期刊文献+
共找到2,216篇文章
< 1 2 111 >
每页显示 20 50 100
An Effective Fault Diagnosis Method for Aero Engines Based on GSA-SAE 被引量:3
1
作者 CUI Jianguo TIAN Yan +4 位作者 CUI Xiao TANG Xiaochu WANG Jinglin JIANG Liying YU Mingyue 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2020年第5期750-757,共8页
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
关键词 aero engines fault diagnosis optimization algorithm of gravitational search algorithm(GSA) stack autoencoder(SAE)network
下载PDF
A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions
2
作者 Qixin Lan Binqiang Chen Bin Yao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第8期2017-2037,共21页
Many kinds of electrical equipment are used in civil and building engineering.The motor is one of the main power components of this electrical equipment,which can provide stable power output.During the long-term use o... Many kinds of electrical equipment are used in civil and building engineering.The motor is one of the main power components of this electrical equipment,which can provide stable power output.During the long-term use of motors,various motor faults may occur,which affects the normal use of electrical equipment and even causes accidents.It is significant to apply fault diagnosis for the motors at the construction site.Aiming at the problem that signal data of faulty motor lack diversity,this research designs a multi-layer perceptron Wasserstein generative adversarial network,which is used to enhance training data through distribution fusion.A discrete wavelet decomposition algorithm is employed to extract the low-frequency wavelet coefficients from the original motor current signals.These are used to train themulti-layer perceptron Wasserstein generative adversarial model.Then,the trainedmodel is applied to generate fake current wavelet coefficients with the fused distribution.A motor fault classification model consisting of a feature extractor and pattern recognizer is built based on perceptron.The data augmentation experiment shows that the fake dataset has a larger distribution than the real dataset.The classification model trained on a real dataset,fake dataset and combined dataset achieves 21.5%,87.2%,and 90.1%prediction accuracy on the unseen real data,respectively.The results indicate that the proposed data augmentation method can effectively generate fake data with the fused distribution.The motor fault classification model trained on a fake dataset has better generalization performance than that trained on a real dataset. 展开更多
关键词 Motor fault diagnosis data augmentation wavelet decomposition generative adversarial network civil and building engineering
下载PDF
Sensor Fault Diagnosis and Reconstruction of Engine Control System Based on Autoassociative Neural Network 被引量:7
3
作者 黄向华 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2004年第1期23-27,共5页
The topology and property of Autoassociative Neural Networks(AANN) and theAANN's application to sensor fault diagnosis and reconstruction of engine control system arestudied. The key feature of AANN is feature ext... The topology and property of Autoassociative Neural Networks(AANN) and theAANN's application to sensor fault diagnosis and reconstruction of engine control system arestudied. The key feature of AANN is feature extract and noise filtering. Sensor fault detection isaccomplished by integrating the optimal estimation and fault detection logic. Digital simulationshows that the scheme can detect hard and soft failures of sensors at the absence of models forengines which have performance deteriorate in the service life, and can provide good analyticalredundancy. 展开更多
关键词 autoassociative neural network engine sensor fault diagnosis analyticalredundancy
下载PDF
Aircraft Engine Gas Path Fault Diagnosis Based on Hybrid PSO-TWSVM 被引量:6
4
作者 Du Yanbin Xiao Lingfei +1 位作者 Chen Yusheng Ding Runze 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2018年第2期334-342,共9页
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
关键词 aircraft engines fault diagnosis TWIN support VECTOR machine (TWSVM) hybrid PARTICLE SWARM optimization (HPSO) algorithm mixed KERNEL function
下载PDF
Fault Diagnosis for Manifold Absolute Pressure Sensor(MAP) of Diesel Engine Based on Elman Neural Network Observer 被引量:17
5
作者 WANG Yingmin ZHANG Fujun +1 位作者 CUI Tao ZHOU Jinlong 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2016年第2期386-395,共10页
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
关键词 neural network diesel engine intake system fault diagnosis threshold value
下载PDF
ENGINE SENSOR FAULT DIAGNOSIS USING MAIN AND DECENTRALIZED NEURAL NETWORKS 被引量:1
6
作者 黄向华 孙健国 《Chinese Journal of Aeronautics》 SCIE EI CSCD 1998年第4期54-57,共4页
This Paper presents a methodology for solving the sensor failure detection, isolation and accommodation of aeroengine control systems using on line learning neural networks(NN), which has one main NN and a set of dec... This Paper presents a methodology for solving the sensor failure detection, isolation and accommodation of aeroengine control systems using on line learning neural networks(NN), which has one main NN and a set of decentralized NNs. Changes in the system dynamics are monitored by the on line learning NN. When a failure occurs in some sensor, the sensor failure detection can be accomplished with high precision, and the sensor failure accommodation can be achieved by replacing the value from the failed sensor with its estimate from the decentralized NN. By integrating the optimal estimation and failure logic, this method can detect soft failures. Simulation of one kind of turboshaft engine control system with this multiple neural network architecture shows that the ANN developed can detect and isolate hard and soft sensor failures timely and provide accurate accommodation. 展开更多
关键词 faultS diagnosis engine sensor analytical redundancy neural nets
下载PDF
Fuzzy Fault Diagnosis of a Diesel Engine Non-start 被引量:1
7
作者 LIU Ke-ming YANG Wei-hong +2 位作者 XU Guang-ming XU Wei-guo GA O Lei-fu 《International Journal of Plant Engineering and Management》 2009年第3期147-150,共4页
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
关键词 diesel engine FUZZY fault tree diagnosis
下载PDF
THE FAULT DIAGNOSIS TECHNOLOGY BASED ON FRACTAL GEOMETRY FOR LOGGING TRUCK ENGINE
8
作者 杜元虎 朱建新 吴跃成 《Journal of Northeast Forestry University》 SCIE CAS CSCD 1996年第4期65-67,共3页
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
关键词 Logging truck fault diagnosis Fractal Fractal dimension enginE
下载PDF
Establishment and Optimization of State Feature System of Diesel Engine Fault Diagnosis
9
作者 Liu Min-lin Liu Bo-yun College of Power Engineering,Naval University of Engineering,Wuhan 430033, China 《中国舰船研究》 2010年第3期47-51,共5页
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
关键词 diesel engine fault diagnosis bootstrap method genetic algorithm.
下载PDF
Fault diagnosis of diesel engine valve clearance under variable operating condition based on soft interval SVM
10
作者 Jiang Zhinong Lai Yuehua +2 位作者 Mao Zhiwei Zhang Jinjie Lai Zehua 《High Technology Letters》 EI CAS 2021年第2期111-120,共10页
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
关键词 diesel engine fault diagnosis operating condition support vector machine(SVM)
下载PDF
Design and Application of a Reconstruction System for Engineering Equipment Fault Diagnosis
11
作者 LI Jie WANG Jian +1 位作者 ZHANG Zhi DAI Ling 《International Journal of Plant Engineering and Management》 2009年第3期136-141,共6页
It is difficult to develop the corresponding fault diagnosis system and the software reusability is bad because the engineering equipment types are so many and their performance is diverse. This paper discussed the so... It is difficult to develop the corresponding fault diagnosis system and the software reusability is bad because the engineering equipment types are so many and their performance is diverse. This paper discussed the solution to engineering equipment integrated fault diagnosis system based on component technology, put forward the sys- tem model and gave the system frame design process and working principle. The software was designed based on the three-layer hierarchy. It is easy to rease and maintain, and the operation of the software is simple. A kind of new theory and method to develop the engineering equipment fault diagnosis system for the future was provided. 展开更多
关键词 engineering equipment component technology fault diagnosis system reconstruction
下载PDF
Condition Monitoring and Fault Diagnosis Based on Rough Set Theory 被引量:1
12
作者 Li Xiong Li Shengli Xu Zongchang 《仪器仪表学报》 EI CAS CSCD 北大核心 2005年第z1期781-783,共3页
In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm bas... In order to raise the efficiency,automatization and intelligentization of condition monitoring and fault diagnosis for complex equipment systems,rough set theory is used to the field. A feature reduction algorithm based on rough set theory is adopted to extract condition information in monitoring and diagnosis for an engine,so that the technology condition monitoring parameters are optimized. The decision tables for each fault source are built and the diagnosis rules rooting in rough set reduction is applied to carry through intelligent fault diagnosis. The cases studied show that rough set method in condition monitoring and fault diagnosis can lighten the work burden in feature selection and afford advantages for autonomic learning and decision during diagnosis. 展开更多
关键词 CONDITION monitoring fault diagnosis ROUGH SET theory enginE
下载PDF
Data fusion for fault diagnosis using multi-class Support Vector Machines 被引量:1
13
作者 胡中辉 蔡云泽 +1 位作者 李远贵 许晓鸣 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2005年第10期1030-1039,共10页
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
关键词 Data fusion fault diagnosis Multi-class classification Multi-class Support Vector Machines Diesel engine
下载PDF
Fault diagnosis of a marine power-generation diesel engine based on the Gramian angular field and a convolutional neural network
14
作者 Congyue LI Yihuai HU +1 位作者 Jiawei JIANG Dexin CUI 《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》 SCIE EI CAS CSCD 2024年第6期470-482,共13页
Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective featu... Marine power-generation diesel engines operate in harsh environments.Their vibration signals are highly complex and the feature information exhibits a non-linear distribution.It is difficult to extract effective feature information from the network model,resulting in low fault-diagnosis accuracy.To address this problem,we propose a fault-diagnosis method that combines the Gramian angular field(GAF)with a convolutional neural network(CNN).Firstly,the vibration signals are transformed into 2D images by taking advantage of the GAF,which preserves the temporal correlation.The raw signals can be mapped to 2D image features such as texture and color.To integrate the feature information,the images of the Gramian angular summation field(GASF)and Gramian angular difference field(GADF)are fused by the weighted average fusion method.Secondly,the channel attention mechanism and temporal attention mechanism are introduced in the CNN model to optimize the CNN learning mechanism.Introducing the concept of residuals in the attention mechanism improves the feasibility of optimization.Finally,the weighted average fused images are fed into the CNN for feature extraction and fault diagnosis.The validity of the proposed method is verified by experiments with abnormal valve clearance.The average diagnostic accuracy is 98.40%.When−20 dB≤signal-to-noise ratio(SNR)≤20 dB,the diagnostic accuracy of the proposed method is higher than 94.00%.The proposed method has superior diagnostic performance.Moreover,it has a certain anti-noise capability and variable-load adaptive capability. 展开更多
关键词 Multi-attention mechanisms(MAM) Convolutional neural network(CNN) Gramian angular field(GAF) Image fusion Marine power-generation diesel engine fault diagnosis
原文传递
Research on Gear-broken Fault Diagnosis in a Tank Gearbox 被引量:1
15
作者 安钢 李胜利 +1 位作者 樊新海 赵沛然 《Defence Technology(防务技术)》 SCIE EI CAS 2007年第4期251-255,共5页
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
关键词 坦克 变速箱 齿轮断裂 故障诊断 特征提取
下载PDF
Elementary Analysis on the Technological Features of an Engineering Equipment Facile Diagnosis System 被引量:1
16
作者 LI Gui San Department of Mechanical Engineering, Liaoyang Petrochemical Polytechnic School, Liaoyang 111003, P.R.China 《International Journal of Plant Engineering and Management》 2001年第4期220-225,共6页
From the point of systemic engineering, the general properties of an engineering equipment fault diagnosis system and the studying object of diagnosis engineering -were discussed. With the developing course of fault d... From the point of systemic engineering, the general properties of an engineering equipment fault diagnosis system and the studying object of diagnosis engineering -were discussed. With the developing course of fault diagnosis technology, the relationship between facile diagnosis system and diagnosis engineering were also discussed. The basic structure and feature of a facile diagnosis system -were discussed, and the isomorphic of a facile diagnosis system and precise diagnosis system -was presented. The facile diagnosis requires the perfection of method , pertinence and apriority of knowledge, adaptability of the object being diagnosed and the approach to the aim of the diagnosis result, as -well as th? outstanding of main functions. 展开更多
关键词 engineering equipment fault diagnosis system engineering.
下载PDF
Fault Diagnosis of Bearing Based on Integration of Nonlinear Geometric Invariables
17
作者 关贞珍 郑海起 《Defence Technology(防务技术)》 SCIE EI CAS 2012年第4期230-235,共6页
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
关键词 机械零件 转动机件 支枢 轴承
下载PDF
Fault Diagnosis for a Diesel Valve Train Based on Time-Freq uency Analysis and Probabilistic Neural Networks
18
作者 WANGCheng-dong WEIRui-xuan +1 位作者 ZHANGYou-yun XIAYong 《International Journal of Plant Engineering and Management》 2004年第3期155-163,共9页
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
关键词 diesel engine fault diagnosis time-frequency analysis probabilistic neuralnetworks
下载PDF
A Robust Approach of Multi-sensor Fusion for Fault Diagnosis Using Convolution Neural Network
19
作者 Jiahao Sun Xiwen Gu +3 位作者 Jun He Shixi Yang Yao Tu Chenfang Wu 《Journal of Dynamics, Monitoring and Diagnostics》 2022年第2期103-110,共8页
Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary informa... Multi-sensor measurement iswidely employed in rotatingmachinery to ensure the safety ofmachines.The information provided by the single sensor is not comprehensive.Multi-sensor signals can provide complementary information in characterizing the health condition of machines.This paper proposed a multi-sensor fusion convolution neural network(MF-CNN)model.The proposed model adds a 2-D convolution layer before the classical 1-D CNN to automatically extract complementary features of multi-sensor signals and minimize the loss of information.A series of experiments are carried out on a rolling bearing test rig to verify the model.Vibration and sound signals are fused to achieve higher classification accuracy than typical machine learning model.In addition,the model is further applied to gas turbine abnormal detection,and shows great robustness and generalization. 展开更多
关键词 deep learning engineering application fault diagnosis multi-sensor fusion
下载PDF
Fault Feature Extraction of Diesel Engine Based on Bispectrum Image Fractal Dimension
20
作者 Jian Zhang Chang-Wen Liu +2 位作者 Feng-Rong Bi Xiao-Bo Bi Xiao Yang 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2018年第2期216-226,共11页
Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early ... Fault feature extraction has a positive effect on accurate diagnosis of diesel engine. Currently, studies of fault feature extraction have focused on the time domain or the frequency domain of signals. However, early fault signals are mostly weak energy signals, and time domain or frequency domain features will be overwhelmed by strong back?ground noise. In order consistent features to be extracted that accurately represent the state of the engine, bispectrum estimation is used to analyze the nonlinearity, non?Gaussianity and quadratic phase coupling(QPC) information of the engine vibration signals under different conditions. Digital image processing and fractal theory is used to extract the fractal features of the bispectrum pictures. The outcomes demonstrate that the diesel engine vibration signal bispectrum under different working conditions shows an obvious differences and the most complicated bispectrum is in the normal state. The fractal dimension of various invalid signs is novel and diverse fractal parameters were utilized to separate and characterize them. The value of the fractal dimension is consistent with the non?Gaussian intensity of the signal, so it can be used as an eigenvalue of fault diagnosis, and also be used as a non?Gaussian signal strength indicator. Consequently, a symptomatic approach in view of the hypothetical outcome is inferred and checked by the examination of vibration signals from the diesel motor. The proposed research provides the basis for on?line monitoring and diagnosis of valve train faults. 展开更多
关键词 engine fault diagnosis Bispectrum image processing FRACTAL Signal processing
下载PDF
上一页 1 2 111 下一页 到第
使用帮助 返回顶部