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A Review on Self-Recovery Regulation(SR)Technique for Unbalance Vibration of High-End Equipment

Chinese Journal of Mechanical engineering 2020年第6期33卷 1-23页

作者： Xin Pan Jiaqiao Lu Jiaji Huo Jinji Gao Haiqi Wu Beijing Key Laboratory of Health Monitoring and Self-recovery for High-end Mechanical Equipment Beijing University of Chemical TechnologyBeijing100029China Key Laboratory of Engine Health Monitoring-Control and Networking(Ministry of Education) Beijing University of Chemical TechnologyBeijing100029China

The high-end equipment represented by high-end machine tools and aero-engines is the core component of the national intelligent manufacturing plan,and the mass unbalance is the main reason for its excessive vibration,that seriously impacts the operation efficiency and running life of the *** order to change the traditional way that the fault of equipment can only be repaired by human,the self-recovery mechanism of human and animal are given to the equipment in this paper,which forms the self-recovery regulation(SR)system for unbalance vibration of high-end *** system can online generate the self-recovery force to restrain the unbalance vibration of the equipment in operation,which is an important direction for the development of the equipment to the advanced intelligent *** on the basic principles of SR technique,the typical engineering application cases of this technique in the field of aeroengine and high-end machine tools are introduced,and four related studies promoting the development of this technique are summarized and analyzed in *** includes feature extraction,imbalance location,regulation method and balancing ***-recovery Regulation(SR)Technique is an important way to realize intelligent manufacturing and intelligent *** research can lay a technical foundation for the development of high-end equipment with self-health function.

关键词： Unbalance vibration Self-recovery regulation Active balancing High-end equipment Self-health

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Modulation Signal Bispectrum Optimization for Fault Diagnosis of Intershaft Bearings 12

Modulation Signal Bispectrum Optimization for Fault Diagnosi...

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12th IEEE Global Reliability and Prognostics and health Management, PHM-Nanjing 2021

作者： Feng, Kun Li, Yezheng He, Ya Key Laboratory Of Engine Health Monitoring-Control And Networking Beijing University Of Chemical Technology Ministry Of Education Beijing China Beijing Key Laboratory Of High-End Mechanical Equipment Health Monitoring And Self Recovery Beijing University Of Chemical Technology Beijing China

ISBN: (纸本)9781665401302

Modulation signal bispectrum (MSB) has attracted much attention due to its sensitivity to nonlinear characteristics related to rolling bearings' faults. However, there are still two challenges in applying MSB to the diagnosis of aero-engines' intershaft bearing: 1) the variable working conditions make the vibration signals show strong non-stationarity;2) unlike the ordinary rolling bearings, the fault features in vibration of intershaft bearings are extremely weak and difficult to extract. Thus, an optimized MSB (OMSB) is proposed for diagnostics of intershaft bearings under variable speed conditions. This paper mainly discusses two core issues: 1) an enhanced filter is presented with the idea of whitening and deconvolution, so as to highlight weak fault features from vibration signal with strong interference;2) an MSB algorithm in rotation speed difference (RSD) domain is established to deal with the non-stationarity caused by various operating speeds, and a lifted modulation spectrum is constructed to exhibit modulation orders in the signal, i.e., the fault feature orders can be clearly extracted. Experimental signals are analyzed by OMSB, and the results prove that the method can suppress the interference of irrelevant components and effectively extract the intershaft bearing's weak fault features. Comparisons also show the superiority of the proposed OMSB. © 2021 IEEE.

关键词： Failure analysis

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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 novel rotor dynamic balancing method based on blade tip clearance measurement without the once per revolution sensor

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Chinese Journal of Aeronautics 2025年第2期 449-462页

作者： Weibo LI Weimin WANG Shuai ZHANG Jiale WANG Yulong LIN Tianqing LI Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical Technology State Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical Technology

The existence of the aeroengine casing, limited monitoring points, and multi-fault characteristics make obtaining the rotor's vibration transmission characteristics challenging, resulting in difficulties accurately identifying the rotor unbalance. This paper utilizes a high-frequency composite sensor to monitor the engine's blade tip clearance(BTC) and extracts unbalanced information from BTC signals for rotor dynamic balancing, while avoiding the need for the once per revolution(OPR) sensor. First, the vibration characteristics of the rotor-blade system under multi-fault conditions are investigated. Then, based on BTC measurement, a none OPR method and an unbalance identification method are proposed, in which the radial vibration of the blade tip in the BTC signals at different speeds is extracted and operated in the time domain to obtain the rotor unbalanced vibration, the signal is reconstructed, and cross-correlation analysis is used to accurately identify the magnitude and phase of the unbalanced signal. Finally, a rotor test bench is utilized for experimental verification. The results reveal that the dynamic balancing method based on the BTC signal can more precisely identify the rotor unbalance than the traditional rotor dynamic balancing method. The application of this technique will effectively improve engine health management and fault prediction.

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A Vibration Acceleration Sensor-based Shock Pulse Method for Condition Evaluation of Rolling Bearings 12

A Vibration Acceleration Sensor-based Shock Pulse Method for...

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12th IEEE Global Reliability and Prognostics and health Management, PHM-Nanjing 2021

作者： He, Ya Hu, Minghui Feng, Kun Jiang, Zhinong Beijing University Of Chemical Technology Key Lab Of Engine Health Monitoring-Control And Networking Of Ministry Of Education Beijing China Beijing University Of Chemical Technology Beijing Key Laboratory Of High-end Mechanical Equipment Health Monitoring And Self-Recovery Beijing China

ISBN: (纸本)9781665401302

Compared to the acceleration sensor based vibration measurement, the shock pulse sensor based SPM has been proved to be simpler and more effective for evaluating rolling bearings' condition, but it also has the inevitable inferiority of being unable to monitor the overall state of the machine. To solve this problem, a novel shock pulse method based on acceleration sensor (APM) is proposed in this paper. It widens the function of an acceleration sensor and realizes the analysis of resonance response beyond the linear response of the sensor. This paper focuses on two core issues. The first one is to extract the available shock pulse from vibration acceleration signals by the band-pass filtering and envelope demodulation. The key to this issue is the bandwidth selection of the band-pass filter. In this study, the deviation rate of pulse value is defined, and the bandwidth is optimized by dealing with the contradiction between the deviation rate and the anti-noise ability of the filter. Another issue is the measurement of the pulse strength. In this paper, an improved pulse quantization method is proposed to reduce the interference of the random impact. Furthermore, the normalization and condition estimation of SPM theory are used to realize the mapping between the measurement value and the bearing's condition. Experimental bearing signals with various fault status were analyzed to verify the feasibility and effectiveness of APM. © 2021 IEEE.

关键词： Roller bearings

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Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM

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High Technology Letters 2021年第2期27卷 111-120页

作者： Jiang Zhinong Lai Yuehua Mao Zhiwei Zhang Jinjie Lai Zehua Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical TechnologyBeijing 100029P.R.China Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing University of Chemical TechnologyBeijing 100029P.R.China China Nuclear Control Systems Engineering Co.Ltd Beijing 102401P.R.China

The fault detection and diagnosis of diesel engine valve clearance can effectively improve the availability and safety of diesel engine and have extremely important value and *** engines generally operate in various stable operating conditions,which have important influence on the fault ***,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration *** the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required *** this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is ***,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration ***,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault *** addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for *** results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.

关键词： diesel engine fault diagnosis operating condition support vector machine(SVM)

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Labeling Degraded Full Operation Cycle Data with 2nd-Level Relaxation Clustering 12

Labeling Degraded Full Operation Cycle Data with 2nd-Level R...

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12th IEEE Global Reliability and Prognostics and health Management, PHM-Nanjing 2021

作者： Li, Zhouzheng Xiao, Yuan Li, Yezheng Liang, Zhaoxi Feng, Kun Key Laboratory Of Engine Health Monitoring-Control And Networking. Ministry Of Education Beijing Key Laboratory Of High-End Mechanical Equipment Health Monitoring And Self-Recovery Beijing University Of Chemical Technology Beijing China Beijing Institute Of Technology School Of Mechanical Engineering Beijing China

ISBN: (纸本)9781665401302

The paper proposes a convenient unsupervised degradation data labeling method based on 2nd-level relaxation clustering. The proposed method serves as a practical auxiliary tool to help researchers segment a full operation cycle data of mechanical equipment into different pieces of healthy, degradation, or failure states. The proposed method is fully automotive and by applying 2nd-level relaxation - a structure specially designed for segmenting operation cycle data with degradation - can outperform existing clustering methods. Experiments on both simulation data and industrial pump data show the proposed method achieves accuracy matching the human expert baseline, making it a simple and effective tool. © 2021 IEEE.

关键词： Clustering algorithms

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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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Fault diagnosis method for blade fracture of gas turbine based on casing vibration

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Mechanical Systems and Signal Processing 2025年 234卷

作者： Hu, Ming-Hui Jin, Bao-teng Liu, Shao-Peng Wang, Wei-Min Jiang, Zhi-Nong Zhang, Xiao-Yang Gao, Jin-ji State Key Laboratory of High-end Compressor and System Technology 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 Beijing Key Laboratory of Health Monitoring and Self-Recovery for High-End Mechanical Equipment Beijing University of Chemical Technology Beijing100029 China National Key Laboratory of Internal Combustion Engines and Power Systems Weichai Power Co. Ltd. China

Blade fracture is one of the most difficult faults to diagnose in rotating machinery. Most gas turbines can only measure casing vibration rather than directly measuring blade vibration with other sensors, making monitoring and diagnosing such faults more challenging. Research on vibration characteristics and fault diagnosis methods of blade fracture is critical to ensure the safe and reliable operation of gas turbines. The current paper presents a new method to monitor the blade fracture faults from the vibration signal of the casing. 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. Then, based on the casing vibration response model, we analyzed the dynamic evolution of vibration response in the pre- and post-fracture stages of rotor blade failure. The results indicate that the response characteristics of velocity RMS, blade passing frequency (BPF), and fundamental frequency can effectively characterize blade fracture faults. Secondly, based on the vibration-sensitive characteristics of blade fracture faults, we propose an Improved Ordered Cluster Analysis (IOCA) identification method. Considering that the blade fracture can decrease the BPF amplitude and lead to the appearance of side-frequency bands. We propose a Fault Localization Estimation (FLE) method, which constructs a fracture localization factor called ΔUBF, and the highest ΔUBF value corresponds to the stage where the blade fracture has appeared. Finally, A fault diagnosis scheme for gas turbine blade fracture based on casing vibration is proposed. An engineering case of a gas turbine blade fracture fault is analyzed. The results show that the proposed method can timely and effectively diagnose the gas turbine blade fracture fault from the vibration signal of the casing. They can further localize the blade fracture fault at the 8th and 9th s

关键词： Rotating machinery

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Evaluation Method of Rotating Machinery health State Based on TPE-XGBoost 12

Evaluation Method of Rotating Machinery Health State Based o...

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12th IEEE Global Reliability and Prognostics and health Management, PHM-Nanjing 2021

作者： Yang, Liping Li, Zhouzheng Xiao, Yuan Feng, Kun Key Laboratory Of Engine Health Monitoring-Control And Networking. Ministry Of Education Beijing Key Lab. Of High-End Mech. Equip. Hlth. Monitoring And Self-Recovery Beijing Univ. Of Chem. Technol. Beijing China

ISBN: (纸本)9781665401302

Aiming at the problems of imbalanced data samples and complex parameter optimization in the existing health assessment tasks of rotating machinery method, this paper develops a health assessment method for rotating machinery based on the TPE-XGBoost model. The method consists of the following steps: (1) Extract 15 commonly used time-frequency domain parameters as input;(2) Label the samples with three categories: healthy, degradation and failure;(3) Use SMOTE-ENN method to balance the labeled samples;(4) With the balanced samples as input, TPE-XGBoost model is trained offline to get the best parameters and structures for online evaluation. (5) Evaluate the health status according to the probability obtained. The applications show that this method can be effectively trained and give early warnings of equipment degradation. It is superior than other commonly used methods. © 2021 IEEE.

关键词： Rotating machinery

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