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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38卷 445-458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 TechnologyBeijing 100029China State Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical TechnologyBeijing 100029China

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 *** 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)***,the vibration characteristics of the rotor-blade system under multi-fault conditions are ***,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 ***,a rotor test bench is utilized for experimental *** 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 *** application of this technique will effectively improve engine health management and fault prediction.

关键词： Aeroengine Rotor unbalance Composite sensor None once per revolution Blade tip clearance Dynamic balancing

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Research on the life prediction of vehicle engine cylinder head driven by dual channel Markov chain

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High Technology Letters 2025年第1期31卷 12-19页

作者： LI Zhijie WANG Yonglai CHEN Cuicui ZHOU Long WANG Yao YUAN Shaoshe National Key Laboratory of Internal Combustion Engines and Power Systems Weichai Power Co.Ltd.Weifang 261061P.R.China Key Laboratory of Engine Health Monitoring-control and Networking Ministry of EducationBeijing University of Chemical TechnologyBeijing 100029P.R.China

The performance degradation of vehicle engine cylinder heads is a complex phenomenon,and the accurate prediction of their remaining useful life is essential for maintenance *** address the problem of low prediction accuracy caused by insufficient data mining depth in current prediction models for the remaining service life of engine cylinder heads,a prediction method of dualchannel model is ***,the driving status data of multiple vehicles is summarized and analyzed,and the on-board network common variables related to cylinder head life are ***,driving segments are defined,the driving state features of each driving segment are extracted,and feature correlation analysis and principal component analysis are *** driving state profiles of the vehicle are divided using the clustering algorithm,and the cumulative degradation factors for driving state profiles are defined and ***,the mileage of each driving segment is classified into intervals by applying fuzzy set theory,and the state transfer probability matrices of driving state profiles and driving segment mileage are calculated.A new engine head life prediction model based on dual channel Markov chain(DCMC)is ***,the proposed method is applied to the residual life prediction of cylinder head of seven actual vehicles,and the comparison with actual life statistics results proved the validity of the proposed method.

关键词： engine cylinder head driving cycle vehicle networking life prediction

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A zero-fault sample diesel engine early warning and localisation method based on multi-cylinder similarity and memory augmentation

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Expert Systems with Applications 2025年 284卷

作者： Mo, Hangfeng Ji, Shude Chen, Shenxiao Wang, Zijia Jiang, Zhinong Mao, Zhiwei Key Laboratory of Engine Health Monitoring-Control and Networking Ministry of Education Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of High-end Compressor and System Technology Beijing University of Chemical Technology Beijing100029 China Tianjin300400 China State Key Laboratory of Tribology in Advanced Equipment Tsinghua University Beijing10084 China

Diesel engines often present challenges in achieving efficient warning and accurate localisation during actual operation due to complex working conditions and the lack of fault samples. To address this issue, this paper proposes a zero-fault sample anomaly warning and localisation method for diesel engines, which is based on multi-cylinder similarity and memory augmented autoencoder. This approach allows for model training without the use of fault samples, a method that has been scarcely explored in industrial research concerning diesel engine fault diagnosis using only normal samples. The method leverages the similarities in operating states among multiple cylinders and constructs a joint similarity matrix by integrating waveform similarity and impact energy similarity. This approach captures the intrinsic correlation characteristics among the cylinders and effectively mitigates the influence of changes in operating conditions on diagnostic performance. Additionally, a memory augmentation autoencoder is employed to model normal samples. By amplifying the reconstruction error of abnormal data, the method facilitates fault warning and localisation even in the absence of fault samples. Experimental results demonstrate that the proposed method can successfully achieve early fault warning and localisation under both single and cross-operating conditions without requiring fault samples, thereby providing an effective solution for early fault detection and intelligent maintenance of diesel engines. © 2025 Elsevier Ltd

关键词： Industrial research

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

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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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Optimal Design of Novel Electromagnetic-Ring Active Balancing Actuator with Radial Excitation

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Chinese Journal of Mechanical engineering 2021年第1期34卷 142-155页

作者： Xin Pan Xiaotian He Haiqi Wu Chuanlong Ju Zhinong Jiang Jinji Gao Beijing Key Laboratory of Health Monitoring and Self-recovery for High-end Mechanical Equipment Beijing University of Chemical TechnologyBeijing 100029China Key Laboratory of Engine Health Monitoring-Control and Networking(Ministry of Education) Beijing University of Chemical TechnologyBeijing 100029China

Imbalance vibration is a typical failure mode of rotational machines and has significant negative effects on the efficiency,accuracy,and service life of *** automatically reduce the imbalance vibration during the operational process,different types of active balancing actuators have been designed and widely applied in actual ***,the existing electromagnetic-ring active balancing actuator is designed based on an axial excitation structure which can cause structural instability and has low electromagnetic driving *** this paper,a novel radial excitation structure and the working principle of an electromagnetic-ring active balancing actuator with a combined driving strategy are presented in ***,based on a finite element model,the performance parameters of the actuator are analyzed,and reasonable design parameters are ***-locking torque measurements and comparative static and dynamic experiments are performed to validate the self-locking torque and driving efficiency of the *** results indicate that this novel active balancing actuator has sufficient self-locking torque,achieves normal step rotation at 2000 r/min,and reduces the driving voltage by 12.5%.The proposed novel balancing actuator using radial excitation and a combination of permanent magnets and soft-iron blocks has improved electromagnetic efficiency and a more stable and compact structure.

关键词： Rotational machines Imbalance vibration control Active balancing Balancing actuator Finite element analysis

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Bionic Artificial Self-Recovery Enables Autonomous health of Machine

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Journal of Bionic engineering 2022年第6期19卷 1545-1561页

作者： Gao Jinji Key Laboratory of Beijing for High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing University of Chemical TechnologyBeijing100029China Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical TechnologyBeijing100029China

This paper explores engineering self-recoveries theory,which emerged from the research of Bionics to meet the great demand of modern high-risk process manufacturing and the development of aerospace *** opens a new era in which artefact s learn from natural *** the rapid development of the industrial Internet and Artificial intelligence technology,we have gained a deep understanding of the law of fault generation and development,which provides an opportunity for the emergence of engineering self-recoveries *** self-recoveries expands the research field of Cybernetics and engineering cybernetics,endows machines with the self-recovery mechanism,which is unique to humans and animals,and enables machines to store,supplement and activate self-recovery power to maintain body *** research on Artificial intelligence has greatly enhanced the function of imitating the human brain,but has ignored the important system and function of humans and animals to maintain their own health—the self-recovery system and self-recovery *** intelligence imitates the conscious thinking control behaviour of the human brain to realize automation and intellectualization,making machines *** self-recovery can imitate the self-recovery mechanism of human unconscious thinking,and prevent and suppress faults in operation to realize self-recovery,possibly making machines *** self-recovery technology includes self-repair,compensation,self-protection and self-recovery *** self-recoveries is the basis of the autonomous health of machines and even artificial systems,and a new research field of *** has broad application prospects in engineering.

关键词： engineering self-recoveries Bionics Artificial self-recovery Artificial intelligence Fault self-recovery regulation

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Flow Field Simulation and Parameter Analysis of Hydraulic Unbalanced Bionic Self-recovery Actuator for Rotary Equipment

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Journal of Bionic engineering 2024年第1期21卷 325-343页

作者： Wei Li Xin Pan Dehong Ge Jinji Gao Key Laboratory of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical TechnologyBeijing 100029China Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing University of Chemical TechnologyBeijing 100029China

The rotor is the most important component of rotating machinery,and the vibration produced by its mass unbalance has a serious influence on the secure and steady operation of the machine,so an effective online suppression technology is urgently needed.A new hydraulic unbalanced bionic self-recovery system is introduced,imitating the way of manually repairing faulty *** accomplish the effect of actuator mass redistribution,the technology employs pressurized air to drive the quantitative transfer of liquid in the reservoir cavity at opposite *** can complete the online adjustment of the equipment's balancing state and suppress the unbalanced vibration of equipment in real time,which gives the equipment the ability to maintain an autonomous health state and improve equipment *** composition and working principle of the system are introduced in detail,and the key performance parameters,such as the minimum running speed and the balancing liquid transfer speed,are analyzed *** fluid-solid coupling model of the actuator was established,and the two-phase flow from inside the hydraulic unbalanced bionic self-recovery actuator was simulated under multiple working conditions and the performance parameters were quantitatively analyzed.A balancing simulation test bed was built,and its effectiveness was verified by performance parameter tests and unbalanced bionic self-recovery *** experimental results show that the mass distribution adjustment of the balancing disk can be achieved using different viscosity balancing liquid,and the response of liquid viscosity 10 cSt is faster than that of liquid viscosity 100 cSt in the process of balancing liquid transfer,and the time is reduced by more than 75%;the system can reduce the simulated rotor amplitude from 18.3μm to 10.6μm online in real time,which provides technical support for the subsequent development of a new generation of bionic intelligent equipment.

关键词： engineering self-recovery-Bionics Simulation analysis Self-recovery regulation

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Time Synchronous Averaging Based on Cross-power Spectrum

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Chinese Journal of Mechanical engineering 2023年第2期36卷 198-205页

作者： Ling Wang Minghui Hu Bo Ma Zhinong Jiang Key Laboratory of Engine Health Monitoring‑control and Networking of Ministry of Education Beijing University of Chemical TechnologyBeijing 100029China Beijing Key Laboratory of High‑end Mechanical Equipment Health Monitoring and Self‑Recovery Beijing University of Chemical TechnologyBeijing 100029China

Periodic components are of great significance for fault diagnosis and health monitoring of rotating *** synchronous averaging is an effective and convenient technique for extracting those ***,the performance of time synchronous averaging is seriously limited when the separate segments are poorly *** paper proposes a new averaging method capable of extracting periodic components without external reference and an accurate period to solve this *** this approach,phase detection and compensation eliminate all segments'phase differences,which enables the segments to be well *** effectiveness of the proposed method is validated by numerical and experimental signals.

关键词： Time synchronous averaging Phase detection Cross-power spectrum Fault diagnosis

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

作者： Zhao, Haipeng Mao, Zhiwei Chen, Kun Jiang, Zhinong Beijing University of Chemical Technology Key Lab of Engine Health Monitoring-Control and Networking Ministry of Education Beijing China

ISBN: (纸本)9781728101996

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. © 2019 IEEE.

关键词： Diesel engines

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

作者： Jiang, Zhinong Lai, Yuehua Wang, Zijia Zhang, Jinjie Beijing University of Chemical Technology Key Laboratory of Engine Health Monitoring Control and Networking Ministry of Education Beijing China

ISBN: (纸本)9781728101996

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. © 2019 IEEE.

关键词： Failure analysis

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