汽车性能监测的自适应神经模糊网络模型

Kong Li-fang, Li Dong, Zhao Ying
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引用次数: 0

摘要

提出了基于自适应神经模糊干扰系统的汽车发动机性能监测与故障检测模型。利用自适应神经模糊干扰系统的识别机制,根据熵的特性,利用熵对自适应神经模糊干扰系统的输入接口进行优化,并结合汽车发动机的特性性能,得到发动机性能的异常状态程度,实现对发动机性能的监测。该方法能灵敏、准确地反映发动机的整体性能。同时,该方法提高了发动机性能是否正常的识别率,发现了发动机潜在的前兆故障,防止了故障的扩散。通过对某型号康明斯6BT5.9发动机的监测,验证了该方法的有效性。
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Adaptive Neural Fuzzy Networks Model of Automobile Performance Monitoring
The model for automobile engine performance monitoring and fault detection was proposed based on adaptive neural fuzzy interference system. With recognition mechanism of the adaptive neural fuzzy interference system, according to the properties of entropy, this paper using entropy optimizes the input interface of adaptive neural fuzzy interference system , this model was combined with characteristic performance of automobile engine to attain the degrees of engine performance's abnormal state for monitoring engine performance. The approach can sensitively and accurately reflect the whole performance of the engine. Meanwhile, this method improves the rate of identifying whether the performance of the engine is normal or not, finds out the potential forepart fault of engine and prevents the spread of the fault. The validity of this method is testified by monitoring certain type of cummins engine 6BT5.9.
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