The diesel locomotive plays an important role in the field of transport, and the engine maintenance work is the prerequisite and gnarantee for the locomotive normal working. In this paper, we first establish the fault...The diesel locomotive plays an important role in the field of transport, and the engine maintenance work is the prerequisite and gnarantee for the locomotive normal working. In this paper, we first establish the fault tree model of locomotive engine 16V240ZJ on the basis of engine non-start as the top event. Then we combines the fitzzy mathematics the- ory and fault tree analysis method for failure diagnosis of 16V240ZJ engine's abnormal start-up. We obtained the fuzzy probability curve and top events probability confidence interval by analyzing the fuzzy fault tree qualitatively and quantitatively. It provides a fuzzy analysis basis for solving the problem of 16V240ZJ engine's abnormal start-up.展开更多
The concept of average incremental correlation degree is put forward.It has been proved that the average incremental correlation model has such properties as parallelism,consistency,affine,affine transformation isoton...The concept of average incremental correlation degree is put forward.It has been proved that the average incremental correlation model has such properties as parallelism,consistency,affine,affine transformation isotonicity and interference factors independence,and it will not lead to changes of the sequence order relation because of the data transformation.Therefore,the new model keeps good stability.Finally,the incremental average correlation model is applied to failure model analysis of equipment,and an ideal diagnostic effect is obtained.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
Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed...Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015?0.017 5 and sample error is controlled within 0?0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis;the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.展开更多
The vibration fault, one of the common faults in the steam turbine generator unit, brings great damage to the production and the running process. It is well known that the information entropy is to describe the degree...The vibration fault, one of the common faults in the steam turbine generator unit, brings great damage to the production and the running process. It is well known that the information entropy is to describe the degree of indeterminacy of the system, so the information entropy can be used to measure Despite its efficiency, one kind of information entropy is just enabled to identify make up for this limitation, based on nalysis was studied for vibration fault the vibration condition of the unit. certain part of the faults. In order to the faulty signals collected from the rotor test platform, the grey correlation adiagnosis of steam turbine shafting in this paper. The reference faulty matrix and the calculation model of grey correlation degree was established based on three kinds of information entropy. The analysis shows that grey correlation analysis is a useful method for fault diagnosis of shafting and can be used as a quantitative index for fault diagnosis.展开更多
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 significance.Diesel engines generally oper...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 significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.展开更多
Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine...Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.展开更多
The cone-shaped kernel distributions of vibration acceleration signals, whichwere acquired from the cylinder head in eight different states of a valve train, were calculatedand displayed in grey images. Probabilistic ...The cone-shaped kernel distributions of vibration acceleration signals, whichwere acquired from the cylinder head in eight different states of a valve train, were calculatedand displayed in grey images. Probabilistic Neural Networks ( PAW) was used to classify the imagesdirectly after the images were normalized. By this way, the problem of fault diagnosis for a valvetrain was transferred to the classification of time-frequency images. As there is no need to extractfeatures from time-frequency images before classification, the fault diagnosis process is highlysimplified. The experimental results show that the vibration signals can be classified accurately bythe proposed methods.展开更多
This paper mainly introduces the basic principles,the methods and the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust faults. The test-bed for monitoring diesel engine exhau...This paper mainly introduces the basic principles,the methods and the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust faults. The test-bed for monitoring diesel engine exhaust faults by thermal infrared imager has been designed. In different running conditions, the exterior surface radiation temperatures of the exhaust pipe of the 6135G-1 diesel engine have been measured by infrared imaging system. According to the principle of infrared temperature measurement, the real temperatures of the exterior surface of the exhaust pipe have been calculated. Based on the principle of heat transfer, the method of calculating the exhaust temperatures according to the exterior surface radiation temperatures of exhaust pipe measured by thermal infrared imager is built. The relationship between diesel engine exhaust temperatures and faults has been analyzed. It is shown that the application of infrared inspection and diagnosis to the identifying of diesel engine exhaust faults is feasible and effective.展开更多
In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases f...In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases frequently. The components will generate enormous log information, including running reports and fault information. The sheer quantity of data is a great challenge for analysis relying on the manual method. In this paper, we implement a management and analysis system of log information, which can assist system administrators to understand the real-time status of the entire system, classify logs into different fault types, and determine the root cause of the faults. In addition, we improve the existing fault correlation analysis method based on the results of system log classification. We apply the system in a cloud computing environment for evaluation. The results show that our system can classify fault logs automatically and effectively. With the proposed system, administrators can easily detect the root cause of faults.展开更多
Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature ex...Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.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
基金supported by Liaoning Technical University for out-standing young teachers
文摘The diesel locomotive plays an important role in the field of transport, and the engine maintenance work is the prerequisite and gnarantee for the locomotive normal working. In this paper, we first establish the fault tree model of locomotive engine 16V240ZJ on the basis of engine non-start as the top event. Then we combines the fitzzy mathematics the- ory and fault tree analysis method for failure diagnosis of 16V240ZJ engine's abnormal start-up. We obtained the fuzzy probability curve and top events probability confidence interval by analyzing the fuzzy fault tree qualitatively and quantitatively. It provides a fuzzy analysis basis for solving the problem of 16V240ZJ engine's abnormal start-up.
基金supported by Marie Curie International Incoming Fellowship within the 7th European Community Framework Programme (Grant No.FP7-PIIF-GA-2013629051)the National Natural Science Foundation of China (No.91324003)Social Science Foundation of the China(10zd&014,12AZD102)
文摘The concept of average incremental correlation degree is put forward.It has been proved that the average incremental correlation model has such properties as parallelism,consistency,affine,affine transformation isotonicity and interference factors independence,and it will not lead to changes of the sequence order relation because of the data transformation.Therefore,the new model keeps good stability.Finally,the incremental average correlation model is applied to failure model analysis of equipment,and an ideal diagnostic effect is obtained.
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Intake system of diesel engine is a strong nonlinear system, and it is difficult to establish accurate model of intake system; and bias fault and precision degradation fault of MAP of diesel engine can't be diagnosed easily using model-based methods. Thus, a fault diagnosis method based on Elman neural network observer is proposed. By comparing simulation results of intake pressure based on BP network and Elman neural network, lower sampling error magnitude is gained using Elman neural network, and the error is less volatile. Forecast accuracy is between 0.015?0.017 5 and sample error is controlled within 0?0.07. Considering the output stability and complexity of solving comprehensively, Elman neural network with a single hidden layer and with 44 nodes is presented as intake system observer. By comparing the relations of confidence intervals of the residual value between the measured and predicted values, error variance and failures in various fault types. Then four typical MAP faults of diesel engine can be diagnosed: complete failure fault, bias fault, precision degradation fault and drift fault. The simulation results show: intake pressure is observable and selection of diagnostic strategy parameter reasonably can increase the accuracy of diagnosis;the proposed fault diagnosis method only depends on data and structural parameters of observer, not depends on the nonlinear model of air intake system. A fault diagnosis method is proposed not depending system model to observe intake pressure, and bias fault and precision degradation fault of MAP of diesel engine can be diagnosed based on residuals.
基金supported by the National Natural Science Foundation of China(NSFC) under Grant No. 50775083 and Grant No.50721005
文摘The vibration fault, one of the common faults in the steam turbine generator unit, brings great damage to the production and the running process. It is well known that the information entropy is to describe the degree of indeterminacy of the system, so the information entropy can be used to measure Despite its efficiency, one kind of information entropy is just enabled to identify make up for this limitation, based on nalysis was studied for vibration fault the vibration condition of the unit. certain part of the faults. In order to the faulty signals collected from the rotor test platform, the grey correlation adiagnosis of steam turbine shafting in this paper. The reference faulty matrix and the calculation model of grey correlation degree was established based on three kinds of information entropy. The analysis shows that grey correlation analysis is a useful method for fault diagnosis of shafting and can be used as a quantitative index for fault diagnosis.
基金Supported by the National Key Research and Development Plan(No.2016YFF0203305)the Fundamental Research Funds for the Central Universities(No.JD1912,ZY1940)Double First-rate Construction Special Funds(No.ZD1601).
文摘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 significance.Diesel engines generally operate in various stable operating conditions,which have important influence on the fault diagnosis.However,many fault diagnosis methods have been put forward under specific stable operating condition based on vibration signal.As the result of great impact caused by operating conditions,corresponding diagnosis models cannot deal with the fault diagnosis under different operating conditions with required accuracy.In this paper,a fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval support vector machine(SVM)is proposed.Firstly,the fault features with weak condition sensitivity have been extracted according to the influence analysis of fault on vibration signal.Moreover,soft interval constraint has been applied to SVM algorithm to reduce the random influence of vibration signal on fault features.In addition,different machine learning algorithms based on different feature sets are adopted to conduct the fault diagnosis under different operating conditions for comparison.Experimental results show that the proposed method is applicable for fault diagnosis under variable operating condition with good accuracy.
文摘Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.
文摘The cone-shaped kernel distributions of vibration acceleration signals, whichwere acquired from the cylinder head in eight different states of a valve train, were calculatedand displayed in grey images. Probabilistic Neural Networks ( PAW) was used to classify the imagesdirectly after the images were normalized. By this way, the problem of fault diagnosis for a valvetrain was transferred to the classification of time-frequency images. As there is no need to extractfeatures from time-frequency images before classification, the fault diagnosis process is highlysimplified. The experimental results show that the vibration signals can be classified accurately bythe proposed methods.
文摘This paper mainly introduces the basic principles,the methods and the applications of infrared technique in the diagnosis and prediction of diesel engine exhaust faults. The test-bed for monitoring diesel engine exhaust faults by thermal infrared imager has been designed. In different running conditions, the exterior surface radiation temperatures of the exhaust pipe of the 6135G-1 diesel engine have been measured by infrared imaging system. According to the principle of infrared temperature measurement, the real temperatures of the exterior surface of the exhaust pipe have been calculated. Based on the principle of heat transfer, the method of calculating the exhaust temperatures according to the exterior surface radiation temperatures of exhaust pipe measured by thermal infrared imager is built. The relationship between diesel engine exhaust temperatures and faults has been analyzed. It is shown that the application of infrared inspection and diagnosis to the identifying of diesel engine exhaust faults is feasible and effective.
基金This work was supported by the National Basic Research 973 Program of China under Grant No. 2014CB340600, the National Natural Science Foundation of China under Grant No. 61272072, and the Program for New Century Excellent Talents in University of China under Grant No. NCET-13-0241.
文摘In modern computer systems, system event logs have always been the primary source for checking system status. As computer systems become more and more complex, the interaction between software and hardware increases frequently. The components will generate enormous log information, including running reports and fault information. The sheer quantity of data is a great challenge for analysis relying on the manual method. In this paper, we implement a management and analysis system of log information, which can assist system administrators to understand the real-time status of the entire system, classify logs into different fault types, and determine the root cause of the faults. In addition, we improve the existing fault correlation analysis method based on the results of system log classification. We apply the system in a cloud computing environment for evaluation. The results show that our system can classify fault logs automatically and effectively. With the proposed system, administrators can easily detect the root cause of faults.
基金supported by National Natural Science Foundation of China (No. 61763037)Inner Mongolia Autonomous Region Natural Science Foundation of China(No. 2019LH06007)Science and Technology Plan Project of Inner Mongolia (No. 2019,2020GG028)。
文摘Working conditions of rolling bearings of wind turbine generators are complicated, and their vibration signals often show non-linear and non-stationary characteristics. In order to improve the efficiency of feature extraction of wind turbine rolling bearings and to strengthen the feature information, a new structural element and an adaptive algorithm based on the peak energy are proposed,which are combined with spectral correlation analysis to form a fault diagnosis algorithm for wind turbine rolling bearings. The proposed method firstly addresses the problem of impulsive signal omissions that are prone to occur in the process of fault feature extraction of traditional structural elements and proposes a "W" structural element to capture more characteristic information. Then, the proposed method selects the scale of multi-scale mathematical morphology, aiming at the problem of multi-scale mathematical morphology scale selection and structural element expansion law. An adaptive algorithm based on peak energy is proposed to carry out morphological scale selection and structural element expansion by improving the computing efficiency and enhancing the feature extraction effect.Finally, the proposed method performs spectral correlation analysis in the frequency domain for an unknown signal of the extracted feature and identifies the fault based on the correlation coefficient. The method is verified by numerical examples using experimental rig bearing data and actual wind field acquisition data and compared with traditional triangular and flat structural elements. The experimental results show that the new structural elements can more effectively extract the pulses in the signal and reduce noise interference,and the fault-diagnosis algorithm can accurately identify the fault category and improve the reliability of the results.
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.