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Erratum to "Computation of multi-rotor vibration phase under a single key phase measurement condition and its application in aero-engine" [Mech. Syst. Signal Process. 230 (2025) 112592] (Mechanical Systems and Signal Processing (2025) 230, (S0888327025002936), (10.1016/***.2025.112592))

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Mechanical Systems and Signal Processing 2025年 231卷

作者： Hu, Minghui Han, Baole Ge, Xiangdong Jin, Yaqiang Wang, Weimin Jiang, Zhinong 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 Aero Engine Corporation of China Shenyang Aero-engine Institute Shenyang110042 China Qingdao Mingserve Technology Ltd. Qingdao266000 China Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical Technology Beijing100029 China

The publisher regrets that Fig. 21 was incorrect in the published article. The correct version of Fig. 21 should display as follows:[Figure presented] The publisher would like to apologize for any inconvenience caused...

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Fault diagnosis for reciprocating compressor based on GLCM and HOG features fusion of time-frequency image

Fault diagnosis for reciprocating compressor based on GLCM a...

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2019 International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2019

作者： Li, Hui Zhao, Haipeng Wang, Zijia Mao, Zhiwei Beijing University of Chemical Technology Key Lab of Engine Health Monitoring-Control and Networking Ministry of Education Beijing China Beijing University of Chemical Technology Beijing Key Laboratory of Health Monitoring Control and Fault Self-recovery for High-end Machinery Beijing China

ISBN: (纸本)9781728101996

In this paper, the gray level co-occurrence matrix (GLCM) and histogram of oriented gradient (HOG) features fusion of time-frequency image are introduced into the reciprocating compressor fault diagnosis. Firstly, vibration signals are acquired from the reciprocating compressor in different states of head tile and the wavelet transform distributions of vibration signals were displayed in time-frequency images. Secondly, GLCM and HOG methods are used to extract features from time-frequency images, then GLCM feature and HOG feature are fused and input into support vector machine for recognition and classification. By this way, the fault diagnosis of time series signals of reciprocating compressor is transferred to the classification of time-frequency images. The results show that can accurately realize diagnosis of small-head wear fault of reciprocating compressor. © 2019 IEEE.

关键词： Failure analysis

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Valve fault diagnosis of internal combustion engine based on an improved stacked autoencoder

Valve fault diagnosis of internal combustion engine based on...

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2019 International Conference on Sensing, Diagnostics, Prognostics, and control, SDPC 2019

作者： Chen, Kun Mao, Zhiwei Zhao, Haipeng Zhang, Jinjie Beijing University of Chemical Technology Key Lab of Engine Health Monitoring-Control and Networking Ministry of Education Beijing China Beijing University of Chemical Technology Beijing Key Laboratory of High-end Mechanical Equipment Beijing China

ISBN: (纸本)9781728101996

The valve train fault is a common mechanical fault of internal combustion engines (ICEs) due to the valve clearance usually oversized because of the wear of valve mechanism, material deformations, and long continuous running hours. Feature extraction dependent on the expertise and experience too much in traditional fault diagnosis. In this study, a stacked autoencoder (SAE) is proposed for adaptive and hierarchical feature extraction in cylinder vibration signals. The capability of feature mining in SAE is enhanced after unsupervised layer-by-layer pre-training and supervised fine-tuning. Further, the dropout trick and the batch normalization trick are introduced to prevent over-fitting and accelerate model convergence. The harmonic search (HS) algorithm is proposed to obtain the optimal hyper-parameter values in the SAE model, and achieve adaptive adjustment of the model structure. The diesel engine vibration data consisting of seven valve health states is employed to verify the effectiveness of the proposed method, the results demonstrate that the proposed method outperforms original SAE and many conventional fault diagnosis algorithms in terms of the classification accuracy. © 2019 IEEE.

关键词： Failure analysis

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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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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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Fault diagnosis for reciprocating compressor based on GLCM and HOG features fusion of time-frequency image

Fault diagnosis for reciprocating compressor based on GLCM a...

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International Conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Hui Li Haipeng Zhao Zijia Wang Zhiwei Mao Key Lab of Engine Health Monitoring-Control and Networking Ministry of Education Beijing University of Chemical Technology Beijing China Beijing Key Laboratory of Health Monitoring Control and Fault Self-recovery for High-end Machinery Beijing University of Chemical Technology Beijing China

ISBN: (数字)9781728101996

ISBN: (纸本)9781728102009

关键词： Feature extraction Time-frequency analysis Image coding Vibrations Support vector machines Fault diagnosis Training

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An intelligent fault diagnosis method for a diesel engine valve based on improved wavelet packet-Mel frequency and convolutional neural network

An intelligent fault diagnosis method for a diesel engine va...

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International Conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Haipeng Zhao Zhiwei Mao Kun Chen Zhinong Jiang Key Lab 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

ISBN: (数字)9781728101996

ISBN: (纸本)9781728102009

Considering the diesel engine vibration signals have the characteristics of the non-stability and non-linearity due to its compact-complex structure, strong noise and especially unstable operating conditions, we proposes an novel method based on improved wavelet packet-Mel frequency and convolutional neural network (CNN) to extract features and diagnose faults of diesel engine valve. Firstly, the wavelet packet transform is applied with the purpose of decomposing vibration signal and reconstructing each wavelet packet coefficient. Secondly, an improved Mel frequency cepstrum method is used to extract features from the reconstructed vibration signals. MFC algorithm is a well-known feature extraction technique widely used for speech recognition. Then, feature matrixes are constituted to obtain more definite and comprehensive time-frequency distributed representation, of which the row represents the average Mel frequency cepstrum coefficients and the column represents the frequency bands of wavelet packet decomposition in ascending order. Finally, a deep hierarchical CNN structure constructed by convolution layers, max-pooling layers and fully-connected layers is trained using a standard backpropagation, of which the input of first layer with 256 neurons is the above 2D feature matrixes and the output of final layer with 3 neurons is the number of vibration signal states. The experimental results of the fault diagnosis for the diesel engine valves show that the proposed method has the good diagnosis performance for diesel engine valve clearance faults.

关键词： Vibrations Diesel engines Valves Feature extraction Wavelet packets Fault diagnosis

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A Fault Diagnosis Method for Valve Train of Diesel engine Considering Incomplete Feature Set

A Fault Diagnosis Method for Valve Train of Diesel Engine Co...

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International Conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Zhinong Jiang Yuehua Lai Zijia Wang Jinjie Zhang 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 Control and Fault Self-recovery for High-end Machinery Beijing University of Chemical Technology Beijing China

ISBN: (数字)9781728101996

ISBN: (纸本)9781728102009

Abnormal valve clearance is a common fault of diesel engine, and early warning of abnormal valve clearance plays an important role in the condition based maintenance of diesel engine. Although information fusion technology can improve the accuracy of fault diagnosis, it cannot guarantee that the fused features can perfectly represent the required key information. For the incomplete feature set, a method combining multi-domain feature and improved support vector machine is proposed. Firstly, the extraction of multi-domain feature is carried out to deeply explore the state information of valve train contained in the original vibration signal. The statistical characteristics and waveform characteristics are extracted from time domain vibration signals, and the frequency domain feature similar to time-domain feature is extracted after the Fourier transform of the vibration signal. What's more, according to the working principle of diesel engine, the energy characteristics in angular frequency domain are extracted. Then, an improved support vector machine method based on multi-domain feature is proposed to further reduce the diagnostic errors caused by incomplete feature set. Finally, the proposed method is compared with other traditional methods about the fault diagnosis of valve train of diesel engine. The results show that the proposed method is applicable to the fault diagnosis of valve train of diesel engine with good accuracy, and the generalization ability of diagnostic model is greatly improved.

关键词： Feature extraction Diesel engines Vibrations Valves Frequency-domain analysis Support vector machines Fault diagnosis

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Valve fault diagnosis of internal combustion engine based on an improved stacked autoencoder

Valve fault diagnosis of internal combustion engine based on...

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International Conference on Sensing, Diagnostics, Prognostics, and control (SDPC)

作者： Kun Chen Zhiwei Mao Haipeng Zhao Jinjie Zhang Key Lab 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 Beijing University of Chemical Technology Beijing China

ISBN: (数字)9781728101996

ISBN: (纸本)9781728102009

关键词： Valves Feature extraction Ice Fault diagnosis Adaptation models Vibrations Classification algorithms

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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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