首页> 外国专利> METHOD FOR PREDICTING THE MAINTENANCE OF COMPONENTS OF A COMBUSTION ENGINE BY MEANS OF A STRUCTURE-BORNE SOUND SENSOR

METHOD FOR PREDICTING THE MAINTENANCE OF COMPONENTS OF A COMBUSTION ENGINE BY MEANS OF A STRUCTURE-BORNE SOUND SENSOR

机译:结构型声传感器预测燃烧发动机各成分的方法

摘要

The method according to the invention for the maintenance prediction of components of an internal combustion engine by means of structure-borne noise sensor (2) offers the possibility of a maintenance forecast for analyzed components, in particular for the valve clearance (17) as a measure of the valve train wear of gas exchange valves. For this purpose, measurement signals from a structure-borne noise sensor (2) are first synchronized (10), decoded (11) and subjected to a signal transformation (12) in an associated evaluation electronics (3). The spectrogram (13) characteristic of the checked working cycle of the internal combustion engine, which is the result of the signal transformation (12), serves as an input variable for a regression model (14) which is specially adapted to the internal combustion engine used and which calculates the current valve clearance (17) and the others Development of this wear mechanism is forecast. If the method according to the invention is applied to identical internal combustion engines which are used to drive vehicles (1) of a vehicle fleet, the resource coordination for the maintenance of the entire fleet can thus be improved and a specific maintenance forecast can be made.
机译:根据本发明的通过结构噪声传感器(2)对内燃机的部件进行维护预测的方法提供了对所分析的部件,特别是对气门间隙(17)进行维护预测的可能性。测量换气阀的气门机构磨损。为此,来自结构噪声传感器(2)的测量信号首先在相关的评估电子设备(3)中进行同步(10),解码(11)并进行信号转换(12)。作为信号变换(12)的结果,内燃机检查的工作循环的频谱图(13)特性用作回归模型(14)的输入变量,该回归模型特别适合于内燃机可以使用它来计算当前的气门间隙(17)等。可以预测这种磨损机理的发展。如果将根据本发明的方法应用于用于驱动车队的车辆(1)的相同的内燃发动机,则可以改善用于整个车队的维护的资源协调,并且可以做出特定的维护预测。

著录项

  • 公开/公告号EP3644038A1

    专利类型

  • 公开/公告日2020-04-29

    原文格式PDF

  • 申请/专利权人 IAV GMBH;

    申请/专利号EP20190020590

  • 申请日2019-10-22

  • 分类号G01M15/12;F02D35/02;F02D41/22;F02D41/28;

  • 国家 EP

  • 入库时间 2022-08-21 11:38:39

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