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Research on Fault Diagnosis Method of Electro-Hydrostatic Actuator

Lei Zhufeng, Qin Lvjun, Wu Xiaodong, Jin Wen, Wang Caixia, Ke Feng
2021 Shock and Vibration  
Then, a fault identification model of hydraulic system based on support vector machine is established.  ...  based on the statistical features of the time domain.  ...  Acknowledgments Wei Hongbo and Zhang Yifei are acknowledged for their valuable technical support. is work was financially supported by the Science Foundation of Xi'an Aeronautical University (no. 2020KY0223  ... 
doi:10.1155/2021/6688420 fatcat:prxarhzikfhmtfyllg35uanzym

Review of local mean decomposition and its application in fault diagnosis of rotating machinery

2019 Journal of Systems Engineering and Electronics  
Local mean decomposition (LMD) is an adaptive mode decomposition method that can decompose a complicated signal into a series of mono-components, namely product functions (PFs).  ...  In recent years, many researchers have adopted LMD in fault detection and diagnosis of rotating machines. We give a comprehensive review of LMD in fault detection and diagnosis of rotating machines.  ...  [126] introduced a novel intelligent method by combining LMD, PCA and SVM to detect leakage in natural gas pipelines. Huang et al.  ... 
doi:10.21629/jsee.2019.04.17 fatcat:3a3ovet3c5gt5owmdbfbkief74

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Mutiu Adesina Adegboye, Aditya Karnik
2019 Sensors  
However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature.  ...  In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed.  ...  Acknowledgments: The authors acknowledge the supports of Petroleum Technology Development Fund (PTDF), Abuja Nigeria for Providing Funding Under its Overseas Scholarship scheme.  ... 
doi:10.3390/s19112548 fatcat:w3nio2nrbrhpfjq7me5tbf3pfq

IoTbasedOptimalLiquidMetalPipelineDamage (1).pdf

ganesh E N
In data processing unit, we introduce a hybrid cat hunting based neural network (hybrid CHNN) to detect and localize the pipeline damages/cracks to avoid unwanted leakage and accidents.  ...  These pipelines are often vulnerable to natural and third-party events such as explosions, earthquakes, explosions, drilling and vehicle traffic.  ...  A pipeline leak detection method has been proposed for circular investigation based on a combination of variation mode decomposition (VMD) and support vector machine (SVM) [27] .  ... 
doi:10.6084/m9.figshare.20079908.v1 fatcat:dvpiibnynbagjd7cx2glzcfw3y

Research on Pattern Recognition Method of Blockage Signal in Pipeline Based on LMD Information Entropy and ELM

Jingzong Yang, Xiaodong Wang, Zao Feng, Guoyong Huang
2017 Mathematical Problems in Engineering  
a pattern recognition method based on local mean decomposition (LMD), information entropy theory, and extreme learning machine (ELM).  ...  Secondly, based on the information entropy theory, the appropriate energy entropy, singular spectrum entropy, power spectrum entropy, and Hilbert spectrum entropy were extracted as the input feature vectors  ...  Today, the acoustic detection method based on impulse response has been preliminarily applied in industries, such as the quality control of pipeline in chemical engineering, oil, and natural gas.  ... 
doi:10.1155/2017/5321815 fatcat:m2r4ekk7lneztmmn73iyzbab6u

An Integration Method Using Kernel Principal Component Analysis and Cascade Support Vector Data Description for Pipeline Leak Detection with Multiple Operating Modes

Mengfei Zhou, Qiang Zhang, Yunwen Liu, Xiaofang Sun, Yijun Cai, Haitian Pan
2019 Processes  
Pipelines are one of the most efficient and economical methods of transporting fluids, such as oil, natural gas, and water.  ...  The conventional methods for pipeline leak detection generally need to extract the features of leak signal to establish a leak detection model.  ...  Acknowledgments: Lei Xie is acknowledged for his valuable technical support. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/pr7100648 fatcat:pe7bg23jm5htzdfsbkfbckvx2e

A Method for Distributed Pipeline Burst and Leakage Detection in Wireless Sensor Networks Using Transform Analysis

Sidra Rashid, Saad Qaisar, Husnain Saeed, Emad Felemban
2014 International Journal of Distributed Sensor Networks  
Bursts and leakages have turned out to be one of the most frequent malfunctions in liquid pipeline distribution systems.  ...  The proposed algorithm is distributed in nature and run on low power sensor nodes.  ...  Acknowledgments This research was supported by King Abdul Aziz City for Science and Technology (KACST), Saudi Arabia, Grants NPST-11-INF1688-10 and NPST-10-ELE1238-10 and National ICTRDF, Pakistan, Grant  ... 
doi:10.1155/2014/939657 fatcat:zz4yzi3trva55adwi5itx2gk2y

Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review

Javier Tejedor, Javier Macias-Guarasa, Hugo Martins, Juan Pastor-Graells, Pedro Corredera, Sonia Martin-Lopez
2017 Applied Sciences  
This paper presents a review of the literature in what respect to machine learning techniques applied to pipeline surveillance systems based on DAS+PRS (although its scope can also be extended to any other  ...  Additionally, this paper addresses the most common issues related to real field deployment and evaluation of DAS+PRS for pipeline threat monitoring, and intends to provide useful insights and recommendations  ...  Author Contributions: Javier Tejedor and Javier Macias-Guarasa designed the structure of the paper, were responsible for the description of the principles of the machine learning applied to DAS, the related  ... 
doi:10.3390/app7080841 fatcat:4t6zzbvqdrgfph3bppfmjgsixy

Review of Vibration-Based Structural Health Monitoring Using Deep Learning

Gyungmin Toh, Junhong Park
2020 Applied Sciences  
When the vibration is used for extracting features for system diagnosis, it is important to correlate the measured signal to the current status of the structure.  ...  Consequently, the diagnosis using vibration requires complete understanding of the extracted features to discard the influence of surrounding environments or unnecessary variations.  ...  Figure 9 . 9 The ten-fold-cross evaluation for the proposed method (1-D convolutional neural network (CNN) + support vector machine (SVM)) and the conventional machine learning method (handcrafted feature  ... 
doi:10.3390/app10051680 fatcat:4vgiycrznvgcjfsv6fshlc3seq

Data Driven Seal Wear Classifications using Acoustic Emissions and Artificial Neural Networks

Nadia. S. Noori, Vignesh. V. Shanbhag, Surya. T. Kandukuri, Rune Schlanbusch
2022 Proceedings of the European Conference of the Prognostics and Health Management Society (PHME)  
We benchmark the developed method against previous work conducted based on Support Vector Machine (SVM), and we compare ANN performance in classifying the running condition of seals in hydraulic cylinders  ...  The work presented in this paper is built on a series of experiments aiming to develop a data-driven and automated method for seal diagnostics using Acoustic Emission (AE) features.  ...  (Kandukuri et al. 2021 ) used AE and the support vector machine (SVM) for classifying unworn, semiworn and worn seals.  ... 
doi:10.36001/phme.2022.v7i1.3327 fatcat:ho34cbeuk5gsdngiu2nuxttvme

Recent Advancements in AI-Enabled Smart Electronics Packaging for Structural Health Monitoring

Vinamra Bhushan Sharma, Saurabh Tewari, Susham Biswas, Bharat Lohani, Umakant Dhar Dwivedi, Deepak Dwivedi, Ashutosh Sharma, Jae Pil Jung
2021 Metals  
Moreover, the Internet of Things (IoT) and smart city concepts are explained to elaborate on the contributions of intelligent SHM systems.  ...  Three smart data capturing methods of SHM, namely, camera-based, smartphone-based, and unmanned aerial vehicle (UAV)-based methods, are also discussed, having made the utilization of intelligent paradigms  ...  It utilized the Sequential Feature Selection paradigm (SFSA) for the feature extraction and three machine learning techniques, namely, Gaussian Mixture Models (GMM), One-Class Support Vector Machine (OCSVM  ... 
doi:10.3390/met11101537 fatcat:wayx7vsxxrekrgpjrzrmxpqswm

Pipeline Leak Detection Systems and Data Fusion: A Survey [article]

Uthman Baroudi, Abdullah Devendiran, Anas Al-Roubaiey
2019 arXiv   pre-print
A comparison of LDSs is performed based on well-defined criteria. We have classified and critically reviewed these techniques.  ...  The pipeline leakage problem is very challenging and critical issue. Solving this problem will save the nation a lot of money, resources and more importantly, it will save the environment.  ...  Acknowledgments The authors would like to acknowledge the support provided by King Abdulaziz City for Science and Technology  ... 
arXiv:1902.03927v1 fatcat:pv324yq6pfh7pgngwgcfupgrku

Failure Detection Methods for Pipeline Networks: From Acoustic Sensing to Cyber-Physical Systems

Boon Wong, Julie A. McCann
2021 Sensors  
for pipeline failures, such as blockages, leakages, cracks, corrosion and weld defects.  ...  Pipeline networks have been widely utilised in the transportation of water, natural gases, oil and waste materials efficiently and safely over varying distances with minimal human intervention.  ...  Author Contributions: B.W. contributed to the study design, data collection and analysis, and writing of the manuscript. J.A.M. contributed to the data analysis and writing of the manuscript.  ... 
doi:10.3390/s21154959 fatcat:sgabwonzx5ewbh6oz3nnvwqtfa

Review of Underground Storage Tank Condition Monitoring Techniques

Ooi Ching Sheng, Wai Keng Ngui, Hui Kar Hoou, Lim Meng Hee, Mohd. Salman Leong, Lim Meng Hee
2019 MATEC Web of Conferences  
As an alternative means to deliver spatial information on structural integrity, the feasibility of integrating nondestructive evaluation (NDE) techniques with machine learning algorithms, on observing  ...  Recently, attention has been drawn to the safety risks of the complex cylindrical-shaped system and its surrounding environment due to contamination resulting from unwanted subsurface leakage.  ...  Based on the above reasoning, the following research concentrates on the necessity and fulfilment of machine learning algorithms by making use of NDE signal- extracted features.  ... 
doi:10.1051/matecconf/201925502009 fatcat:ylv2tc24sfdktcj3zcge3t573m

Structural Health Monitoring in Composite Structures: A Comprehensive Review

Sahar Hassani, Mohsen Mousavi, Amir H. Gandomi
2021 Sensors  
Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures.  ...  Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids.  ...  Algorithms Features Refs -Mode shapes and stiffness matrix [209] GA -Natural frequencies [210,211] -Natural frequencies and accelerations [212] DE -Mode shapes -Natural frequencies and mode shape [213]  ... 
doi:10.3390/s22010153 pmid:35009695 pmcid:PMC8747674 fatcat:fnz2qe7hd5acxfsyrouw53fa5e
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