基于ANFIS的汽车发动机振动参数故障诊断云模型

Li-fang Kong, Rong-ling Shi, Tian Zhang, Hao Wei
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引用次数: 0

摘要

为了解决振动参数的故障诊断问题,应用自适应神经模糊推理系统(ANFIS)建立了汽车发动机的故障诊断模型,并推导了扇出云模型,输出结果进行了延续。通过对建立的诊断模型与发动机试验数据的验证,发现识别准确率从88.75%提高到99.68%,训练误差从0.001683下降到0.0011526。仿真结果表明,改进后的ANFIS模型的拟合能力、收敛速度和识别精度均优于ANFIS。这样可以有效地识别汽车发动机的偶然故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Vibration Parameter Fault Diagnosis Cloud Model for Automobile Engine Based on ANFIS
In order to solve the fault diagnosis problem of Vibration Parameter, Adaptive Neuro-Fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine and induce cloud model of fan-out, outputting results are continued. Through verification of the built diagnosis model with data of engine tests, it has been found that the recognition accuracy increase from 88.75% to 99.68%, training error falling from 0.001683 to 0.0011526. Simulation results show that the fitting ability, convergence speed and recognition accuracy of improved ANFIS model are all superior to ANFIS. So a contingent fault of automobile engine can be identified effectively.
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