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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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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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A Clustering Fault Diagnosis Method for Gas Turbine Performance Parameters Based on t-SNE

A Clustering Fault Diagnosis Method for Gas Turbine Performa...

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2022 Global Reliability and Prognostics and health Management Conference, PHM-Yantai 2022

作者： Miao, Dongyan Liu, Baoxia Li, Zhouzheng Li, Gang Zhang, Peng Feng, Kun Ministry of Education Key Laboratory of Engine Health Monitoring-Control and Networking China Compressor Maintenance Center of North Pipeline Company of PipeChina China Beijing University of Chemical Technology Beijing Key Laboratory of High-End Mechanical Equipment Health Monitoring and Self-Recovery Beijing China

ISBN: (数字)9781665496315

ISBN: (纸本)9781665496315

This paper proposed a clustering method for gas turbine fault diagnosis. Our method is based on t-SNE and focuses on its clustering capability. The goal is to cluster the data of different health states of the same gas turbine to judge the time of failure. In order to strengthen the clustering ability of t-SNE for gas turbine data, we take the time factor into consideration based on the characteristics of the continuity of gas turbine health status. As t-SNE doesn't actually provide class information but returns the results of dimension reduction, we use quick grid clustering to process the dimensionally reduced data and offer clustering results. We design a simulation dataset and demonstrate the effectiveness of our method through multiple experiments. Finally, we also apply this method to a real-world dataset and get a good result. © 2022 IEEE.

关键词： Gas turbines

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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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The construction method of typical engine fault identification model based on Simulink

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IFAC-PapersOnLine 2024年第29期58卷 19-24页

作者： Xin bo Zhu Cheng Hu RuiJie Jiang Zhinong Gao Zhilong Key Laboratory of Engine Health Monitoring-Control and Networking Ministry of Education Beijing University of Chemical Technology Beijing China China North Engine Research Institute Tianjin China Key Laboratory of Engine Health Monitoring-Control and Networking Ministry of Education Beijing Key Laboratory of Health Monitoring Control and Fault Self-Recovery for High-end Machinery Beijing University of Chemical Technology Beijing China

This paper provides a summary and overview of methods for constructing typical fault identification model for diesel engines based on Simulink, aiming at improving the efficiency of engine fault diagnosis and maintenance. First of all, the common types of diesel engine faults were analyzed, including intake and exhaust system faults, cooling system faults, lubrication system faults and fuel system faults. Then, the basic steps and methods of Simulink modeling are introduced in detail, by using data acquisition and preprocessing techniques, a large amount of sensor data was converted into signals that can be used as inputs. Next, this paper discussed the specific process of the fault identification model, including the data reading module, speed discrimination module, threshold discrimination module and state identification module, and the feasibility of the model is verified through practical application cases. The research show that the fault identification model based on Simulink can effectively improve the accuracy and efficiency of engine fault diagnosis, providing a new technical method to enhance the operational safety and reliability of engines, and also offering valuable reference for subsequent related research.

关键词： Diesel engine Fault Identification Model Construction Simulink

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

An Improved Initial Parameter Setting Method for Variational...

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2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Li, He Zhang, Jiniie Jiang, Zhinong Mao, Zhiwei Beijing University of Chemical Technology Key Laboratory of Engine Health Monitoring Control and Networking Ministry of Education Beijing100029 China Beijing University of Chemical Technology State Key Laboratory of High-end Compressor and System Technology Beijing100029 China

ISBN: (纸本)9798350318012

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. © 2023 IEEE.

关键词： Domain decomposition methods

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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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A Modified Iterative Learning control Approach for the Active Suppression of Rotor Vibration Induced by Coupled Unbalance and Misalignment

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Chinese Journal of Mechanical engineering 2024年第1期37卷 242-253页

作者： Yifan Bao Jianfei Yao Fabrizio Scarpa Yan Li College of Mechanical and Electrical Engineering Beijing University of Chemical TechnologyBeijing 100029China State Key Laboratory of High-end Compressor and System Technology Beijing 100029China Beijing Key Laboratory of High-end Mechanical Equipment Health Monitoring and Self-Recovery Beijing University of Chemical TechnologyBeijing 100029China Key Lab of Engine Health Monitoring-Control and Networking of Ministry of Education Beijing University of Chemical TechnologyBeijing 100029China Bristol Composites Institute(ACCIS) University of BristolBristol BS81TRUK Dynamics and Control Research Group(DCRG) University of BristolBristol BS81TRUK

This paper proposes a modified iterative learning control(MILC)periodical feedback-feedforward algorithm to reduce the vibration of a rotor caused by coupled unbalance and parallel *** control of the vibration of the rotor is provided by an active magnetic actuator(AMA).The iterative gain of the MILC algorithm here presented has a self-adjustment based on the magnitude of the *** filters are adopted to extract the synchronous(1×Ω)and twice rotational frequency(2×Ω)components of the rotor *** the notch frequency of the filter and the size of feedforward storage used during the experiment have a real-time adaptation to the rotational *** method proposed in this work can provide effective suppression of the vibration of the rotor in case of sudden changes or fluctuations of the rotor *** and experiments using the MILC algorithm proposed here are carried out and give evidence to the feasibility and robustness of the technique proposed.

关键词： Rotor vibration suppression Modified iterative learning control Unbalance Parallel misalignment Active magnetic actuator

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