基于模糊能量自学习的汽车发动机传感器节点故障诊断方法研究

Car Engine Sensor Network Fault Diagnosis Methods Research Based on the Fuzzy Energy Self Learning

针对传统汽车发动机传感器网络节点故障检测效率低,抗干扰差的问题。提出了一种基于模糊能量自学习的汽车发动机传感器故障检测方法。该方法先运算传感器网络中的节点的能量,再利用基于U-H模糊模型优化方法对相关的模糊能量进行自学习,最终获取传感器网络中节点的能量算好,从而完成对汽车发动机传感器节点能量耗尽故障的有效诊断。研究结果表明,基于模糊能量自学习的发动机传感器故障诊断方法有效性高,克服了外界干扰对故障诊断的不利影响。

According to the traditional motor car engine sensor network node fault detection efficiency is low,the problem of poor anti-jamming.Puts forward a based on fuzzy energy self learning car engine sensor fault detection method.The method first operation in the sensor networks node energy,and then based on U-H fuzzy model optimization method to related fuzzy energy self learning,and finally get in sensor networks node energy is good,thus completing the car engine sensor node energy depletion fault diagnosis effectively.The research results show that based on fuzzy energy self learning engine sensor fault diagnosis methods of high effectiveness,overcoming the interference to the harmful effects of fault diagnosis.