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Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function network and pattern recognition neural network

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Chinese Journal of Mechanical Engineering 2023年第2期36卷 180-197页

作者： Yuhong Jin Lei Hou Zhenyong Lu Yushu Chen School of Astronautics Harbin Institute of TechnologyHarbin 150001China Institute of Dynamics and Control Science Shandong Normal UniversityJi’nan 250014China

The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault ***,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration *** this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and pattern recognition neural network(PRNN)is ***,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are ***,the dynamic response is obtained by the harmonic balance *** adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall *** analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault *** on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as *** results show that the proposed method has high fault diagnosis *** research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.

关键词： Hollow shaft rotor Breathing crack Radial basis function network pattern recognition neural network Machine learning

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Detection of multi-type data anomaly for structural health monitoring using pattern recognition neural network

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SMART STRUCTURES AND SYSTEMS 2022年第1期29卷 129-140页

作者： Gao, Ke Chen, Zhi-Dan Weng, Shun Zhu, Hong-Ping Wu, Li-Ying Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan Peoples R China

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

关键词： data anomaly detection feature extraction pattern recognition neural network structural health monitoring

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Particle swarm optimization pattern recognition neural network for transmission lines faults classification

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INTELLIGENT DATA ANALYSIS 2022年第1期26卷 189-203页

作者： Zhang, Liang Zhao, Zhengang Zhang, Dacheng Luo, Chuan Li, Chuan Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Yunnan Key Lab Comp Technol Applicat Kunming 650500 Yunnan Peoples R China

The operating conditions of the transmission lines can be assessed through the information measured by the smart meters in the power supply bureau. Accurate classification of transmission line faults can be helpful to improve the maintenance strategy of smart grids. This paper analyzes the mechanism of the voltage loss and the phase fault of the transmission line by using the operation data collected by the smart meters from three power supply bureaus (named Bureau A, B and C), where the faults are labeled by expert systems. In this work, a novel Particle Swarm Optimization pattern recognition neural network (PSO-PRNN) classifier is built to accurately categorize the faults and its classification performance is compared with the ones of traditional K-Nearest Neighbor (KNN), Decision Tree (DT), PSO-KNN and PSO-DT classifiers. The results show that the classification accuracy of PSO-PRNN outperforms traditional classifiers when being applied to the data collected from all three bureaus. In the A power supply bureau are 83.0%, 88.7%, 82.0%, 86.9% and 96.1%, and the classification accuracy rates are 55.7%, 68.7%, 56.6%, 68.7% and 82.5%, when used to process the data of the bureau B. The classification accuracy is 57.1%, 66.4%, 57.2%, 69.0% and 82.1%, when processing the data of bureau C. The results show that the PSO-PRNN classifier is superior to the others in terms of accuracy and applicability.

关键词： Transmission lines voltage phase loss phase break particle swarm optimization pattern recognition neural network

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A Feasibility Area Approach for Early Stage Detection of Stealthy Infiltrated Cyberattacks in Power Systems

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2024年 19卷 6455-6470页

作者： Elsayed, Ahmed Abd Elaziz Khani, Hadi Farag, Hany Essa Zidan York Univ Lassonde Sch Engn Dept Elect Engn & Comp Sci Toronto ON M3J 1L4 Canada

Advanced stealthy cyberattacks are capable of infiltrating the cybersecurity layers of power grids and alter their operating conditions, resulting in adverse effects on the system performance. Detecting such Stealthy Infiltrated Cyberattacks (SICA) at the earliest opportunity becomes crucial in order to enable power system operators to implement appropriate corrective measures. To that end, this paper proposes the addition of a new cybersecurity layer for SICA after they have broken through existing cyberattack prevention layers. The paper develops the Feasibility Area (FA) as a classifier mechanism to detect SICA in the collected data of Power System State Variables (PSSV). The proposed detection layer consists of two computational stages. The first stage involves estimating the FA parameters through a historical window of data over a specified period of time, which is then inputted to the second stage. In the second stage, the position of each PSSV with respect to the estimated FA is assessed and utilized by the SICA detection mechanism to identify broken through attacks. A flag vector is created indicating the location of each PSSV with respect to the defined FA. The location of each PSSV and its pattern represented in the flag vector are utilized to identify the existence of SICA. Various SICA detection mechanisms using mathematical techniques and the pattern recognition neural network (PRNN) have been applied. The numerical results from the evaluation of the proposed FA approach demonstrate a promising performance in detecting the SICA using the proposed method.

关键词： Cybersecurity false data injection stealthy infiltrated attacks bad data detection layer feasibility area pattern recognition neural network

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Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower

SMART AGRICULTURAL TECHNOLOGY

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SMART AGRICULTURAL TECHNOLOGY 2025年 10卷

作者： Atsmon, Guy Brook, Anna Cohen, Tom Avikasis Kizel, Fadi Eizenberg, Hanan Lati, Ran Nisim Agr Res Org Newe Yaar Res Ctr Dept Plant Pathol & Weed Res IL-30095 Ramat Yishay Israel Hebrew Univ Jerusalem Robert H Smith Fac Agr Food & Environm IL-7610001 Rehovot Israel Univ Haifa Sch Environm Sci Spect & Remote Sensing Lab IL-498838 Har Hakarmel Israel Technion Israel Inst Technol Dept Mapping & Geoinformat Engn Haifa Israel

Sunflower broomrape (Orobanche cumana) poses a severe threat to sunflower crops, parasitizing their roots and hindering plant growth. Current control methods, which typically rely on uniform herbicide applications, are economically inefficient and environmentally damaging. This study investigates the use of unmanned aerial vehicle (UAV)-based multispectral imaging to detect broomrape-infected sunflowers by analyzing temporal patterns in spectral vegetation indices (VIs). Over four imaging campaigns conducted during early subsoil parasitic stages, multispectral data were collected and processed to compute ten VIs. These VIs, reflecting changes in canopy reflectance over time, were then analyzed using various machine learning models, including a pattern recognition neural network (PRNN). Results showed that the PRNN model, trained on time-series data, achieved an overall accuracy of 84.8 % and a true positive rate of 80.4 % in detecting broomrape infection, emphasizing the strength of utilizing temporal data for enhancing detection accuracies. Pixel-level reconstruction maps revealed varying spectral responses within infected canopies, highlighting the importance of accounting for this heterogeneity. This study demonstrates the potential of UAV-based multispectral imaging combined with advanced machine learning (ML) techniques for early detection of broomrape infestations in sunflower crops, offering insights for managing similar infestations in other crops.

关键词： Classification Multispectral Site-specific weed management pattern recognition neural network

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UV/VIS imaging-based PAT tool for drug particle size inspection in intact tablets supported by pattern recognition neural networks

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INTERNATIONAL JOURNAL OF PHARMACEUTICS 2022年第0期620卷 121773页

作者： Meszaros, Lilla Alexandra Farkas, Attila Madarasz, Lajos Bicsar, Rozlia Galata, Dorain Lazlo Nagy, Brigitta Nagy, Zsombor Kristof Budapest Univ Technol & Econ Dept Organ Chem & Technol Muegyetem Rakpart 3 H-1111 Budapest Hungary

The potential of machine vision systems has not currently been exploited for pharmaceutical applications, although expected to provide revolutionary solutions for in-process and final product testing. The presented paper aimed to analyze the particle size of meloxicam, a yellow model active pharmaceutical ingredient, in intact tablets by a digital UV/VIS imaging-based machine vision system. Two image processing algorithms were developed and coupled with pattern recognition neural networks for UV and VIS images for particle size-based classification of the prepared tablets. The developed method can identify tablets containing finer or larger particles than the target with more than 97% accuracy. Two algorithms were developed for UV and VIS images for particle size analysis of the prepared tablets. According to the applied statistical tests, the obtained particle size distributions were similar to the results of the laser diffraction-based reference method. Digital UV/VIS imaging combined with multivariate data analysis can provide a new non-destructive, rapid, in-line tool for particle size analysis in tablets.

关键词： Image analysis Machine vision Tablet inspection Particle size distribution Particle size analysis pattern recognition neural network

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High performance computation of human computer interface for neurodegenerative individuals using eye movements and deep learning technique

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JOURNAL OF SUPERCOMPUTING 2022年第2期78卷 2772-2792页

作者： Ramakrishnan, Jayabrabu Doss, Rajesh Palaniswamy, Thangam Faqihi, Raddad Fathima, Dowlath Srinivasan, Karthik Jazan Univ Coll Comp Sci & Informat Technol Jazan Saudi Arabia Natl Def Acad Fac Comp Sci Pune Maharashtra India King Abdulaziz Univ Fac Engn Elect & Comp Engn Jeddah Saudi Arabia Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia Saudi Elect Univ Coll Sci & Theoret Studies Dept Basic Sci Riyadh Saudi Arabia

Disabilities due to neurodegenerative disease are rapidly increasing in number. The need for rehabilitative devices to achieve a normal and comfortable life in the absence of biochannels has also increased. The activities of biochannels can be easily replaced by implementing rehabilitative devices. The electrooculography (EOG)-based human-computer interface (HCI) is one of the most important techniques for enabling disabled persons to enjoy a normal life. The technique of measuring the cornea-retina potential difference is called EOG. The technology for converting thoughts to different control patterns to activate external devices is called the HCI. In this paper, we carried out a study on ten subjects aged 20-30 years using a five-electrode signal acquisition system (AD T26). The subject performances were experimentally verified by implementing periodogram features with a feedforward neural network trained on a nature-inspired algorithm. The analysis was conducted offline and online to evaluate the achievement of the developed HCI. The study showed an average classification accuracy of 93.93% for four tasks, with 95% accuracy for the offline mode and 90.12% for the online mode. The participating subjects drove the mobile robot in all directions frequently and quickly with a recognition accuracy of 98.12%. The study confirmed that the four tasks (related to driving the external device) performed by the subjects were convenient to perform in real time.

关键词： Feedforward neural network Electrooculography Periodogram Ant lion optimization Human-computer interface Hilbert Huang transform pattern recognition neural network

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Very Short-Term Forecast: Different Classification Methods of the Whole Sky Camera Images for Sudden PV Power Variations Detection

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ENERGIES 2022年第24期15卷 9433页

作者： Niccolai, Alessandro Ogliari, Emanuele Nespoli, Alfredo Zich, Riccardo Vanetti, Valentina Politecn Milan Dept Energy Via Giuseppe La Masa 34 I-20156 Milan Italy

Solar radiation is by nature intermittent and influenced by many factors such as latitude, season and atmospheric conditions. As a consequence, the growing penetration of Photovoltaic (PV) systems into the electricity network implies significant problems of stability, reliability and scheduling of power grid operation. Concerning the very short-term PV power production, the power fluctuations are primarily related to the interaction between solar irradiance and cloud cover. In small-scale systems such as microgrids, the adoption of a forecasting tool is a brilliant solution to minimize PV power curtailment and limit the installed energy storage capacity. In the present work, two different nowcasting methods are applied to classify the solar attenuation due to clouds presence on five different forecast horizons, from 1 to 5 min: a pattern recognition neural network and a Random Forest model. The proposed methods are tested and compared on a real case study: available data consists of historical irradiance measurements and infrared sky images collected in a real PV facility, the SolarTech(LAB) in Politecnico di Milano. The classification output is a range of values corresponding to the future value assumed by the Clear Sky Index (CSI), an indicator allowing to account for irradiance variations only related to clouds passage, neglecting diurnal and seasonal influences. The developed models present similar performance in all the considered time horizons, reliably detecting the CSI drops caused by incoming overcast and partially cloudy sky conditions.

关键词： clear sky index random forest pattern recognition neural network nowcasting all-sky-cam

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On Possibilities of Human Head Detection for Person Flow Monitoring System 15th

On Possibilities of Human Head Detection for Person Flow Mon...

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15th International Work-Conference on Artificial neural networks (IWANN)

作者： Dolezel, Petr Stursa, Dominik Skrabanek, Pavel Univ Pardubice Fac Elect Engn & Informat Pardubice Czech Republic Brno Univ Technol Inst Automat & Comp Sci Brno Czech Republic

ISBN: (纸本)9783030205188;9783030205171

Along with the development of human society, economy, industry and engineering, as well as with growing population in the world's biggest cities, various approaches to person detection have become the subject of great interest. One approach to developing a person detection system is proposed in this paper. A high-angle video sequence is considered as the input to the system. Then, three classification algorithms are considered: support vector machines, pattern recognition neural networks and convolutional neural networks. The results showed very little difference between the classifiers, with the overall accuracy more than 95% over a testing set.

关键词： Person flow monitoring Support vector machines pattern recognition neural network Convolutional neural network Histograms of oriented gradients

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Behavior Analysis for Nursing Home Monitoring System 1st

Behavior Analysis for Nursing Home Monitoring System

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1st International Conference on Big Data Analysis and Deep Learning (ICBDL)

作者： Seint, Pann Thinzar Zin, Thi Thi Univ Miyazaki Grad Sch Engn Miyazaki Japan

ISBN: (纸本)9789811308697;9789811308680

In this paper, we describe the nursing home monitoring system based on computer vision. This system is aimed for an effective and automatic take care of aged persons to monitor appropriate medication intake. Skin region detection for mouth and hand tracking and color label detection for water and medication bottles tracking are mainly performed for initialization. To differentiate the hand and face region, we use the regional properties of head with online learning. Tracking is done by the minimum Eigen values detection. The overlapping area ratios of desired object to body parts are simply used as feature vectors and pattern recognition neural network is proposed for the decision of simplest action. This paper presents the 7 types of simple actions recognition for medication intake and our experimental results give the promising results.

关键词： Medication intake monitoring Mouth and hand tracking Eigen values Feature vectors pattern recognition neural network

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