The fault diagnosis of vehicle power system that the structure and characteristics of components are complex, each module and internal modules exist coupling, cross-linked mutual relations and the uncertainties, the system status and working conditions are difficult to describe by precisely mathematical model, and test cost expensive, less fault samples. Thus its fault diagnosis is the decision problem of uncertain information in a small sample. it is proposed that combining multi-signal flow graph model with Bayesian network fault diagnosis method. The fault diagnosis model of power system and the corresponding Bayesian network structure are built, which achieve the fault diagnosis of power system, Diagnosis example shows that the method of the vehicle power has a higher failure troubleshooting capabilities of the system single and multiple faults.
In order to solve the problem of ECG being complex, and the problem of abnormal ECG signal analysis standards being not uniform, this paper analyzes Lin Zetao’s clustering algorithm research achievements in the classification of abnormal heart rate. According to the characteristics of the clustering algorithm which is suitable for dealing with the rare data and the large amounts of data, this paper makes the analysis of the method of setting parameters in key process of the application of the clustering algorithm. This paper proposes an accurate clustering algorithm (LCFCM) about abnormal ECG which combines logical judgment, cluster analysis and fuzzy clustering together. Based on the algorithm design requirements, this algorithm perfects the logical judgment criteria and the key technologies of the ECG waveform vector extraction method. Ultimately, the MIT-BIH database is used as the sample to make experiment. The experiment shows that, this paper proposes LCFCM algorithm, whose accurate rate toward the classification of the abnormal heart rate reaches 93%. And at the same time, the good self-adaptive algorithm on different individuals ECG, has good practical value.
Vehicle radiator is the important heat-exchange equipment to improve the power, economy and reliability of the engine and transmission device, the selection and design of radiator is limited by the vehicle cabin space. In order to meet the cooling requirements, radiator core shape design is optimized. During the radiator core shape optimal design, there is a manual repetitive operation problem when Fluent pre-processing software Gambit establishes 3D mesh model according to the different testing points, Fluent secondary development using VC++6.0 programming language develops the radiator core shape parametric modeling software and improves development efficiency.
Aiming the problem of the armored vehicle's gun control system that there are many kinds of internal devices, complex fault reasons ,but no all-around and online fault diagnosis and state inspection mean, The automatic test platform for the gyroscope group with performance test and fault diagnosis for component and circuit is designed .The platform based on dependency matrix and optimal criterion of the maximum failure feature information entropy optimize test points ,choose optimal test points design. Performance test module is created and provides test result information for fault dictionary in fault diagnosis module. Automatic test platform is able to locate the circuit component failure.The platform is tested by actual vehicle experiment, and the results prove the reliability and validity of the platform.
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