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fault diagnosis of rocket engine ground testing bed with self-organizing maps(SOMs)

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Journal of Harbin Institute of Technology(New Series) 2009年第2期16卷 204-208页

作者：朱宁冯志刚王祁 Dept.of Automatic Measurement and Control Harbin Institute of Technology School of Automation Shenyang Institute of Aeronautical Engineering

To solve the fault diagnosis problem of liquid propellant rocket engine ground testing bed,a fault diagnosis approach based on self-organizing map(SOM)is *** SOM projects the multidimensional ground testing bed data into a two-dimensional *** of the SOM is used to cluster the ground testing bed *** out map of the SOM is divided to several *** region is represented for one fault *** fault mode of testing data is determined according to the region of their labels belonged *** method is evaluated using the testing data of a liquid-propellant rocket engine ground testing bed with sixteen fault *** results show that it is a reliable and effective method for fault diagnosis with good visualization property.

关键词： fault diagnosis self-organizing map (SOM) U-matrix visualization

fault diagnosis of Reciprocating Compressors Valve Based on Cyclostationary Method

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Journal of Donghua University(English Edition) 2011年第4期28卷 349-352页

作者：王雷王奉涛赵俊龙马孝江 Institute of Vibration Engineering School of Mechanical EngineeringDalian University of Technology

The relationship between second-order cyclostationary method and time-frequency distribution is studied, and cyclic autocorrelation（CA） function is indicated to be one sort of special time-frequency distribution method. Furthermore, a fault diagnosis method for reciprocating compressors based on empirical mode decomposition （EMD） and CA function is proposed, and then it is applied to the fault diagnosis of reciprocating compressor valve. Firstly, the vibration signal of reciprocating compressor valve is decomposed by using ENID method, and several intrinsic mode functions （IMFs） are obtained. Secondly, the IMFs are evaluated by some denoising criterions to remove the noise and interfering ones. Finally, the CA functions of the remained IMFs are calculated, which will be used to reconstruct the CA function of the original vibration signal. Engineering application indicates that this method can sufficiently inhibit the cross-interference items of CA function. Therefore, more explicit working conditions of reciprocating compressor components can be achieved.

关键词： reciprocating compressor valve fault diagnosis cyclic autocorrelation (CA) function empirical mode decomposition (EMD)

fault diagnosis of Vehicle Transmission System Based on Rough Set Theory

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Journal of Beijing Institute of Technology 2001年第2期10卷 204-208页

作者：李晓雷张振华吴晓兵田春姝北京理工大学车辆与交通工程学院北京100081

Rough set theory is used to treat the data of vehicle transmission system faults. The minimum fault feature vector can be obtained by calculating the importance and dependency of each attribute. Real time diagnosis, as a result, can be actualized. Ultimate decision making can be done by analyzing the consistency of decision information. The result shows that rough set theory is useful and possesses its unique merits in this field.

关键词： rough set fault diagnosis vehicle transmission system

fault diagnosis in neutral point indirectly grounded system basedon information fusion

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Journal of Harbin Institute of Technology(New Series) 2003年第4期10卷 434-437页

作者：于飞鞠丽叶刘喜梅崔平远钟秋海 Dept. of Automatic Control Beijing Institute of Technology Information and Control Engineering Institute Qingdao University of Science and Technology Deep Space Exploration Research Center Harbin Institute of Technology

In neutral point indirectly grounded systems, phase-to-ground fault is putting new demands on fault diagnosis technology. Information fusion is applied to detect the phase-to-ground fault, which integrates several sources of information, including line current, line voltage, zero sequence current and voltage, and quintic harmonic wave component. This method is testified through the simulation of Matlab. Simulation results show that the precision and reliability of the detection has been greatly increased.

关键词： information fusion fault diagnosis neutral point indirectly grounded system phase-to-ground fault

fault diagnosis of a mine hoist using PCA and SVM techniques

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Journal of China University of Mining and Technology 2008年第3期18卷 327-331页

作者： CHANG Yan-wei WANG Yao-cai LIU Tao WANG Zhi-jie School of lnformation and Electrical Engineering China University of Mining ＆ Technology Xuzhou Jiangsu 221008 China School of Mechanical and Electrical Engineering Xuzhou Normal University Xuzhou Jiangsu 221116 China Department of Electronics ＆ Information Technology Suzhou Vocational University Suzhou Jiangsu 215104 China

A new method based on principal component analysis (PCA) and support vector machines (SVMs) is proposed for fault diagnosis of mine hoists. PCA is used to extract the principal features associated with the gearbox. Then, with the irrelevant gearbox variables removed, the remaining gearbox, the hydraulic system and the wire rope parameters were used as input to a multi-class SVM. The SVM is first trained by using the one class-based multi-class optimization algorithm and it is then applied to fault identification. Comparison of various methods showed the PCA-SVM method successfully removed redundancy to solve the dimensionality curse. These results show that the algorithm using the RBF kernel function for the SVM had the best classification properties.

关键词： fault diagnosis principal component analysis support vector machines mine hoist

fault diagnosis using a probability least squares support vector classification machine

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Mining Science and Technology 2010年第6期20卷 917-921页

作者： GAO Yang, WANG Xuesong, CHENG Yuhu, PAN Jie School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China School of Information and Electrical Engineering China University of Mining & Technology Xuzhou 221116 China

Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.

关键词： fault diagnosis probability least squares support vector classification machine roller bearing

fault diagnosis of Cylindrical Grinding Machine

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Transactions of Tianjin University 2010年第1期16卷 40-44页

作者：杜兵张宏伟蒋永翔 School of Mechanical Engineering Tianjin University

Based on experiment modal analysis(EMA) and operation modal analysis(OMA), the dynamic characteristics of cylindrical grinding machine were measured and provided a basis for further failure *** influences of grinding parameters on dynamic characteristics were studied by analyzing the diagnostic signals extracted from racing and grinding *** significant frequency of 38 Hz related to grinding wheel spindle speed of 2 307 r/min showed that the wheel spindle system was in a state of imbalan...

关键词： cylindrical grinding machine fault diagnosis experiment modal analysis operation modal analysis vibration

fault diagnosis Approach of Local Ventilation System in Coal Mines Based on Multidisciplinary Technology

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Journal of China University of Mining and Technology 2006年第3期16卷 317-320页

作者： GONG Xiao-yan XUE He TAO Xin-li HU Ning School of Mechanical Engineering Xi ＇an University. of Science and Technology Xi＇an Shanxi 710054 China Department of Aeronautics and Space Engineer Tohoku University Sendai 980-8579 Japan

In order to reduce the probability of fault occurrence of local ventilation system in coal mine and prevent gas from exceeding the standard limit, an approach incorporating the reliability analysis, rough set theory, genetic algorithm (GA), and intelligent decision support system (IDSS) was used to establish and develop a fault diagnosis system of local ventilation in coal mine. fault tree model was established and its reliability analysis was performed. The algorithms and software of key fault symptom and fault diagnosis rule acquiring were also analyzed and developed. Finally, a prototype system was developed and demonstrated by a mine instance. The research results indicate that the proposed approach in this paper can accurately and quickly find the fault reason in a local ventilation system of coal mines and can reduce difficulty of the fault diagnosis of the local ventilation system, which is significant to decrease gas exploding accidents in coal mines.

关键词： fault diagnosis local ventilation rough set theory genetic algorithm IDSS

fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

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Frontiers of Mechanical Engineering 2015年第3期10卷 277-286页

作者： Diego CABRERA Fernando SANCHO Rene-Vinicio SANCHEZ Grover ZURITA Mariela CERRADA Chuan LI Rafael E. VASQUEZ Departamento de Ingenieria Mecainica Universidad Politecnica Salesiana Cuenca Ecuador Departamento de Ciencias de la Computaci6n e Inteligencia Artificial Universidad de Sevilla Espafia Departamento de Ingenieria Electro-Mechnica Universidad PrivadaBoliviana Cochabamba Bolivia Departamento de Sistemas de Control Universidad de Los AndesM6rida Venezuela Research Center of System Health Maintenance Chongqing Technologyand Business University Chongqing 400067 China Facultad de Ingenieria Mecainica Universidad Pontificia BolivarianaMedellln Colombia

This paper addresses the development of a random forest classifier for the muki-class fault diagnosis in spur gearboxes. The vibration signal＇s condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients＇ energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters＇ space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

关键词： fault diagnosis spur gearbox wavelet packet decomposition random forest