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第八届国际振动工程会议

作者： Yifan Bao Jianfei Yao Jiabao Dai Yan Li Beijing Key Laboratory of Health Monitoring and Self-recoverying for High-end Mechanical Equipment Beijing University of Chemical Technology College of Mechanical and Electrical Engineering Beijing University of Chemical Technology Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical Technology Engineering Research Center of Chemical Safety Ministry of Education Beijing University of Chemical Technology

Rotor vibration caused by various faults is inevitable in the operation of rotating machinery, it is vital to reduce the rotor vibration considering the various excitations. In this paper,firstly, a finite element model of the rotor-bearing system mounted with active magnetic actuator（AMA） is established, and the models of unbalance and parallel misalignment faults are ***, aiming at the unbalance-misalignment coupled fault and dynamic uncertainties of AMA in the rotor-bearing system, an active suppression method based on robust H∞ control is ***, an active suppression method of rotor vibration based on hybrid sensitivity H∞ algorithm is designed for use in AMA. Finally, the simulation results show that the method can effectively suppress the rotor vibration induced by the multiple excitations such as unbalance and parallel misalignment.

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An Improved Initial Parameter Setting Method for Variational Time-Domain Decomposition

An Improved Initial Parameter Setting Method for Variational...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： He Li Jiniie Zhang Zhinong Jiang Zhiwei Mao Key Laboratory of Engine Health Monitoring Control and Networking Ministry of Education Beijing University of Chemical Technology Beijing China State Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical Technology Beijing China

Effectively decomposing vibration signals is crucial for identifying fault features and evaluating the performance of mechanical systems. Reciprocating mechanical vibration signals typically exhibit complex non-stationary and multi-impact characteristics, posing challenges in signal decomposition. Currently, in response to this issue, the Variational Time-Domain Decomposition (VTDD) method has been proposed to achieve outstanding adaptive impact separation in reciprocating mechanical vibration signals. However, VTDD has limitations in the initial parameter value settings, particularly when dealing with reciprocating mechanical signals under strong noise conditions, which result in slow convergence and less accurate decomposition. To address these limitations, this paper proposes an improved initial parameter value setting method based on a noise threshold-driven impact center estimation algorithm. This method preserves the impacts from vibration signals by estimating the noise threshold, then accurately estimates the number and positions of impacts, which is applied to replace the original VTDD parameter setting approach. Experimental results demonstrate that this improved method delivers excellent decomposition outcomes and applicability for both simulated and real mechanical vibration signals. In summary, by enhancing the initial parameter value setting, the VTDD method performance provides a more reliable tool for vibration signal analysis, especially in the challenging task of recognizing complex impact signals.

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A Research Method of Vibration Stationarity Based on Correlation Coefficient

A Research Method of Vibration Stationarity Based on Correla...

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Data Driven control and Learning Systems (DDCLS)

作者： Bo Ma Libing Liang Zeyong Tao Weidong Cai Beijing Key Laboratory of Health Monitoring control and Fault Self-Recovery for High-end Mechanical Equipment Beijing University of Chemical Technology Beijing Key Laboratory of Engine Health Monitoring and Networking Ministry of Education Beijing University of Chemical Technology Beijing 100029 Compressor Health Intelligent Monitoring Center State Key Laboratory of Compressor Technology Beijing State Nuclear Power Plant Service Company Shanghai

ISBN: (数字)9781728159225

ISBN: (纸本)9781728159232

Due to the influence of various factors during the operation of mechanical equipment, the vibration appears the characteristics of non-stationarity. In order to analyze the correlation of other monitoring indicators that cause vibration non-stationarity, it is necessary to study the vibration stationarity. A research method of vibration stationarity based on correlation coefficient is proposed in this paper. Firstly, the correlation coefficient of vibration data and corresponding time is calculated by sliding to reflect the vibration trend. Secondly, the fluctuation threshold is determined by statistical characteristics. Finally, the vibration stationarity is tested. The test results of vibration data of nuclear main pump show that the method proposed in this paper can better test the vibration stationarity, which lays a foundation for the study of the factors related to the operating states of mechanical equipment.

关键词： Vibrations Correlation coefficient Market research Fluctuations Correlation Vibration measurement Shafts

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Intelligent Fault Diagnosis for Unknown Faults of Rotating Machinery based on the CNN and the DCGAN

Intelligent Fault Diagnosis for Unknown Faults of Rotating M...

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Data Driven control and Learning Systems (DDCLS)

作者： Gongye Yu Yapeng You Bo Ma Yongming Han Key Laboratory of Engine Health Monitoring-Control and Networking (Ministry of Education) Beijing University of Chemical Technology Beijing China Beijing Key Laboratory of Health Monitoring and Self-recovery for High-end Mechanical Equipment Beijing University of Chemical Technology Beijing China College of Information Science and Technology Beijing University of Chemical Technology Beijing China

The fault diagnosis model based on machine learning can only achieve accurate recognition of the fault types included in the training, but in practical applications, it is limited by the classification mechanism of the diagnosis model and cannot achieve the recognition of new faults, which is unknown faults. To address this problem, this paper proposes a fault identification method for rotating machinery based on the Convolutional Neural Networks (CNN) and the Deep Convolutional Generative Adversarial Network (DCGAN) to identify unknown faults of rotating machinery. The method first trained CNN with each known class data to build the initial diagnosis model, and trained DCGAN to obtain the discriminative network of each known class to build the confidence probability calculation model, and then the results of the initial diagnosis were corrected according to the confidence probability, and finally the intelligent diagnosis of the known and unknown class faults was realized. The analysis results of the centrifugal pump fault simulation showed that the proposed method achieved an average diagnostic accuracy of 96.16% and 91.79% for known and unknown faults, respectively.

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A Mixed Visco-Elastohydrodynamic Lubrication Model Amended by the 3d Deformation Velocities and Wear Prediction Based on it

SSRN

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SSRN 2024年

作者： Wang, Zijia Zhang, Jinjie Wang, Huailei Guo, Dan Zuo, Koucheng Mao, Zhiwei State Key Laboratory of Tribology in Advanced Equipment Tsinghua University Beijing100084 China National Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical Technology Beijing100029 China Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical Technology Beijing100029 China

The viscoelasticity of journal bearing under dynamic load cannot be ignored in mixed lubrication. This study proposes the three-dimensional (3D) deformation velocities caused by viscoelasticity to amend the bearing surface velocity and derives a generalized average Reynolds equation based on flow conservation. Therefore, a new mixed visco-elastohydrodynamic lubrication model amended by 3D deformation velocities is built and a nested solution method for it is proposed. Further, the lubrication model is coupled with the Archard model to develop the wear prediction. The viscoelastic parameter of the bearing is experimentally measured, and the numerical simulations of lubrication and wear are developed for the ordinary bearing under simple dynamic load conditions and the crankshaft main bearing of the internal combustion engine (ICE) under impact. The simulation results show that the deformation velocities reduce the extrusion effect and increase the contracting effect in the lubrication flow field. This mechanism enables the new model to more accurately identify the micro asperity contact, impact, and early wear features of bearing than the traditional model. Furthermore, the increase in load of ICE intensifies early wear of bearing. The research results provide valuable methods and references for the theoretical analysis and wear fault diagnosis of journal bearing. © 2024, The Authors. All rights reserved.

关键词： Viscoelasticity

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Research on Motor Fault Identification Method Based on Vibration Current Multisource Data Fusion and Deep Learning

Research on Motor Fault Identification Method Based on Vibra...

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Prognostics and System health Management Conference (PHM-Qingdao)

作者： Yiyi He Kun Feng Baoxia Liu Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical Technology Beijing China State Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical Technology Beijing China National pipe networknorth company pipeline compressor unit maintenance center Beijing University of Chemical Technology Beijing China

Traditional motor fault diagnosis techniques are usually based on a single type of state parameter. However, the monitoring range of a single type of motor state parameters is very limited, and it is difficult to meet the needs of comprehensive fault diagnosis. The purpose of this paper is to propose a fault diagnosis method for typical AC induction motors by fusing two types of parameters, i. e. vibration data and current data, in order to improve the accuracy of diagnosis. Based on the analysis of the basic faults of typical AC asynchronous motors, this paper studies the methods of motor fault feature extraction, including Fast Fourier Transform (FFT) and Hilbert-Huang Transform (HHT). Then, the method of motor multi-sensor parameter fusion is studied, and the realization scheme of data fusion is designed. Convolutional neural network is constructed as a feature fusion tool. The results of fast Fourier transform (FFT) of current data and Hilbert-Huang transform (HHT) of voltage data of different fault types are input into CNN as fault features, and the deep-seated features are extracted by using CNN's feature extraction ability. Finally, the CNN network series Multilayer perceptron (MLP) was constructed to realize fault classification, and the motor fault identification and fault diagnosis were realized based on the motor vibration and current parameters. The results show that the motor fault diagnosis technology based on data fusion can improve the accuracy of diagnosis results, reduce the uncertainty caused by single parameter, and improve the accuracy of motor fault diagnosis.

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Fault Diagnosis Method for Blade Fracture of Gas Turbine Based on Casing Vibration

SSRN

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SSRN 2023年

作者： Hu, Ming-Hui Liu, Shao-Peng Wang, Hao Zou, Li-Min Wang, Wei-Ming Jiang, Zhi-Nong State Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical Technology Beijing100029 China Beijing Key Laboratory of Health Monitoring and Self-Recovery for High-End Mechanical Equipment Beijing University of Chemical Technology Beijing100029 China Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical Technology Beijing100029 China China Ship Research and Development Academy Beijing100101 China

Blade fracture is one of the most difficult faults to diagnose in rotating machinery. Most gas turbines can only measure casing vibration and cannot measure shaft vibration, making monitoring and diagnosing such faults even more challenging. Research on vibration characteristics and blade fracture fault diagnosis methods is crucial to ensure the safe and reliable operation of gas turbines. Firstly, based on the function mechanism of blade wake excitation forces, the transfer path of the vibration response of gas turbine blades and the variation rule of the wake excitation force have been investigated. Secondly, based on the casing vibration response model, we analyzed the variation rule of vibration response before and after rotor blade fracture. The results indicate that the response characteristics of velocity RMS, blade passing frequency, and fundamental frequency can effectively characterize blade fracture faults. We propose an Improved Ordered Cluster Analysis (IOCA) identification method based on this foundation. Further, based on blade fracture results in a decrease in the amplitude of the blade pass frequency and the appearance of the harmonic sideband, we propose a fault localization method(FLM), which constructs a fracture localization factor called and the highest value corresponds to the stage where the blade fracture has appeared. Above all, a fault diagnosis scheme for gas turbine blade fracture based on casing vibration is proposed. At last, an engineering case of a gas turbine blade fracture fault is analyzed. The results show that this method can timely and effectively alarm when a blade fracture fault appears, and the location of the fracture can be accurately determined. © 2023, The Authors. All rights reserved.

关键词： Gas turbines

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Research on Intelligent Diagnosis of Wear Faults of Centrifugal Pumps Based on Stacked Autoencoder

Research on Intelligent Diagnosis of Wear Faults of Centrifu...

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Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Mingsheng Xiang Yingli Li Kun Feng Key Laboratory of Engine Health Monitoring-Control and Networking Ministry of Education Beijing University of Chemical Technology Beijing People's Republic of China China Petroleum Safety and Environmental Protection Technology Research Institute Beijing People's Republic of China Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing University of Chemical Technology Beijing People's Republic of China

Mechanical fault diagnosis is very important in industry because early detection can avoid some dangerous situations, and not much research has been done on the diagnosis of wear faults in centrifugal pumps. With the rapid development of data analysis techniques, data-driven diagnosis methods are becoming increasingly popular. In this study, stacked autoencoder based method is proposed to solve the centrifugal pump seal wear fault diagnosis problem. The method extracts power spectral density features directly from the vibration signal and chunks the features, greatly reducing the training difficulty and improving the accuracy of the model. The effectiveness of the method is verified using a centrifugal pump dataset, and the results show that the method can diagnose not only the fault site but also the degree of wear.

关键词： Fault diagnosis Vibrations Training Industries Data analysis Pumps Seals

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Erratum to “Local Maximum Synchrosqueezing Chirplet Transform: An Effective Tool for Strongly Nonstationary Signals of Gas Turbine”

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IEEE Transactions on Instrumentation and Measurement 2021年 70卷

作者： Ya He Zhinong Jiang Minghui Hu YeZheng Li Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical Technology Beijing China Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing University of Chemical Technology Beijing China

In the above article [1] , the following expressions and analysis were published incorrectly. The correct expressions are given as follows.

关键词： Chirp Tools

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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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