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作 者:王郡成 WANG JunCheng(BAIC Foton Automobile Co.,Ltd,Zhucheng 262233,China;Weifang Economic School,Zhucheng 262234,China)
机构地区:[1]北汽福田汽车股份有限公司时代事业部,山东诸城262233 [2]潍坊市经济学校,山东诸城262234
出 处:《计算机测量与控制》2023年第5期56-61,共6页Computer Measurement &Control
基 金:潍坊工商职业学院科研项目(2019A05)。
摘 要:常规驾驶室异常抖动故障诊断方法多采用神经网络算法,忽略了故障数据之间的关联性,使得诊断准确率较低;提出特定车速下轻型卡车驾驶室异常抖动故障诊断方法;利用数据采集器采集驾驶室在线信息数据,并采用周期性的方式对数据分类,分析故障数据之间的内部关系,解析收集的信息数据,判断车辆驾驶室运行状态,并采用故障树方法计算底部事件的概率重要度,实现驾驶室抖动故障诊断;利用对比实验对所提方法的诊断性能进行测试,结果显示,所提方法对于驾驶室异常抖动故障具有较高的诊断准确率,最高值为97%。Conventional cab abnormal jitter fault diagnosis methods mostly adopt neural network algorithm,ignoring the correlation between fault data,which makes the diagnosis accuracy low.A fault diagnosis method for the abnormal shaking of light truck cab at a specific speed is proposed.The data collector is used to collect the online information data of the cab,classify the data in a periodic manner,analyze the internal relationship between the fault data,explain the collected information data,judge the operation status of the vehicle cab.The fault tree method is used to calculate the probability importance of the bottom event,and realize the jitter fault diagnosis of the cab.The diagnostic performance of the proposed method is tested by the comparative experiment,the results show that the proposed method has a high diagnostic accuracy for the abnormal jitter fault of the cab with a maximum value of 97%.
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