sea, and Air. ASMEDC, 2004. http://dx.doi.org/10.1115/gt2004-53801.

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A method for solving the gas path analysis problem of jet engine diagnostics based on a probabilistic approach is presented. The method is materialized through the use of a Bayesian Belief Network (BBN). Building a BBN for gas turbine performance fault diagnosis requires information of a stochastic nature expressing the probability of whether a series of events occurred or not. This information can be extracted by a deterministic model and does not depend on hard to find flight data of different faulty operations of the engine. The diagnostic problem and the overall diagnostic procedure are fi
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Reports on the topic "Probabilistic method for diagnosis"

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Mensing, R. W. A probabilistic method for estimating system susceptibility to HPM. Office of Scientific and Technical Information (OSTI), 1989. http://dx.doi.org/10.2172/5911803.

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Lacour, Ronald J. Radon Transform Analysis of a Probabilistic Method for Image Generation. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada222203.

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Lin, L., and J. Adams. Probabilistic method for seismic vulnerability ranking of canadian hydropower dams. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2007. http://dx.doi.org/10.4095/226350.

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Ceylan, Ismail Ilkan, Stefan Borgwardt, and Thomas Lukasiewicz. Most Probable Explanations for Probabilistic Database Queries. Technische Universität Dresden, 2017. http://dx.doi.org/10.25368/2023.220.

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Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum
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Mobrand, Lars E. Applied Ecosystem Analysis - Background : EDT the Ecosystem Diagnosis and Treatment Method. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/607526.

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Frey, H., and E. Rubin. Development and application of a probabilistic evaluation method for advanced process technologies. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/6253036.

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Kim, Minsung, Seok Ho Yoon, W. Vance Payne, and Piotr A. Domanski. Cooling mode fault detection and diagnosis method for a residential heat pump. National Institute of Standards and Technology, 2008. http://dx.doi.org/10.6028/nist.sp.1087.

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Lestelle, Lawrence C., and Lars E. Mobrand. Applied Ecosystem Analysis - - a Primer : EDT the Ecosystem Diagnosis and Treatment Method. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/658270.

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Terzic, Vesna, and William Pasco. Novel Method for Probabilistic Evaluation of the Post-Earthquake Functionality of a Bridge. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.1916.

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Abstract:
While modern overpass bridges are safe against collapse, their functionality will likely be compromised in case of design-level or beyond design-level earthquake, which may generate excessive residual displacements of the bridge deck. Presently, there is no validated, quantitative approach for estimating the operational level of the bridge after an earthquake due to the difficulty of accurately simulating residual displacements. This research develops a novel method for probabilistic evaluation of the post-earthquake functionality state of the bridge; the approach is founded on an explicit eva
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Frey, H. C., and E. S. Rubin. Development and application of a probabilistic evaluation method for advanced process technologies. Final report. Office of Scientific and Technical Information (OSTI), 1991. http://dx.doi.org/10.2172/10107730.

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