CN116202929A - 训练人工智能ai模型并确定过滤器剩余使用寿命的方法 - Google Patents
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Abstract
训练人工智能AI模型并确定过滤器剩余使用寿命的方法。本公开提出了一种训练人工智能(AI)模型并使用经训练的AI模型检测选择性催化还原(SCR)***中过滤器(103)的堵塞程度和预测过滤器(103)的剩余使用寿命(RUL)的方法。SCR***包括柴油废气流体(DEF)罐(101)、DEF过滤器(103)和至少一个泵(102)单元。使用输入来训练AI模型,所述输入诸如是取决于泵(102)的电流或电压特性的参数以及一个或多个***操作参数。
Description
技术领域
本公开涉及一种训练人工智能(AI)模型并检测选择性催化还原(SCR)***中过滤器堵塞程度的方法,从而预测剩余使用寿命,并且通过了解劣化程度来实现有效的故障排除和增强/适配***运转。
背景技术
选择性催化还原(SCR)是柴油发动机中使用的排放控制技术***。在该技术中,液体还原剂(水溶液)被注入柴油发动机的废气流中。液体还原剂通常是汽车级尿素,另外称为柴油废气流体(DEF)。DEF引发化学反应,这将氮氧化物(NOx)转化为氮气、水和微量二氧化碳(CO2),然后其通过车辆排气管排出。
DEF存储在罐中,并通过泵单元输送到配量模块,配量模块将其配量到废气流中。DEF过滤器安装在SCR模块中,以确保DEF不具有使泵劣化的污染物,或者喷射器和配量到废气气流中的DEF不含污染物。在长久使用之后,DEF过滤器被堵塞,并且需要更换。对于车辆用户而言需要知道该DEF过滤器堵塞程度,以便进行有效的预测诊断。随着数据科学方面的进步,可以使用历史数据结合统计建模、数据挖掘技术和机器学习来预测未来的结果,诸如组件的故障。因此,需要在SCR***中应用预测分析来确定过滤器的剩余使用寿命。
题为“Method to detect clogging in a fuel filter of a vehicle”的专利申请IN202041006256AA公开了一种使用自学习算法检测燃料过滤器堵塞程度的方法。在步骤201中,ECU(105)接收一组参数的测量值。在步骤202中,ECU(105)存储矩阵,该矩阵包括该组参数中的每一个与压差值之间的相关系数。在步骤203中,ECU(105)基于该组参数的测量值和矩阵计算压差的预测值。在步骤204中,ECU(105)用实际压差的动态值来线性化压差的预测值。实际压差的动态值是使用在各种实例处测量的压差的实际值从自学习算法中导出的。在步骤205中,ECU(105)向车辆用户指示过滤器堵塞程度的值。
附图说明
参考以下附图描述了本发明的实施例:
图1描绘了车辆中选择性催化还原(SCR)***的一部分(100);
图2图示了用于训练人工智能(AI)模型以检测选择性催化还原(SCR)***中过滤器(103)的堵塞程度的方法步骤(200);以及至少
图3图示了用于预测选择性催化还原(SCR)***中过滤器(103)的剩余使用寿命的方法步骤(300)。
具体实施方式
图1描绘了SCR***的一部分(100)。除了其他下游组件(诸如配量模块和本领域技术人员已知的其他组件),SCR***还包括柴油废气流体(DEF)罐(101)、DEF过滤器(103)和至少一个泵(102)单元。DEF罐(101)存储称为柴油废气流体或尿素溶液的水溶液。DEF过滤器(103)位于DEF罐(101)的下游,并过滤(103)尿素溶液中的污染物。过滤器(103)将水溶液输送到泵(102)单元。位于DEF过滤器(103)下游的泵(102)单元向配量模块供应柴油废气流体。配量模块调节导入废气气管中的DEF量。一个或多个传感器被配置为测量泵(102)操作参数。这些传感器与电子控制单元(ECU(104))通信。该组传感器被配置为测量泵(102)操作参数。
ECU(104)是使用母板互连的一个或多个微芯片或集成电路、硬连线逻辑、由存储器设备存储并由微处理器执行的软件、固件、专用集成电路(ASIC)和/或现场可编程门阵列(FPGA)的组合。ECU(104)与人工智能(AI)模型通信。在示例性实施例中,AI模块可以是ECU(104)的一部分。AI模块可以是嵌入在单个芯片中的软件,或是软件和硬件的组合,其中每个模块及其功能性由彼此连接的分离的独立芯片执行,以作为***运转。例如,神经网络芯片可以嵌入在ECU(104)内,所述神经网络芯片是专门化的硅芯片,其并入AI技术并用于机器学习。
一开始就应该理解,尽管示例性实施例在各图中图示并在下面描述,但是本公开决不应该限于附图中图示并在下面描述的示例性实现方式和技术。
图2图示了用于训练人工智能(AI)模型以检测选择性催化还原(SCR)***中过滤器(103)的堵塞程度的方法步骤。SCR***与根据图1解释的相同。重申所述SCR***包括泵(102)单元、所述过滤器(103)、一组传感器和至少一个电子控制单元(ECU(104))。过滤器(103)将水溶液输送到泵(102)单元,该组传感器被配置为测量泵(102)操作参数,该组传感器与ECU(104)通信。在训练阶段期间,跨过滤器(103)上采用压差传感器来获取跨过滤器(103)上的实时压降。在示例性实施例中,待训练的AI模型驻留在ECU(104)内。在其他实施例中,AI模型可以与ECU(104)分离,但是作为所述SCR***的一部分,即与ECU(104)通信。根据本公开的示例性实施例解释方法步骤(200)。
方法步骤201包括借助于第一组传感器测量一组泵(102)操作参数的实时值。该组泵(102)操作参数包括取决于泵(102)相对于时间的电流或电压特性的参数。例如,这些可以是运动开始点(BMP)处的电流值(iBMP)、机械停止点(MSP)处的电流值(iMSP)、到达BMP所花费的时间(tiBMP)、到达MSP所花费的时间(tiMSP)。同样,BSP和ESP点以相似的术语对应于吸入冲程。
BMP是驱动冲程中阀芯位移开始的时间点。MSP是驱动冲程中阀芯位移停止的时间点。驱动冲程是泵电枢中螺线管通电的结果。BSP是吸入冲程中阀芯位移开始的时间点。ESP是吸入冲程中阀芯位移停止的时间点。吸入冲程是泵电枢中螺旋弹簧势能的结果。
方法步骤202包括从ECU(104)检索测量的实时值的第二组参数的值。第二组参数包括一个或多个***操作参数,诸如电池电压以及至少待配量的水溶液的量。如果准确度需要,则可以包括诸如环境温度和/或压力的其他参数来训练模型。
方法步骤203包括将测量值和检索值作为输入馈送给AI模型。在本公开的一个实施例中,测量值和检索值被馈送到AI模型,其中在不同的入口侧压降水平下使用30个样本的窗口长度进行移动平均。AI模型的类型可以从组线性回归、K最近邻、随机森林分类器、决策树分类器、朴素贝叶斯分类器、支持向量机、神经网络等中选择。
方法步骤204包括依据从实验传感器接收的数据标记输出,以确定过滤器(103)堵塞。在训练阶段期间,附加地采用压差传感器来获取跨过滤器(103)上的实时压降,从而指示堵塞。当训练AI模型时,例如,使用数据集的70%-30%训练测试分割。意味着70%的时间输出用来自压差传感器的实际值标记,并且剩下的30%使其基于先前的输入和标记的输出反向传播和自己学习值。由各种模型在测试上获得准确度并且选择最适合的模型,或者将该模型送去用更多样的输入进行再训练,直到AI模型展现最佳准确度。
图3图示了用于预测选择性催化还原(SCR)***中过滤器(103)的剩余使用寿命的方法步骤。SCR***与根据图1解释的相同。重申所述SCR***包括泵(102)单元、所述过滤器(103)、一组传感器和至少一个电子控制单元(ECU(104))。过滤器(103)将水溶液输送到泵(102)单元,该组传感器被配置为测量泵(102)操作参数,该组传感器与ECU(104)通信。此外,在所述方法步骤(300)的示例性实现方式中,预训练的AI模型驻留在ECU(104)内。已经根据图2的方法步骤(200)训练了AI模型。
方法步骤301包括借助于第一组传感器测量一组泵(102)操作参数的实时值。第一组传感器测量取决于泵(102)相对于时间的电流或电压特性的该组泵(102)操作参数。这些值由传感器持续测量,并且数据被持续发送到ECU(104)。例如,这些可以是运动开始点(BMP)处的电流值(iBMP)、机械停止点(MSP)处的电流值(iMSP)、到达BMP所花费的时间(tiBMP)、到达MSP所花费的时间(tiMSP)。同样,BSP和ESP点以相似的术语对应于吸入冲程。
BMP是泵的驱动冲程中阀芯位移开始的时间点。MSP是驱动冲程中阀芯位移停止的时间点。驱动冲程是泵电枢中螺线管通电的结果。BSP是吸入冲程中阀芯位移开始的时间点。ESP是吸入冲程中阀芯位移停止的时间点。吸入冲程是泵电枢中螺旋弹簧势能的结果。
方法步骤302包括由ECU(104)检索测量的实时值的第二组参数的值。第二组参数包括一个或多个***操作参数,诸如电池电压以及至少待配量的水溶液的量。它可以扩展到包括其他参数,诸如环境温度和/或压力等。
方法步骤303将测量值和检索值馈送给预训练的AI模型。已经根据方法步骤(200)训练了AI模型。AI模型的类型可以根据***要求和训练期间展现的准确度从线性回归、K最近邻、随机森林分类器、决策树分类器、朴素贝叶斯分类器、支持向量机、神经网络等中选择。
方法步骤304包括执行预训练的机器学习模型,以确定过滤器(103)堵塞程度。AI模型运行经训练的算法来检测跨过滤器(103)上的压降程度。基于历史数据分析和过滤器(103)特性,检测到的压降与过滤器(103)堵塞程度相关。
方法步骤305将所确定的过滤器(103)堵塞程度与预先检索的历史数据进行比较,以预测过滤器(103)的剩余使用寿命(RUL)。可以使用从AI模型获得的过滤器(103)性能数据的历史来预测过滤器(103)的RUL。过滤器(103)正劣化的速率被持续跟踪并存储为历史数据。这可以通过外推法进一步扩展,以依据时间、公里数、DEF消耗量、再填充事件数等来确定过滤器(103)的RUL。这可以通过仪表板上的音频或视觉装置指示给用户。
本领域技术人员将领会,虽然这些方法步骤仅描述了实现目标的一系列步骤,但是这些方法技术可以由单个ECU(104)或几个的组合来实现。ECU(104)应该适配为驱动泵(102)单元;借助于第一组传感器测量一组泵(102)操作参数的实时值;检索测量的实时值的第二组参数的值;将测量值和检索值馈送到预训练的AI模型;从预训练的AI模型接收输出,以确定过滤器(103)堵塞程度;将所确定的过滤器(103)堵塞程度与预先记录的历史数据进行比较,以预测过滤器(103)的剩余使用寿命。
在实验和测试阶段中,在实验设置中测试了各种AI模型,诸如K最近邻、决策树分类器、随机森林分类器。测试设置使用了节流阀来代替由过滤器(103)创建的压降。使用阀产生的节流程度来控制造成的压降。在节流阀的任一侧采用压力传感器来监测压降。外部电流钳用于记录连续的泵(102)电流值。由于所使用的各种管和配件所致的水头损失已经被计算,并且在AI模型中补偿了与***实际工作条件的偏差。在泵(102)入口侧压降、泵(102)电压以及配量的量的不同水平下记录了测试数据。诸如K最近邻、决策树分类器、随机森林分类器的AI模型展现出范围从90-96%的准确度。
开发一种训练人工智能(AI)模型并使用其经训练的模型确定过滤器(103)的剩余使用寿命的方法的这种思想为用户提供了该过滤器(103)的实时劣化数据。使用本发明可以得出附加的推论,诸如基于过滤器(103)性能劣化的速率的DEF质量的比较评估。此外,由于压力管线泄漏、故障泵(102)或过滤器(103)劣化而出现的压力升高误差或负压误差——当它是由于过滤器(103)因为堵塞导致劣化而出现时——可以使用AI模型来查明。它还导致可以实现***运转的增强,如容积校正、附加冲程、频率改变等。
必须理解,以上详细描述中解释的实施例仅是说明性的,并不限制本发明的范围。对训练人工智能(AI)模型和确定过滤器(103)的剩余使用寿命的方法的任何修改都是设想到的,并且形成本发明的一部分。本发明的范围仅由权利要求限定。
Claims (9)
1.一种训练人工智能(AI)模型以检测选择性催化还原(SCR)***中过滤器(103)的堵塞程度的方法(200),所述SCR***包括泵(102)单元、所述过滤器(103)、一组传感器和至少一个电子控制单元(ECU(104)),过滤器(103)将水溶液输送到泵(102)单元,所述一组传感器被配置为测量泵(102)操作参数,所述一组传感器与ECU(104)通信,训练方法包括:
借助于第一组传感器测量(201)一组泵(102)操作参数的实时值;
从ECU(104)检索(202)测量的实时值的第二组参数的值;
将测量值和检索值作为输入馈送(203)到AI模型;
依据从实验传感器接收的数据标记(204)输出,以确定过滤器(103)堵塞。
2.如权利要求1所述的训练人工智能(AI)模型以检测过滤器(103)的堵塞程度的方法(200),其中一组泵(102)操作参数包括取决于泵(102)相对于时间的电流或电压特性的参数。
3.如权利要求1所述的训练人工智能(AI)模型以检测过滤器(103)的堵塞程度的方法(200),其中第二组参数包括一个或多个***操作参数,诸如电池电压以及至少待配量的水溶液的量。
4.一种预测选择性催化还原(SCR)***中过滤器(103)的剩余使用寿命的方法(300),所述SCR***包括泵(102)单元、所述过滤器(103)、一组传感器和至少一个电子控制单元(ECU(104)),过滤器(103)将水溶液输送到泵(102)单元,所述一组传感器被配置为测量泵(102)操作参数,所述一组传感器与ECU(104)通信,所述方法包括:
借助于第一组传感器测量(301)一组泵(102)操作参数的实时值;
由ECU(104)检索(302)测量的实时值的第二组参数的值;
将测量值和检索值馈送(303)到预训练的AI模型;
执行(304)预训练的AI模型以确定过滤器(103)堵塞程度;
将所确定的过滤器(103)堵塞程度与预先检索的历史数据进行比较(305),以预测过滤器(103)的剩余使用寿命。
5.如权利要求4所述的预测过滤器(103)的剩余使用寿命的方法(300),其中所述一组泵(102)操作参数包括取决于泵(102)相对于时间的电流或电压特性的参数。
6.如权利要求4所述的预测过滤器(103)的剩余使用寿命的方法(300),其中第二组参数包括一个或多个***操作参数,诸如电池电压以及至少待配量的水溶液的量。
7.一种适配为预测选择性催化还原(SCR)***中过滤器(103)的剩余使用寿命的电子控制单元(ECU(104)),所述SCR***包括泵(102)单元、所述过滤器(103)、一组传感器和至少一个电子控制单元(ECU(104)),过滤器(103)将水溶液输送到泵(102)单元,所述一组传感器被配置为测量一组泵(102)操作参数,所述一组传感器与ECU(104)通信,ECU(104)被配置为:
驱动泵(102)单元;
借助于第一组传感器测量一组泵(102)操作参数的实时值;
检索测量的实时值的第二组参数的值;
将测量值和检索值馈送到预训练的AI模型;
从预训练的AI模型接收输出,以确定过滤器(103)堵塞程度;
将所确定的过滤器(103)堵塞程度与预先记录的历史数据进行比较,以预测过滤器(103)的剩余使用寿命。
8.如权利要求7所述的适配为预测过滤器(103)的剩余使用寿命的电子控制单元(ECU(104)),其中,所述一组泵(102)操作参数包括取决于泵(102)相对于时间的电流或电压特性的参数。
9.如权利要求7所述的适配为预测过滤器(103)的剩余使用寿命的电子控制单元(ECU(104)),其中,第二组参数包括一个或多个***操作参数,诸如电池电压以及至少待配量的水溶液的量。
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