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A Comparative Study between machine learning algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors

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ENERGIES 2019年第11期12卷 2105页

作者： Pandarakone, Shrinathan Esakimuthu Mizuno, Yukio Nakamura, Hisahide Nagoya Inst Technol Dept Elect & Mech Engn Nagoya Aichi 4668555 Japan TOENEC Corp Res & Dev Div Nagoya Aichi 4570819 Japan

Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.

关键词： induction motor minor bearing fault frequency spectrum analysis machine learning algorithm artificial intelligence

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Detection of Unfocused EEG Epochs by the Application of machine learning algorithm

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SENSORS 2024年第15期24卷 4829页

作者： Akhter, Rafia Beyette, Fred R. Univ Georgia Coll Engn Dept ECE Athens GA 30602 USA

Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. The time-locked EEG to an external event is known as event-related potential (ERP). ERP can be a biomarker of human perception and other cognitive processes. The success of ERP research depends on the laboratory conditions and attentiveness of the test subjects. Specifically, the inability to control experimental variables has reduced ERP research in the real world. This study collected EEG data under various experimental circumstances within an auditory oddball paradigm experiment to enable the use of ERP as an active biomarker in normal laboratory conditions. Then, ERP epochs were analyzed to identify unfocused epochs, affected by typical artifacts and external distortion. For the initial comparison, the ability of four unsupervised machine learning algorithms (MLAs) was evaluated to identify unfocused epochs. Then, their accuracy was compared with the human inspection and a current EEG analysis tool (EEGLab). All four MLAs were typically 95-100% accurate. In summary, our analysis finds that humans might miss subtle differences in the regular ERP patterns, but MLAs could efficiently identify those. Thus, our analysis suggests that unsupervised MLAs perform better for detecting unfocused ERP epochs compared with the other two standard methods.

关键词： electroencephalography distraction artifacts machine learning algorithm oddball paradigm human visual inspection EEGLab

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Research on wind turbine icing prediction data processing and accuracy of machine learning algorithm

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RENEWABLE ENERGY 2024年 237卷

作者： Zhang, Lidong Zhao, Yuze Guo, Yunfeng Hu, Tianyu Xu, Xiandong Zhang, Duanmei Song, Changpeng Guo, Yuanjun Ma, Yuanchi Northeast Elect Power Univ Sch Energy & Power Engn Jilin 132012 Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China Univ Manchester Sch Engn Manchester M13 9PL England Key Lab Smart Energy & Informat Technol Tianjin Mu Tianjin 300072 Peoples R China Changchun Inst Technol Coll Jilin Emergency Management Changchun 130021 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Guangdong Peoples R China

Studying the icing problem of wind turbine blades is crucial for optimizing wind farm operation and maintenance. Traditional machine learning algorithms like Random Forest and Support Vector machines have limitations in handling complex time series data and capturing long-term dependencies, leading to insufficient accuracy and generalization. To address these gaps, this paper proposes a comprehensive approach involving advanced machine learning frameworks and feature engineering techniques. This paper based on the original SACDA dataset, four distinct types of processing are performed on the prediction data via PCA dimensionality reduction and the introduction of new features;meanwhile, the four types mentioned above of datasets are trained and predicted using the Gated Recycling Unit model (GRU), Random Forest (RF), GA-BP Neural Network (BP), and Extreme learning machine (ELM) models. The results indicate that the prediction accuracies of all four types of models are more satisfactory, with the RF model having the highest prediction accuracy and the overall accuracy remaining above 99 percent, the dataset + RF model after dimensionality reduction of the original sensitive features having the highest prediction accuracy and speed.

关键词： Wind turbine Icing prediction PCA dimension reduction machine learning algorithm

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A Comparison of the machine learning algorithm for Evaporation Duct Estimation

A Comparison of the Machine Learning Algorithm for Evaporati...

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作者： Yang, C. Brno University of Technology

In this research, a comparison of the relevance vector machine (RVM), least square support vector machine (LSSVM) and the radial basis function neural network (RBFNN) for evaporation duct estimation are presented. The parabolic equation model is adopted as the forward propagation model, and which is used to establish the training database between the radar sea clutter power and the evaporation duct height. The comparison of the RVM, LSSVM and RBFNN for evaporation duct estimation are investigated via the experimental and the simulation studies, and the statistical analysis method is employed to analyze the performance of the three machine learning algorithms in the simulation study. The analysis demonstrate that the M profile of RBFNN estimation has a relatively good match to the measured profile for the experimental study; for the simulation study, the LSSVM is the most precise one among the three machine learning algorithms, besides, the performance of RVM is basically identical to the RBFNN.

关键词： machine learning algorithm evaporation duct radar sea clutter parameter estimation.

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DDoS Attack Detection Model using machine learning algorithm in Next Generation Firewall

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Procedia Computer Science 2024年 233卷 175-183页

作者： Manthan Patel P.P. Amritha Vinay B Sudheer M. Sethumadhavan TIFAC - CORE in Cyber Security Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

Next generation firewall is taking major part to secure network environment in the industry. This device will monitor all the traffic which is coming inside the network or going outside of the network. With all these security devices attackers can still perform various kind of attacks on the network. DDoS attack is one of the hardest attack to identify which will send packets to the network and which will look like normal traffic but it will act as a DDoS traffic. In this paper we used binary decision tree, XGBoost and support vector machine to identify DDoS attack traffic pattern from the different features in the packet header. Data will be fetched from the packet header and among them standard deviation of the packet bytes and packet flows are the features considered. We have applied this data on the trained dataset. algorithm will predict whether the traffic coming inside the network is trusted or not. Out of the three algorithms Binary Decision tree algorithm is giving 99 percentage of accuracy and will predict the data as fast as possible. Here priority is to filter DDos attacks of any security level in the line speed of the NIDS or any other appliances. This method of DDoS attack detection will add extra layer of security in the next generation firewall which will make firewall more robust.

关键词： Next Generation Firewall Distributed Denial-of-Service Attack machine learning algorithm

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Flood susceptibility assessment of the Agartala Urban Watershed, India, using machine learning algorithm

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第2期196卷 110-110页

作者： Debnath, Jatan Debbarma, Jimmi Debnath, Amal Meraj, Gowhar Chand, Kesar Singh, Suraj Kumar Kanga, Shruti Kumar, Pankaj Sahariah, Dhrubajyoti Saikia, Anup Gauhati Univ Dept Geog Gauhati 781014 Assam India Tripura Univ Dept Geog & Disaster Management Agartala Tripura India Tripura Univ Dept Forestry & Biodivers Agartala Tripura India Univ Tokyo Dept Ecosyst Studies Bunkyo City Tokyo Japan GB Pant Natl Inst Himalayan Environm NIHE Ctr Environm Assessment & Climate Change Himachal Reg Ctr Himachal Pradesh Kulu India Suresh Gyan Vihar Univ Ctr Sustainable Dev Jaipur Rajasthan India Cent Univ Punjab Dept Geog Bathinda India Inst Global Environm Strategies Hayama Japan

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive machine learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.

关键词： Agartala urban watershed Decision tree Flood susceptibility machine learning algorithm Random forest

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Single resistive sensor for selective detection of multiple VOCs employing SnO2 hollowspheres and machine learning algorithm: A proof of concept

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SENSORS AND ACTUATORS B-CHEMICAL 2020年 321卷 128484-128484页

作者： Acharyya, Snehanjan Jana, Biswabandhu Nag, Sudip Saha, Goutam Guha, Prasanta Kumar Indian Inst Technol Kharagpur Adv Technol Dev Ctr Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

Selective detection of harmful gasses and volatile organic compounds (VOCs) in the ambient has become a major challenge. Primarily, semiconducting metal-oxide based gas sensors sense various gases simultaneously, hence their selectivity is poor. This paper presents a single chemiresistive metal-oxide gas sensor for identification of multiple VOCs accurately by employing highly sensitive microstructure and machine learning tools. Tin oxide (SnO2) hollowspheres were taken as sensing material that were prepared through optimized hydrothermal route. Different characterizations were carried out to confirm the formation of desired morphology and structural features. The sensor device was fabricated by controlled drop cast technique over gold based interdigitated electrodes. The sensor showed remarkable response towards the target VOCs with high sensitivity and fast recovery time. Incorporation of machine learning algorithm on the obtained sensor data provided accurate identification of all the VOCs (best performance shown by random forest). In addition, the quantitative prediction of gas concentration was performed for each target gas using regression model. In comparison to e-noses (having array of sensors with different sensing material), a single chemiresistive metal-oxide sensor with proper machine learning tool is simple, economic, compact and easy to fabricate.

关键词： Indoor air quality Volatile organic compound Selectivity Chemiresistive gas sensor Tin oxide hollowspheres machine learning algorithm

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A New Approach for Surface Urban Heat Island Monitoring Based on machine learning algorithm and Spatiotemporal Fusion Model

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IEEE ACCESS 2020年 8卷 164268-164281页

作者： Yao, Yuan Chang, Cun Ndayisaba, Felix Wang, Shuang Chengdu Univ Sch Architecture & Civil Engn Chengdu 610106 Peoples R China Chengdu Univ Inst Higher Educ Sichuan Prov Key Lab Pattern Recognit & Intelligent Informat P Chengdu 610106 Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog State Key Lab Desert & Oasis Ecol Urumqi 830011 Peoples R China Univ Ghent Fac Biosci Engn B-9000 Ghent Belgium Protestant Inst Arts & Social Sci Dept Geog Huye Rwanda Xinjiang Agr Univ Sch Management Urumqi 830000 Peoples R China

Land surface temperature (LST) is an important indicator for assessing the surface urban heat island (SUHI) effect. This paper presents a novel approach to derive LST estimates by integrating machine learning algorithm and spatiotemporal fusion model at high spatial and temporal resolution. The spatial resolutions of Landsat TM and Landsat 8 LST data were first downscaled using random forest (RF) algorithm from 120 m and 100 m, respectively, to 30 m. The resultant LST data were fused with MODerate-resolution Imaging Spectroradiometer (MODIS) LST data, by means of the Flexible Spatiotemporal Data Fusion method (FSDAF), in order to generate high spatiotemporal resolution summer daytime LST data covering the center of Chengdu city in China. The proposed new method was used to estimate the spatiotemporal variations of the summer daytime SUHI from 2009 to 2018 over Chengdu city. Results show that: (1) RF performs way better than the classical downscaling algorithm-thermal sharpening algorithm (TsHARP) for LST, and produces higher accuracy for different land covers;(2) the fused high spatiotemporal resolution summer daytime LST values were evaluated with in situ LST obtained from Chengdu Meteorological Office and the final validation results indicated that the proposed method, in generating LST dataset, can provide more details of urban thermal environment and produce higher accuracy than the traditional FSDAF;(3) significantly increasing trends of summer daytime SUHI intensity (SUHII) in the study area were observed. SUHII increased from 2.78 degrees C in 2009 to 4.04 degrees C in 2018. The highest and lowest summer daytime LST estimates were recorded over impervious surface area (ISA) and waters, respectively.

关键词： Remote sensing Earth Artificial satellites Spatial resolution Land surface temperature Urban areas Spatiotemporal phenomena machine learning algorithm land surface temperature spatiotemporal fusion surface urban heat island (SUHI)

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Prediction of the slurry pressure and inversion of formation characteristics based on a machine learning algorithm during tunnelling in a fault fracture zone

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2024年 144卷

作者： Shen, Xiang Chen, Yifan Cao, Liqiang Chen, Xiangsheng Fu, Yanbin Hong, Chengyu Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Peoples R China Beijing Univ Technol Inst Geotech & Underground Engn Beijing 100124 Peoples R China Shenzhen Univ Key Lab Coastal Urban Resilient Infrastruct MOE Shenzhen 518060 Peoples R China Shenzhen Key Lab Green Efficient & Intelligent Con Shenzhen 518060 Guangdong Peoples R China

The reasonable setting of the slurry pressure is very important for the safety of shield tunnel construction. In view of the common geological problems of high water pressure and multiple fracture zones in water conveyance tunnels in China, the instability mechanism of the shield excavation face in these formations remains unclear. In this paper, based on the Pearl River Delta Water Resources Allocation Project, various machine learning algorithms are introduced, and the predicted values of the slurry pressure obtained with different machine learning algorithms are evaluated and compared. The optimal machine learning algorithm suitable for this project is determined. Then, using this algorithm, considering the influence of the fault fracture zone, the concept of the formation influence coefficient is proposed, which is used as the input of the prediction model. The influences of the formation coefficient distribution and the influence width of the fracture zone on the accuracy of slurry pressure prediction are examined. Finally, based on this model, the slurry pressure of nearly 100 groups is predicted and verified. The results show that (1) if the influence of the fault fracture zone is considered and a reasonable influence width of the fault fracture zone can be determined, the mean absolute error (MAE) of the predicted slurry pressure is much higher than that of the predictions without considering the fault fracture zone, and the prediction accuracy can be improved by approximately 25%;(2) when the formation influence coefficient is obtained via the method of the theoretical distribution and the influence width of the fault fracture zone is 17 rings, the prediction accuracy of the slurry pressure reaches the optimal value;and (3) through on-site refined investigation, it can be determined that the influence width of the fault fracture zone is 19 rings. The research results of this paper can provide an effective method for setting the shield slurry pressure under

关键词： Shield tunnel Fault fracture zone machine learning algorithm Slurry pressure Prediction and analysis

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Estimation of economic seismic loss of steel moment-frame buildings using a machine learning algorithm

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ENGINEERING STRUCTURES 2022年 254卷 113877-113877页

作者： Hwang, Seong-Hoon Mangalathu, Sujith Shin, Jinwon Jeon, Jong-Su Kumoh Natl Inst Technol Dept Architectural Engn Gumi 39177 South Korea Mangalathu Res Data Scientist Data Analyt Puthoor Po 691507 Kerala India Catholic Kwandong Univ Dept Architectural Engn Kangnung 25601 South Korea Hanyang Univ Dept Civil & Environm Engn Seoul 04763 South Korea

In this study, the effect of modeling-related uncertainties on the expected annual losses of modern code-compliant steel moment-frame buildings is analyzed. Probabilistic structural models are initially employed to account for all the critical sources of uncertainty. Then, these structural models are used to develop machine-learning-based prediction models to estimate the expected annual losses and the associated economic contrib-utors;the developed machine-learning-based prediction models exhibit an excellent performance in the pre-diction of the economic seismic losses of steel frame buildings. The effect of structural-modeling-related uncertainties on each loss contributor is also evaluated;the effect of uncertain modeling parameters is observed to be more pronounced on loss contributors such as demolition and structural collapse losses that are controlled primarily by ground motions with a low probability of earthquake occurrence.

关键词： Expected annual losses machine learning algorithm Structural-modeling-related uncertainty Steel moment-frame buildings Seismic risk

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