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Journal articles on the topic 'Probabilistic method for diagnosis'

Author: Grafiati

Published: 28 May 2022

Last updated: 18 July 2025

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1

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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2

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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6

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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8

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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Seyal, J., and P. W. Macfarlane. "Diagnosis of acute myocardial ischaemia using probabilistic methods." Journal of Electrocardiology 31 (January 1998): 69. http://dx.doi.org/10.1016/s0022-0736(98)90292-8.

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12

SEYAL, J. "Diagnosis of acute myocardial ischaemia using probabilistic methods." Journal of Cardiothoracic and Vascular Anesthesia 31 (1998): 69. http://dx.doi.org/10.1016/s1053-0770(98)90292-0.

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13

Kourd, Yahia, Dimitri Lefebvre, and Noureddine Guersi. "Neural Networks and Fault Probability Evaluation for Diagnosis Issues." Computational Intelligence and Neuroscience 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/370486.

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This paper presents a new FDI technique for fault detection and isolation in unknown nonlinear systems. The objective of the research is to construct and analyze residuals by means of artificial intelligence and probabilistic methods. Artificial neural networks are first used for modeling issues. Neural networks models are designed for learning the fault-free and the faulty behaviors of the considered systems. Once the residuals generated, an evaluation using probabilistic criteria is applied to them to determine what is the most likely fault among a set of candidate faults. The study also inc

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14

Romesis, Christoforos, Nikolaos Aretakis, and Konstantinos Mathioudakis. "Model-Assisted Probabilistic Neural Networks for Effective Turbofan Fault Diagnosis." Aerospace 11, no. 11 (2024): 913. http://dx.doi.org/10.3390/aerospace11110913.

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A diagnostic method for gas-path faults of turbofan engines, relying on a Probabilistic Neural Network (PNN) coupled with a thermodynamic model of the engine, is presented. The novel aspect of the method is that its training information is generated dynamically by an accompanying Engine Performance Model. In the proposed approach, the PNN efficiently addresses the first step of a diagnostic process (i.e., detection of the faulty component at the current operating point), while with the aid of an adaptive engine model, the fault is then further isolated and identified. A description of the prop

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15

Zhu, Yanting, Shunyi Zhao, Yuxuan Zhang, Chengxi Zhang, and Jin Wu. "A Review of Statistical-Based Fault Detection and Diagnosis with Probabilistic Models." Symmetry 16, no. 4 (2024): 455. http://dx.doi.org/10.3390/sym16040455.

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As industrial processes grow increasingly complex, fault identification becomes challenging, and even minor errors can significantly impact both productivity and system safety. Fault detection and diagnosis (FDD) has emerged as a crucial strategy for maintaining system reliability and safety through condition monitoring and abnormality recovery to manage this challenge. Statistical-based FDD methods that rely on large-scale process data and their features have been developed for detecting faults. This paper overviews recent investigations and developments in statistical-based FDD methods, focu

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Lucas, Peter J. F., Giso Dal, Arjen Hommersom, and Guus Grievink. "Model-based Probabilistic Diagnosis in Large Cyberphysical Systems." PHM Society European Conference 8, no. 1 (2024): 12. http://dx.doi.org/10.36001/phme.2024.v8i1.4033.

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Model-based diagnosis is concerned with diagnosing faults or malfunction of real-world physical or cyberphysical systems using a model of the structure and behavior of the systems. As cyberphysical systems can be extremely large and complex, and the associated computational models will be then equally large and complex, they impose a hard to beat challenge on the computational feasibility of reasoning with such models. When such a model is able to handle the uncertainty associated with diagnostics, giving rise to probabilistic model-based diagnostics, the computational feasibility becomes even

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Wu, Yongxin, Hu Wang, and Tingting Guo. "The fault diagnosis method of photovoltaic module based on probabilistic neural network." IOP Conference Series: Earth and Environmental Science 170 (July 2018): 042009. http://dx.doi.org/10.1088/1755-1315/170/4/042009.

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18

Gao, Caixia, Tong Wu, and Ziyi Fu. "Rolling Bearings Fault Diagnosis Method Using EMD Decomposition and Probabilistic Neural Network." Proceedings of International Conference on Artificial Life and Robotics 23 (February 2, 2018): 691–94. http://dx.doi.org/10.5954/icarob.2018.os18-1.

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19

Ustinova, Yelena Ivanovna. "Hematogenic ocular tuberculosis: pathohistomorphology, diagnosis." Ophthalmology journal 6, no. 3 (2013): 51–61. http://dx.doi.org/10.17816/ov2013351-61.

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The results of patho- and histopathology investigations by A.L. Prigoshina in ocular tuberculosis were analyzed, including 98 eyes of 67 patients who died from different forms of tuberculosis forms, and 82 eyes enucleated for poor clinical outcomes of ocular tuberculosis (n=82). Results of the ocular diagnosis of tuberculosis in clinical settings by several authors using different methods are also presented (more than 700 patients). Investigation results prove the probability of hematogenous ocular tuberculosis form separation in the classification. Ocular tuberculosis diagnosis by investigati

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20

Jiang, Bing Zhen, and Jia Wei Xiang. "Rolling Bearing Fault Diagnosis Approach Based on PPCA Denoising and Cyclic Bispectrum Method." Applied Mechanics and Materials 536-537 (April 2014): 26–29. http://dx.doi.org/10.4028/www.scientific.net/amm.536-537.26.

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A new method for bearing fault diagnosis is proposed based on Probabilistic Principal Component Analysis (PPCA) and Cyclic Bispectrum (CB). The first procedure is signal de-noised using PPCA and the second procedure is the CB analysis. The effectiveness of the proposed method is demonstrated by numerical simulation and experimental investigation of a rolling bearing with outer race fault.

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21

Santucci, Valentino, Alfredo Milani, and Fabio Caraffini. "An Optimisation-Driven Prediction Method for Automated Diagnosis and Prognosis." Mathematics 7, no. 11 (2019): 1051. http://dx.doi.org/10.3390/math7111051.

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This article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and

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22

Ji, Xin Xin, and Zhong Min Wang. "Faults Diagnosis of Ball Bearing Based on Probabilistic Neural Network." Applied Mechanics and Materials 543-547 (March 2014): 1149–52. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.1149.

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To solve the difficulties of establishing precise mathematical model of ball bearing fault diagnosis, a classification method based on probabilistic neural network (PNN) used for ball bearing fault mode classification is proposed. Firstly, this paper analyzed the basic theory of PNN, and then a mapping relationship between feature vector and fault mode is set up based on PNN. Secondly, selections of ball bearing fault features and practical procedure of neural network setting and training are discussed. Experiments and compared with the algorithm of back propagation neural network (BPNN) prove

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23

Chen, Chun Chao. "Realizing Method of MATLAB Software for Fault Diagnosis of Freight Bearings." Applied Mechanics and Materials 273 (January 2013): 245–49. http://dx.doi.org/10.4028/www.scientific.net/amm.273.245.

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In order to realize the online fault diagnosis of freight rolling bearing without disassembling, a simulation test platform was established in the laboratory and acoustic emission (AE) sensor of AE-98/R15 was used to acquire AE signals. According to the signal characteristics, MATLAB software was used to analyze features of signals with wavelet packet and to recognize bearing state with probabilistic neural network. These methods have a very good effect for fault diagnosis in the laboratory. The innovation of this paper is that the above methods are effectively used for fault diagnosis and pro

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24

Sato, Renato Cesar, and Graziela Tiemy Kajita Sato. "Probabilistic graphic models applied to identification of diseases." Einstein (São Paulo) 13, no. 2 (2015): 330–33. http://dx.doi.org/10.1590/s1679-45082015rb3121.

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ABSTRACT Decision-making is fundamental when making diagnosis or choosing treatment. The broad dissemination of computed systems and databases allows systematization of part of decisions through artificial intelligence. In this text, we present basic use of probabilistic graphic models as tools to analyze causality in health conditions. This method has been used to make diagnosis of Alzheimer´s disease, sleep apnea and heart diseases.

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25

Fareh, Messaouda, Ishak Riali, Hafsa Kherbache, and Marwa Guemmouz. "Probabilistic reasoning for diagnosis prediction of Coronavirus disease based on probabilistic ontology." Computer Science and Information Systems, no. 00 (2023): 35. http://dx.doi.org/10.2298/csis220829035f.

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The novel Coronavirus has been declared a pandemic by the World Health Organization (WHO). Predicting the diagnosis of COVID-19 is essential for disease cure and control. The paper?s main aim is to predict the COVID-19 diagnosis using probabilistic ontologies to address the randomness and incomplete ness of knowledge. Our approach begins with constructing the entities, attributes, and relationships of COVID-19 ontology, by extracting symptoms and risk factors. The probabilistic components of COVID-19 ontology are developed by creating a Multi-Entity Bayesian Network, then determining its compo

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26

Tao, Lingyu, Xiaohui Yang, Yichen Zhou, and Li Yang. "A Novel Transformers Fault Diagnosis Method Based on Probabilistic Neural Network and Bio-Inspired Optimizer." Sensors 21, no. 11 (2021): 3623. http://dx.doi.org/10.3390/s21113623.

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Since it is difficult for the traditional fault diagnosis method based on dissolved gas analysis (DGA) to meet today’s engineering needs in terms of diagnostic accuracy and stability, this paper proposes an artificial intelligence fault diagnosis method based on a probabilistic neural network (PNN) and bio-inspired optimizer. The PNN is used as the basic classifier of the fault diagnosis model, and the bio-inspired optimizer, improved salp swarm algorithm (ISSA), is used to optimize the hidden layer smoothing factor of PNN, which stably improves the classification performance of PNN. Compared

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Ai, Zeren, Hui Cao, Jihui Wang, Zhichao Cui, Longde Wang, and Kuo Jiang. "Research Method for Ship Engine Fault Diagnosis Based on Multi-Head Graph Attention Feature Fusion." Applied Sciences 13, no. 22 (2023): 12421. http://dx.doi.org/10.3390/app132212421.

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At present, there are problems such as low fault data, insufficient labeling information, and poor fault diagnosis in the field of ship engine diagnosis. To address the above problems, this paper proposes a fault diagnosis method based on probabilistic similarity and rank-order similarity of multi-head graph attention neural networks (MPGANN) models. Firstly, the ship engine dataset is used to explore the similarity between the data using the probabilistic similarity of T_SNE and the rank order similarity of Spearman’s correlation coefficient to define the neighbor relationship between the sam

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Plavcan, David, Georg J. Mayr, and Achim Zeileis. "Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model." Journal of Applied Meteorology and Climatology 53, no. 3 (2014): 652–59. http://dx.doi.org/10.1175/jamc-d-13-0267.1.

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AbstractDiagnosing foehn winds from weather station data downwind of topographic obstacles requires distinguishing them from other downslope winds, particularly nocturnal ones driven by radiative cooling. An automatic classification scheme to obtain reproducible results that include information about the (un)certainty of the diagnosis is presented. A statistical mixture model separates foehn and no-foehn winds in a measured time series of wind. In addition to wind speed and direction, it accommodates other physically meaningful classifiers such as the (potential) temperature difference to an u

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Zhao, Pengcheng, Wei Zhang, Xiaoshan Cao, and Xiang Li. "Denoising diffusion probabilistic model-enabled data augmentation method for intelligent machine fault diagnosis." Engineering Applications of Artificial Intelligence 139 (January 2025): 109520. http://dx.doi.org/10.1016/j.engappai.2024.109520.

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30

Meng, Liang, Yuanhao Su, Xiaojia Kong, et al. "A Probabilistic Bayesian Parallel Deep Learning Framework for Wind Turbine Bearing Fault Diagnosis." Sensors 22, no. 19 (2022): 7644. http://dx.doi.org/10.3390/s22197644.

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The technology of fault diagnosis helps improve the reliability of wind turbines. Difficulties in feature extraction and low confidence in diagnostic results are widespread in the process of deep learning-based fault diagnosis of wind turbine bearings. Therefore, a probabilistic Bayesian parallel deep learning (BayesianPDL) framework is proposed and then achieves fault classification. A parallel deep learning (PDL) framework is proposed to solve the problem of difficult feature extraction of bearing faults. Next, the weights and biases in the PDL framework are converted from deterministic valu

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Junath, N., Alok Bharadwaj, Sachin Tyagi, Kalpana Sengar, Mohammad Najmus Saquib Hasan, and M. Jayasudha. "Prognostic Diagnosis for Breast Cancer Patients Using Probabilistic Bayesian Classification." BioMed Research International 2022 (July 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/1859222.

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The diagnosis and treatment of patients in the healthcare industry are greatly aided by data analytics. Massive amounts of data should be handled using machine learning approaches to provide tools for prediction and categorization to support practitioner decision-making. Based on the kind of tumor, disorders like breast cancer can be categorized. The difficulties associated with evaluating vast amounts of data should be overcome by discovering an efficient method for categorization. Based on the Bayesian method, we analyzed the influence of clinic pathological indicators on the prognosis and s

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32

Ma, Jianpeng, Zhenghui Li, Chengwei Li, Liwei Zhan, and Guang-Zhu Zhang. "Rolling Bearing Fault Diagnosis Based on Refined Composite Multi-Scale Approximate Entropy and Optimized Probabilistic Neural Network." Entropy 23, no. 2 (2021): 259. http://dx.doi.org/10.3390/e23020259.

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A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algorithm. Rolling bearing early fault diagnosis is a time-sensitive task, which is significant to ensure the reliability and safety of mechanical fault system. At the same time, the early fault features are masked by strong background noise, which also brings difficulties to fault diagnosis. So, we firstly utilize the composite ensemble intrinsic time-

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Xie, Chun Ling. "Diagnosis of Nuclear Power Plant Based on Probabilistic Neural Network." Applied Mechanics and Materials 513-517 (February 2014): 3992–95. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3992.

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This paper presents development of an automatic fault diagnosis system in the nuclear power plants to minimize possible nuclear disasters. Probabilistic Neural Network (PNN) overcame the shortcomings of entrapment in local optimum, slow convergence rate which was in BP algorithm. PNN is fit to diagnose the fault of nuclear power plant and has auto-adaptability. The method not only makes the original neural network smaller in terms of computation and realization, but also improves diagnosis speed and accuracy of nuclear power plant.

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34

Zhang, San Tong. "Research of Fault Diagnosis Based on Bayesian Network for Air Brake System." Advanced Materials Research 143-144 (October 2010): 629–33. http://dx.doi.org/10.4028/www.scientific.net/amr.143-144.629.

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A method for solving the fault diagnosis problem of air brake system based on probabilistic approach is presented. The fault diagnosis model based on Bayesian network was built for the uncertainty characteristic of fault in the air brake system. Through evaluating the characteristic of Bayesian networks in the diagnosis inference and model expression, it is demonstrated that this method can solve the uncertain problems in fault diagnosis. The test result has shown that the Bayesian network model is effective in fault diagnosis of the air brake system.

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Li, Yichen, Zhiqiang Rao, Ziyi Li, and Lu Ding. "Research on Fault Location Method of Track Circuit Compensation Capacitor Based on Probabilistic Neural Network." Architecture Engineering and Science 3, no. 2 (2022): 93. http://dx.doi.org/10.32629/aes.v3i2.822.

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Compensation capacitor is an important component for extending the signal transmission of track circuit, and its safe operation is very important to the transportation business of rail transit. According to the difficulty of diagnosing the fault of compensation capacitor, a fault location model of compensation capacitor based on probabilistic neural network is established. Firstly, the influence of compensation capacitors on the current curve is analyzed from the two aspects of the failure reasons of compensation capacitors and the influence on signal transmission. Then, according to the param

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Cai, Jiang Hui, Wen Jun Meng, and Zhi Mei Chen. "A Fault Diagnosis Method Based on Constrained Frequent Pattern Trees." Applied Mechanics and Materials 39 (November 2010): 449–54. http://dx.doi.org/10.4028/www.scientific.net/amm.39.449.

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Data mining is a broad term used to describe various methods for discovering patterns in data. A kind of pattern often considered is association rules, probabilistic rules stating that objects satisfying description A also satisfy description B with certain support and confidence. In this study, we first make use of the first-order predicate logic to represent knowledge derived from celestial spectra data. Next, we propose a concept of constrained frequent pattern trees (CFP) along with an algorithm used to construct CFPs, aiming to improve the efficiency and pertinence of association rule min

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VÉRONIQUE, DELCROIX, MAALEJ MOHAMED-AMINE, and PIECHOWIAK SYLVAIN. "BAYESIAN NETWORKS VERSUS OTHER PROBABILISTIC MODELS FOR THE MULTIPLE DIAGNOSIS OF LARGE DEVICES." International Journal on Artificial Intelligence Tools 16, no. 03 (2007): 417–33. http://dx.doi.org/10.1142/s0218213007003345.

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Multiple diagnosis methods using Bayesian networks are rooted in numerous research projects about model-based diagnosis. Some of this research exploits probabilities to make a diagnosis. Many Bayesian network applications are used for medical diagnosis or for the diagnosis of technical problems in small or moderately large devices. This paper explains in detail the advantages of using Bayesian networks as graphic probabilistic models for diagnosing complex devices, and then compares such models with other probabilistic models that may or may not use Bayesian networks.

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Wang, Yanyu, Peng Qiu, Yang Liu, Yishen Guo, and Cheng Peng. "IGSA-PNN-based Methods for Power Transformer Fault Diagnosis." Journal of Physics: Conference Series 2694, no. 1 (2024): 012082. http://dx.doi.org/10.1088/1742-6596/2694/1/012082.

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Abstract To enhance the precision of power transformer fault diagnosis, it is necessary to make improvements. Aiming at the shortcomings of Probabilistic Neural Network (PNN) network experience selection smoothing factor and avoiding the shortcomings of traditional Gravitational Search Algorithm (GSA) easy 0to fall into local optimum and convergence speed slow, a Probabilistic Neural Network (PNN) model using chaos sequence to improved GSA for power transformer fault diagnosis is proposed. Firstly, chaos sequence is used to increase the diversity of gravitational particles to avoid falling int

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Liang, Shitong, and Jie Ma. "Compound Fault Diagnosis of Gearbox Based on RLMD and SSA-PNN." Mathematical Problems in Engineering 2021 (September 29, 2021): 1–9. http://dx.doi.org/10.1155/2021/3716033.

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In order to solve the difficulty in the classification of gearbox compound faults, a gearbox fault diagnosis method based on the sparrow search algorithm (SSA) improved probabilistic neural network (PNN) is proposed. Firstly, the gearbox fault signal is decomposed into a series of product functions (PFs) by robust local mean decomposition (RLMD). Then, the permutation entropy of PFs, which contains much fault information, is calculated to construct the feature vector and input it into the SSA-PNN model. The experimental results show that compared with the traditional fault diagnosis methods ba

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40

Aulia, Wizra. "SISTEM PAKAR DIAGNOSA PENYAKIT JANTUNG KORONER DENGAN METODE PROBABILISTIC FUZZY DECISION TREE." Jurnal Sains dan Informatika 4, no. 2 (2018): 106. http://dx.doi.org/10.22216/jsi.v4i2.3258.

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<p><em>Di Indonesia, penyakit jantung koroner menempati posisi pertama sebagai penyakit yang paling banyak mengakibatkan kematian. Jika gejala penyakit jantung koroner dikenali sejak dini maka dapat dilakukan tindakan antisipasi. Diagnosa dilakukan berdasarkan 6 gejala penyakit jantung koroner yaitu sakit dada, tekanan darah tinggi, kolesterol, kadar gula darah, hasil EKG dan jumlah denjut jantung. Metode yang dipakai adalah Probabilistic Fuzzy Decision Tree (PFDT) dengan algoritma Probabilistic Fuzzy ID3. Hasil keakuratan sistem pakar diagnosa penyakit jantung koroner dengan metod

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41

Vanchuliak, Oleh, Victor Bachinskiy, and Alexander Ushenko. "THE METHOD OF SPECTRALLY SELECTIVE LASER MUELLER MATRIX POLARIMETRY FOR VERIFICATION OF ACUTE CORONARY INSUFFICIENCY." CBU International Conference Proceedings 4 (September 17, 2016): 706–10. http://dx.doi.org/10.12955/cbup.v4.836.

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INTRODUCTION: As the existing methods of evaluation of acute coronary insufficiency currently held is to some extent subjective. However, the specificity of forensic medicine requires objective methods. Thus, there is a necessity for objective methods of diagnosis for acute coronary insufficiency (ACI).OBJECTIVES: The objective of this study isto establish the diagnostic possibilities of the laser method, Muellermatrix polarimetry, on wavelength 450 nm of autofluorescence, with the method of statistical analysis of native heart slices to provide an after-death diagnosis of acute coronary insuf

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Wang, Bo, Hongwei Ke, Xiaodong Ma, and Bing Yu. "Fault Diagnosis Method for Engine Control System Based on Probabilistic Neural Network and Support Vector Machine." Applied Sciences 9, no. 19 (2019): 4122. http://dx.doi.org/10.3390/app9194122.

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Due to the poor working conditions of an engine, its control system is prone to failure. If these faults cannot be treated in time, it will cause great loss of life and property. In order to improve the safety and reliability of an aero-engine, fault diagnosis, and optimization method of engine control system based on probabilistic neural network (PNN) and support vector machine (SVM) is proposed. Firstly, using the German 3 W piston engine as a control object, the fault diagnosis scheme is designed and introduced briefly. Then, the fault injection is performed to produce faults, and the data

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Sharma, Niharika, Prakhar Mishra, K. Sadhana, and P. Nithyakani. "Segmenting and classifying MRI images for brain tumors using CNN." International Journal of Engineering & Technology 7, no. 3.29 (2018): 146. http://dx.doi.org/10.14419/ijet.v7i3.29.18545.

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Gliomas are one of the most prevalent and aggressive form of brain tumours in the world. Patient's usually go on to live a very short life after the initial diagnosis. Therefore, it is crucial to successfully and quickly outline a method for diagnosing the same in it's very earliest stages.Magnetic Resonance Imaging, or MRI as it is more frequently called is a noninvasive method of imaging parts of human anatomy. MRI's utilise robust fields of magnetism, along with waves that have frequencies corresponding to the radio waves in the spectrum to develop precise pictures to get a sense of the hap

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Aseeri, Ahmad O. "Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals." Computers 10, no. 6 (2021): 82. http://dx.doi.org/10.3390/computers10060082.

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Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classi

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Ni, Yun Feng, and Dong Dong Ban. "Rolling Bearing Fault Diagnosis Method Based on Principal Components Analysis and Probabilistic Neural Network." IOP Conference Series: Materials Science and Engineering 740 (March 17, 2020): 012012. http://dx.doi.org/10.1088/1757-899x/740/1/012012.

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Luo, Yuanqing, Changzheng Chen, Siyu Zhao, Xiangxi Kong, and Zhong Wang. "Rolling Bearing Diagnosis Based on Adaptive Probabilistic PCA and the Enhanced Morphological Filter." Shock and Vibration 2020 (August 19, 2020): 1–26. http://dx.doi.org/10.1155/2020/8828517.

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Early fault diagnosis of rolling element bearing is still a difficult problem. Firstly, in order to effectively extract the fault impulse signal of the bearing, a new enhanced morphological difference operator (EMDO) is constructed by combining two optimal feature extraction-type operators. Next, in the process of processing the test signal, in order to reduce the interference problem caused by strong background noise, the probabilistic principal component analysis (PPCA) method is introduced. In the traditional PPCA method, two important system parameters (decomposition principal component k

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Joseph, Voufo*1 Joseph Kenfack2 O. Videme Bossou3 John Ngundam Mucho4 T. Tamo Tatiétsé5. "ROBUSTNESS 'ANALYSIS OF FAULT DIAGNOSIS WITH FAULT PASSAGE INDICATORS ON MEDIUM VOLTAGE DISTRIBUTION NETWORKS IN PRESENCE OF THE DISPERSED GENERATION: CASE OF CENTRAL-AFRICAN COUNTRIES." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 11 (2017): 512–23. https://doi.org/10.5281/zenodo.1066224.

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The article analyses the robustness of the fault diagnosis in the MV distribution network with the fault passage indicators (FPI). A probabilistic method using the probabilities of functioning and the signature response of FPI is proposed. The efficiency of the method is proven experimentally.   

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Wong, Pak Kin, Jian-Hua Zhong, Zhi-Xin Yang, and Chi Man Vong. "A new framework for intelligent simultaneous-fault diagnosis of rotating machinery using pairwise-coupled sparse Bayesian extreme learning committee machine." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 6 (2016): 1146–61. http://dx.doi.org/10.1177/0954406216632022.

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This paper proposes a new diagnostic framework, namely, probabilistic committee machine, to diagnose simultaneous-fault in the rotating machinery. The new framework combines a feature extraction method with ensemble empirical mode decomposition and singular value decomposition, multiple pairwise-coupled sparse Bayesian extreme learning machines (PCSBELM), and a parameter optimization algorithm to create an intelligent diagnostic framework. The feature extraction method is employed to find the features of single faults in a simultaneous-fault pattern. Multiple PCSBELM networks are built as diff

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Nawaz, Ayla, Christian Herzog né Hoffmann, Jan Graßhoff, Sven Pfeiffer, Gerwald Lichtenberg, and Philipp Rostalski. "Probabilistic model-based fault diagnosis for the cavities of the European XFEL." at - Automatisierungstechnik 69, no. 6 (2021): 538–49. http://dx.doi.org/10.1515/auto-2020-0064.

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Abstract The European X-ray Free Electron Laser (EuXFEL) is a complex system with many interconnected components and sensor measurements. We use factor graphs to systematically design a probabilistic fault diagnosis method for its cavity system. This approach is expandable to further subsystems and considers uncertainties from measurements and modeling. After representing a model of the cavity system in the factor graph framework, we infer marginal distributions, e. g., of the fault classes using tabulated message-passing definitions. The emerging fault diagnosis method consists of an unscente

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Xiong, Yanzhi, and Tianlan Wei. "Probalistic model of spam filter system." Applied and Computational Engineering 6, no. 1 (2023): 382–87. http://dx.doi.org/10.54254/2755-2721/6/20230812.

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Numerous tasks have been carried out using probabilistic reasoning, including picture recognition, computer diagnosis, stock price prediction, movie recommendation, and cyber intrusion detection. But until recently, the breadth of probabilistic programming was constrained (partly because of the low processing power), and the majority of inference methods had to be created manually for every job. Nevertheless, in 2015, 3D representations of human faces were created from 2D photographs of such faces using a 50-line probabilistic computer vision software. The inference approach of the software, w

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