Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is intr...Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.展开更多
The health status of aero engines is very important to the flight safety.However,it is difficult for aero engines to make an effective fault diagnosis due to its complex structure and poor working environment.Therefor...The health status of aero engines is very important to the flight safety.However,it is difficult for aero engines to make an effective fault diagnosis due to its complex structure and poor working environment.Therefore,an effective fault diagnosis method for aero engines based on the gravitational search algorithm and the stack autoencoder(GSA-SAE)is proposed,and the fault diagnosis technology of a turbofan engine is studied.Firstly,the data of 17 parameters,including total inlet air temperature,high-pressure rotor speed,low-pressure rotor speed,turbine pressure ratio,total inlet air temperature of high-pressure compressor and outlet air pressure of high-pressure compressor and so on,are preprocessed,and the fault diagnosis model architecture of SAE is constructed.In order to solve the problem that the best diagnosis effect cannot be obtained due to manually setting the number of neurons in each hidden layer of SAE network,a GSA optimization algorithm for the SAE network is proposed to find and obtain the optimal number of neurons in each hidden layer of SAE network.Furthermore,an optimal fault diagnosis model based on GSA-SAE is established for aero engines.Finally,the effectiveness of the optimal GSA-SAE fault diagnosis model is demonstrated using the practical data of aero engines.The results illustrate that the proposed fault diagnosis method effectively solves the problem of the poor fault diagnosis result because of manually setting the number of neurons in each hidden layer of SAE network,and has good fault diagnosis efficiency.The fault diagnosis accuracy of the GSA-SAE model reaches 98.222%,which is significantly higher than that of SAE,the general regression neural network(GRNN)and the back propagation(BP)network fault diagnosis models.展开更多
Using fuzzy C cluster mean (FCM), fuzzy theory and neural network, a fault diagnosis method was proposed, which was based on fuzzy C-means clustering algorithm of neural network that was applied in non-linear analog c...Using fuzzy C cluster mean (FCM), fuzzy theory and neural network, a fault diagnosis method was proposed, which was based on fuzzy C-means clustering algorithm of neural network that was applied in non-linear analog circuits and in diagnoses the ARNIC 429 reception circuit of aviation aircraft avionics. The C cluster algorithm can make the amount of the fuzzy rule automatically and can create an initial fuzzy rule database of fault diagnosis. A type of fuzzy neural network and a fault tree were generated. The algorithm avoids the disadvantage that gets into the part of optimum circumstance. A validate application was implemented, which proves that the method is effective. Therefore, the method is superior to the traditional methods in fault diagnosis, and the efficiency is heavily improved.展开更多
The paper discusses the fundamental conceptions and properties of fractal geometry.The definitions of fractal dimension are described and the mathods of calculating fractal dimension are introduced. The paper research...The paper discusses the fundamental conceptions and properties of fractal geometry.The definitions of fractal dimension are described and the mathods of calculating fractal dimension are introduced. The paper researches the peculiarities of fault diagnosis for logging truck engine and puts forward the technical way of diagnosing the faults with the help of the fractal geometry.展开更多
For too many state features are used in the diesel engine state evaluation and fault diagnosis, it is not easy to obtain the rational eigenvalues. In the paper, the cylinder subassembly of diesel engine is used to sea...For too many state features are used in the diesel engine state evaluation and fault diagnosis, it is not easy to obtain the rational eigenvalues. In the paper, the cylinder subassembly of diesel engine is used to search for the method of establishing state feature system and optimal approach. The signal of diesel engine has been collected when the piston ring and airtight ring are working at different states, then with the Bootstrap method and Genetic Algorithm (GA), an optimum parameter combination is received. Example shows this method is simple and efficient for establishing diesel engine state feature system, Thus, this method is valuable for the virtual state evaluation of similar complex system.展开更多
A fault diagnosis method of bearing based on integration of non-linear geometric invariables was presented for the non-linearity exiting in bearing system but ignored in traditional fault diagnosis.The meanings of non...A fault diagnosis method of bearing based on integration of non-linear geometric invariables was presented for the non-linearity exiting in bearing system but ignored in traditional fault diagnosis.The meanings of non-linear geometric invariables,such as fractal dimension,Lyapunov exponent,Kolmogorov entropy,correlation distance entropy and their calculation method were analyzed.Grey theory is applied to integrate these parameters and the correlation values as fault characteristic value was input into the support vector machines for diagnosis.The experimental results show that this method can distinguish the bearing fault effectively,it provides a new approach for the fault diagnosis of rotating machinery.展开更多
This paper presents a simulator model of a marine diesel engine based on physical, semi-physical, mathematical and thermodynamic equations, which allows fast predictive simulations The whole engine system is divided i...This paper presents a simulator model of a marine diesel engine based on physical, semi-physical, mathematical and thermodynamic equations, which allows fast predictive simulations The whole engine system is divided into several functional blocks： cooling, lubrication, air, injection, combustion and emissions. The sub-models and dynamic characteristics of individual blocks are established according to engine working principles equations and experimental data collected from a marine diesel engine test bench for SIMB Company under the reference 6M26SRP1. The overall engine system dynamics is expressed as a set of simultaneous algebraic and differential equations using sub-blocks and S-Functions of Matlab/Simulink. The simulation of this model, implemented on Matlab/Simulink has been validated and can be used to obtain engine performance, pressure, temperature, efficiency, heat release, crank angle, fuel rate, emissions at different sub-blocks. The simulator will be used, in future work, to study the engine performance in faulty conditions, and can be used to assist marine engineers in fault diagnosis and estimation （FDI） as well as designers to predict the behavior of the cooling system, lubrication system, injection system, combustion, emissions, in order to optimize the dimensions of different components. This program is a platform for fault simulator, to investigate the impact on sub-blocks engine＇s output of changing values for faults parameters such as： faulty fuel injector, leaky cylinder, worn fuel pump, broken piston rings, a dirty turbocharger, dirty air filter, dirty air cooler, air leakage, water leakage, oil leakage and contamination, fouling of heat exchanger, pumps wear, failure of injectors （and many others）.展开更多
A fault diagnosis method of working position gear in a tank gearbox is put forward based on simulating the fault of working position gear in an actual tank,extracting the envelope of vibration signal by Hilbert transf...A fault diagnosis method of working position gear in a tank gearbox is put forward based on simulating the fault of working position gear in an actual tank,extracting the envelope of vibration signal by Hilbert transformation amplitude demodulation method,and zooming the low-frequency band to envelope signal.展开更多
航空发动机结构与系统的复杂性导致轴承的故障诊断方法通常面临特征提取与模式识别的困难。针对以上不足,考虑实际工程诊断的实时性与准确性,提出了一种新的基于转子位移概率密度信息(probability density information of rotor displac...航空发动机结构与系统的复杂性导致轴承的故障诊断方法通常面临特征提取与模式识别的困难。针对以上不足,考虑实际工程诊断的实时性与准确性,提出了一种新的基于转子位移概率密度信息(probability density information of rotor displacement,PIRD)的航空发动机轴承智能故障诊断方法。其主要对一维卷积神经网络(1-dimensional convolutional neural network,1DCNN)模型进行改进,在传统的卷积层前面增加了PIRD的提取层,可以提取转子振动位移信号的概率密度信息,有效地降低了数据的冗余度,同时保留了故障监测的重要指标。提出的PIRD-CNN诊断模型保留了1DCNN端到端的故障诊断优势,将该模型在航空发动机试验台产生的轴承故障数据进行测试,其对轴承故障诊断精度可达96.58%,与基准研究相对比表明,PIRD-CNN能够快速且更加精准地诊断航空发动机轴承的故障。展开更多
基金supported by the Fundamental Research Funds for the Central Universities(No.NS2016027)
文摘Twin support vector machine(TWSVM)is a new development of support vector machine(SVM)algorithm.It has the smaller computation scale and the stronger ability to cope with unbalanced problems.In this paper,TWSVM is introduced into aircraft engine gas path fault diagnosis.The generalization capacity of Gauss kernel function usually used in TWSVM is relatively weak.So a mixed kernel function is used to improve performance to ensure that the TWSVM algorithm can better balance a strong generalization ability and a good learning ability.Experimental results prove that the cross validation training accuracy of TWSVM using the mixed kernel function averagely increases 2%.Grid search is usually applied in parameter optimization of TWSVM,but it heavily depends on experience.Therefore,the hybrid particle swarm algorithm is introduced.It can intelligently and rapidly find the global optimum.Experiments prove that its training accuracy is better than that of the classical particle swarm algorithm by 5%.
基金supported by the National Natural Science Foundation of China(No.51605309)the Aeronautical Science Foundation of China(Nos.201933054002,20163354004)。
文摘The health status of aero engines is very important to the flight safety.However,it is difficult for aero engines to make an effective fault diagnosis due to its complex structure and poor working environment.Therefore,an effective fault diagnosis method for aero engines based on the gravitational search algorithm and the stack autoencoder(GSA-SAE)is proposed,and the fault diagnosis technology of a turbofan engine is studied.Firstly,the data of 17 parameters,including total inlet air temperature,high-pressure rotor speed,low-pressure rotor speed,turbine pressure ratio,total inlet air temperature of high-pressure compressor and outlet air pressure of high-pressure compressor and so on,are preprocessed,and the fault diagnosis model architecture of SAE is constructed.In order to solve the problem that the best diagnosis effect cannot be obtained due to manually setting the number of neurons in each hidden layer of SAE network,a GSA optimization algorithm for the SAE network is proposed to find and obtain the optimal number of neurons in each hidden layer of SAE network.Furthermore,an optimal fault diagnosis model based on GSA-SAE is established for aero engines.Finally,the effectiveness of the optimal GSA-SAE fault diagnosis model is demonstrated using the practical data of aero engines.The results illustrate that the proposed fault diagnosis method effectively solves the problem of the poor fault diagnosis result because of manually setting the number of neurons in each hidden layer of SAE network,and has good fault diagnosis efficiency.The fault diagnosis accuracy of the GSA-SAE model reaches 98.222%,which is significantly higher than that of SAE,the general regression neural network(GRNN)and the back propagation(BP)network fault diagnosis models.
基金Project (MHRD0705) supported by the Science Foundation by Civil Aviation Administrator of ChinaProject (07ZCKFGX01500) supported by Tianjin Science Foundation and Technology Key Project
文摘Using fuzzy C cluster mean (FCM), fuzzy theory and neural network, a fault diagnosis method was proposed, which was based on fuzzy C-means clustering algorithm of neural network that was applied in non-linear analog circuits and in diagnoses the ARNIC 429 reception circuit of aviation aircraft avionics. The C cluster algorithm can make the amount of the fuzzy rule automatically and can create an initial fuzzy rule database of fault diagnosis. A type of fuzzy neural network and a fault tree were generated. The algorithm avoids the disadvantage that gets into the part of optimum circumstance. A validate application was implemented, which proves that the method is effective. Therefore, the method is superior to the traditional methods in fault diagnosis, and the efficiency is heavily improved.
文摘The paper discusses the fundamental conceptions and properties of fractal geometry.The definitions of fractal dimension are described and the mathods of calculating fractal dimension are introduced. The paper researches the peculiarities of fault diagnosis for logging truck engine and puts forward the technical way of diagnosing the faults with the help of the fractal geometry.
文摘For too many state features are used in the diesel engine state evaluation and fault diagnosis, it is not easy to obtain the rational eigenvalues. In the paper, the cylinder subassembly of diesel engine is used to search for the method of establishing state feature system and optimal approach. The signal of diesel engine has been collected when the piston ring and airtight ring are working at different states, then with the Bootstrap method and Genetic Algorithm (GA), an optimum parameter combination is received. Example shows this method is simple and efficient for establishing diesel engine state feature system, Thus, this method is valuable for the virtual state evaluation of similar complex system.
文摘A fault diagnosis method of bearing based on integration of non-linear geometric invariables was presented for the non-linearity exiting in bearing system but ignored in traditional fault diagnosis.The meanings of non-linear geometric invariables,such as fractal dimension,Lyapunov exponent,Kolmogorov entropy,correlation distance entropy and their calculation method were analyzed.Grey theory is applied to integrate these parameters and the correlation values as fault characteristic value was input into the support vector machines for diagnosis.The experimental results show that this method can distinguish the bearing fault effectively,it provides a new approach for the fault diagnosis of rotating machinery.
文摘This paper presents a simulator model of a marine diesel engine based on physical, semi-physical, mathematical and thermodynamic equations, which allows fast predictive simulations The whole engine system is divided into several functional blocks： cooling, lubrication, air, injection, combustion and emissions. The sub-models and dynamic characteristics of individual blocks are established according to engine working principles equations and experimental data collected from a marine diesel engine test bench for SIMB Company under the reference 6M26SRP1. The overall engine system dynamics is expressed as a set of simultaneous algebraic and differential equations using sub-blocks and S-Functions of Matlab/Simulink. The simulation of this model, implemented on Matlab/Simulink has been validated and can be used to obtain engine performance, pressure, temperature, efficiency, heat release, crank angle, fuel rate, emissions at different sub-blocks. The simulator will be used, in future work, to study the engine performance in faulty conditions, and can be used to assist marine engineers in fault diagnosis and estimation （FDI） as well as designers to predict the behavior of the cooling system, lubrication system, injection system, combustion, emissions, in order to optimize the dimensions of different components. This program is a platform for fault simulator, to investigate the impact on sub-blocks engine＇s output of changing values for faults parameters such as： faulty fuel injector, leaky cylinder, worn fuel pump, broken piston rings, a dirty turbocharger, dirty air filter, dirty air cooler, air leakage, water leakage, oil leakage and contamination, fouling of heat exchanger, pumps wear, failure of injectors （and many others）.
基金Sponsored by National Defense Science and Technology Key Lab Foundation of China(51457120104JB3505)
文摘A fault diagnosis method of working position gear in a tank gearbox is put forward based on simulating the fault of working position gear in an actual tank,extracting the envelope of vibration signal by Hilbert transformation amplitude demodulation method,and zooming the low-frequency band to envelope signal.
文摘航空发动机结构与系统的复杂性导致轴承的故障诊断方法通常面临特征提取与模式识别的困难。针对以上不足,考虑实际工程诊断的实时性与准确性,提出了一种新的基于转子位移概率密度信息(probability density information of rotor displacement,PIRD)的航空发动机轴承智能故障诊断方法。其主要对一维卷积神经网络(1-dimensional convolutional neural network,1DCNN)模型进行改进,在传统的卷积层前面增加了PIRD的提取层,可以提取转子振动位移信号的概率密度信息,有效地降低了数据的冗余度,同时保留了故障监测的重要指标。提出的PIRD-CNN诊断模型保留了1DCNN端到端的故障诊断优势,将该模型在航空发动机试验台产生的轴承故障数据进行测试,其对轴承故障诊断精度可达96.58%,与基准研究相对比表明,PIRD-CNN能够快速且更加精准地诊断航空发动机轴承的故障。
