CN111045411A - 用于动力传动系组件的故障诊断方法 - Google Patents
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Abstract
用于动力传动系组件的故障诊断方法可包括:由服务器基于指示动力传动系组件故障的振动大数据建立诊断模型;通过使用从振动大数据中提取的特征向量,由服务器对动力传动系组件中的故障组件进行分类和建模;由车辆的控制器通过输入命令或驾驶员的设置启动车辆的动力传动系组件的故障诊断;和通过将与在车辆行驶期间测量的车辆的动力传动系振动相对应的特征向量与在诊断模型中建模的振动大数据进行比较,由车辆的控制器诊断车辆的动力传动系组件的故障。
Description
相关申请的交叉引用
本申请要求2018年10月11日提交的韩国专利申请第10-2018-0121109号的优先权和权益,其全部内容通过引用结合于此。
技术领域
本公开涉及用于动力传动系组件的故障诊断方法。
背景技术
本节中的陈述仅提供与本公开相关的背景信息,并且可以不构成现有技术。
人脑由称为神经元的许多神经细胞组成,每个神经元通过称为突触的连接与数百或数千个其他神经元连接。每个神经元通过树突从与其连接的其他神经元接收电信号和化学信号,并且这些信号在细胞体中聚合。如果聚合值大于阈值,即神经元特定阈值,则激活神经元以经由轴突将其输出传输到相邻神经元。神经元之间的信息交换是并行执行的,并且通过学习增强了该信息交换功能。
“人工智能(AI)”是该领域的技术结构中的最高上级概念,以便通过模仿我们的人类大脑和神经网络使计算机或机器人像人类一样思考和行动。
虽然基于与人工智能相关的学习的控制系统的研究已经在汽车工业中不断进行,但到目前为止,它仅应用于结合说话者识别(语音识别)技术的技术,以及用于车辆的移动IT技术。
换句话说,该技术主要通过语音识别和通过智能手机互锁的应用程序操纵来进行导航或音频操纵。
通常,由于汽车由数万个部件制成,因此不容易识别故障症状并准确地确定大部件中的哪些部件出现故障。
因此,如果使用基于深度学习的人工智能来执行汽车组件的故障诊断,则可更准确和快速地识别并修复故障组件。
前述内容仅旨在帮助理解本公开的背景,并且不旨在表示本公开落入本领域技术人员已知的相关技术的范围内。
发明内容
已经做出本公开以解决上述问题并且提供一种基于深度学习的使用人工智能的汽车动力传动系组件的故障诊断方法。
根据本公开的用于动力传动系组件的故障诊断方法可包括:由服务器基于指示动力传动系组件故障的振动大数据建立诊断模型;通过使用从振动大数据中提取的特征向量,由服务器对动力传动系组件中的故障组件进行分类和建模;由车辆的控制器通过输入命令或驾驶员的设置启动车辆的动力传动系组件的故障诊断;和通过将与车辆行驶期间测量的车辆的动力传动系振动相对应的特征向量与在诊断模型中建模的振动大数据进行比较,由车辆的控制器诊断车辆的动力传动系组件的故障。
诊断动力传动系组件的故障可基于对车辆的行驶期间测量的动力传动系振动的特征向量进行数据预处理之后通过深度学习计算的车辆的动力传动系组件的故障概率。
该方法还可包括:当确定车辆的动力传动系组件未发生故障时,通过应用燃烧控制学习值来执行NVH(噪声、振动和声振粗糙度)性能评估。
在另一种形式中,该方法可包括由发动机控制器通过根据NVH性能评估的结果改变燃烧控制变量来执行主动燃烧控制。
在其他形式中,该方法包括通过在执行主动燃烧控制之后接收驾驶员的评估结果来确定NVH性能是否得到改善。
当来自驾驶员的评估结果指示NVH性能未得到改善时,可向驾驶员通知除动力传动系组件之外的故障组件的概率。
在车辆行驶预定距离之后,可自动执行启动故障诊断。
在启动故障诊断之后,可通知驾驶员切换到行驶模式以诊断车辆的动力传动系组件的故障。
根据本发明的动力传动系组件的故障诊断方法,通过使用建立的深度学习模型根据动力传动系振动特性诊断故障,可准确、快速地识别可能故障的组件。
因此,可减少或最小化故障诊断,并且从而可执行准确和快速的故障排除。
另外,通过主动燃烧控制可改善NVH性能。
根据本文提供的描述,其他适用领域将变得显而易见。应该理解的是,描述和具体实例仅用于说明的目的,并不旨在限制本公开的范围。
附图说明
为了可很好地理解本公开,现在将描述其各种形式,通过实例的方式给出,参考附图,其中:
图1示意性地示出了用于实现动力传动系组件的故障诊断方法的系统配置;
图2是表示动力传动系组件的故障诊断方法的流程图。
图3A、图3B和图3C分别示出了由开发的深度学习模型提取的特征向量的实例;
图4A、图4B和图4C分别示出了动力传动系组件的故障诊断方法的配置中的模型建立步骤;
图5A、图5B图5C和图5D分别示出了动力传动系组件的故障诊断方法的配置中的故障诊断步骤;
图6示出了图5A至图5D的故障诊断结果的实例;
图7A和图7B示出了动力传动系组件的故障诊断方法的配置中的主动燃烧控制步骤;和
图8示出了图7A和图7B的主动燃烧控制结果的实例。
本文描述的附图仅用于说明目的,并不旨在以任何方式限制本公开的范围。
具体实施方式
以下描述本质上仅是示例性的,并不旨在限制本公开、应用或用途。应该理解的是,在整个附图中,相应的附图标记表示相同或相应的部件和特征。
可以以各种形式修改本公开的示例性形式,并且本公开的范围不应被解释为限于下面详述的示例性形式。提供本示例性形式以向本领域技术人员更充分地描述本公开。因此,可夸大图中元件的形状等,以强调更清楚的描述。应注意,每个附图中的相同组件由相同的附图标记表示。省略了可能不必要地模糊本公开的主旨的已知特征和配置的详细描述。
图1示意性地示出了用于实现本公开的一种形式的动力传动系组件的故障诊断方法的系统配置;并且图2顺序地示出了根据本公开内容的一个方面的动力传动系组件的故障诊断方法。
下文中,参考图1和图2,将详细描述用于动力传动系组件的故障诊断方法。
为了实现动力传动系组件的故障诊断方法,车辆可配备有振动传感器、室内麦克风、动力传动系组件的故障诊断AI,以及诸如ECU(发动机控制单元)的发动机控制器。
可通过振动传感器提取动力传动系的振动信号,并且可基于动力传动系组件的故障诊断AI确定纯动力传动系特征信号,以诊断动力传动系组件是否发生故障。
此外,当动力传动系未发生故障时,发动机控制器可执行主动燃烧控制以响应NVH,并且室内麦克风测量驾驶员的语音命令和室内NVH水平。此外,它可与说话者识别技术相关联以确定NVH是否得到改善。
这里,动力传动系组件的故障诊断AI被称为实施动力传动系故障诊断算法的人工智能控制器,或者可对应于基于深度学习的学习模型。用于故障诊断算法的控制器可以以硬件方式(例如,处理器)、软件方式或硬件和软件方式的组合(即,一系列命令)来体现,其处理至少一个功能或操作。本申请使用众所周知的控制器(例如,处理器或一系列命令)来处理动力系组件的振动大数据。
可通过收集在动力传动系开发阶段中NVH性能发展期间发生的振动大数据并收集与该领域产生的故障相关的振动大数据来开发基于深度学习的动力传动系故障诊断学习模型。
也就是说,可收集关于类型和振动信息大数据的失败原因的信息,以通过中央服务器基于深度学习创建学习模型,并且可为每种类型建立特征向量学习模型。
中央服务器可基于高性能GPU为每种类型的动力传动系故障生成特征向量学习模型,并且针对动力传动系类型、故障原因提取和详细故障组件的类型对模型进行分类和建模。
也就是说,根据本公开的用于动力传动系组件的故障诊断方法可包括诊断模型建立步骤、故障诊断启动步骤、故障诊断步骤和主动燃烧控制步骤,并且该方法将通过图2顺序地描述。
在实验室级,可应用深度学习模型,其通过由诊断模型建立步骤输入发动机驱动条件确定信号(诸如动力传动系振动信号和RPM)来建立S11。
此外,可执行故障诊断启动引导S12。
故障诊断启动可允许驾驶员直接输入可应用语音识别技术的启动命令。
另外,它可在基于行驶距离行驶一定行驶距离之后自动启动并且将故障诊断入口引导至驾驶员。
故障诊断启动引导可包括用于动力传动系故障诊断的行驶模式(RPM和加速条件等)切换引导。
当在引导之后启动故障诊断控制时,动力传动系组件的故障诊断AI可接收并存储由振动传感器测量的振动信号,并且将存储的振动信号预处理为数据S13。
数据预处理可执行时间/幅度/频率格式化算法(归一化)以应用故障诊断学习模型并通过应用零填充和白噪来执行数据处理。
可基于在数据预处理之后开发的深度学习模型来执行故障诊断S14。
也就是说,基于通过诊断模型建立步骤中收集的组件的异常振动大数据,取决于当前行驶期间动力传动系的预处理振动信号是否对应于动力传动系的非典型信号中的任何一个,可确定动力传动系是发生故障S16还是动力传动系没有发生故障S21。
当确定故障时,可输出诸如一级、二级和三级结果的概率,并且可提供与诸如服务紧急等级和周期的维修请求项的跟进动作相关的信息。
当动力传动系在步骤21中没有发生故障时,对于驾驶员识别的NVH(噪声、振动和声振粗糙度)问题,可确定NVH是否由于耐久性进度而恶化。
为此,可通过应用燃烧控制学习值来执行NVH评估S22。
根据步骤S22的评估结果,可通过改变与耐久性进度相关的燃烧控制来执行主动燃烧控制S23。
燃烧控制学习值还可取决于在开发阶段中学习的NVH模型,并且可通过应用这一点来完成改进工作以根据燃烧控制变量的最佳组合来达到NVH水平。
此外,在如此执行燃烧控制的改变之后,可反映驾驶员的评估结果S24。
也就是说,可确定通过诸如驾驶员语音识别的评估输入的满意度和不满意度,并且当满意时可保持对步骤S23提出的控制变量的改变,但是可引导存在可能的是,动力传动系以外的组件可能发生故障,从而在不满意时可进行仔细检查。
通过步骤更详细地描述,诊断模型建立步骤可通过与图3A至图3C相同的过程建模。结果,首先,可提取深度学习模型的特征向量,如图4A至图4C所示。
诊断模型建立可通过首先测量实时振动信号来按单位时间生成帧。
另外,可应用用于单位帧的N分区细节信息窗口算法。
然后,可执行每个详细时间数据的频率分析(电平提取),并且可执行用于动力系的频率特性(低频带~高频带)效率增加的Log Mel过滤器后处理。
此外,在通过时间进度处理个体数据特征提取之后,可将其集成到一个学习模型中。
可生成基于特征向量(参数)的学习模型,该学习模型使用在发生相同故障形状时收集的振动大数据,其可总是更新。
当在行驶期间输入未知NVH的异常信号时,如图5A至图5D所示的故障诊断步骤可提取特征向量,并且执行故障诊断AI分析以导出故障诊断结果并提供导出的信息。
还如图6所示,可提供故障项和概率。
另一方面,当通过步骤S21确定动力传动系未发生故障时,如图7A和图7B所示,可确定燃烧和优化组合的NVH问题区域以执行发动机控制变量的主动控制。
图8示出了由于这种主动燃烧控制而改善了NVH性能。
如上所述,本公开收集在NVH性能发展期间发生的动力传动系振动的大数据,并且对每种组件类型的故障的振动的特征向量进行建模,并且在动力传动系故障诊断中,深度学习是通过对所测量的动力传动系振动的特征向量进行建模以导出和引导组件故障概率的数据来执行,从而可更快速和准确地实现动力传动系组件的故障诊断。
尽管已经参考附图描述了本公开,但是应该理解,本公开不限于所公开的示例性形式,并且对于本领域技术人员来说显而易见的是,可在不脱离本公开的精神和范围的情况下进行各种改变和修改。
Claims (8)
1.一种动力传动系组件的故障诊断方法,包括:
由服务器基于指示动力传动系组件故障的振动大数据建立诊断模型;
通过使用从所述振动大数据中提取的特征向量,由所述服务器对所述动力传动系组件中的故障组件进行分类和建模;
由车辆的控制器通过输入命令或驾驶员的设置启动所述车辆的动力传动系组件的故障诊断;和
通过将与所述车辆行驶期间测量的所述车辆的动力传动系振动相对应的特征向量与在所述诊断模型中建模的振动大数据进行比较,由所述控制器诊断所述车辆的动力传动系组件的故障。
2.根据权利要求1所述的故障诊断方法,其中,诊断所述动力传动系组件的故障是基于对所述车辆的行驶期间测量的动力传动系振动的特征向量进行数据预处理之后通过深度学习计算的所述车辆的动力传动系组件的故障概率。
3.根据权利要求2所述的故障诊断方法,还包括:当确定所述车辆的动力传动系组件未发生故障时,通过应用燃烧控制学习值来执行噪声、振动和声振粗糙度性能评估。
4.根据权利要求3所述的故障诊断方法,还包括:由发动机控制器通过基于所述噪声、振动和声振粗糙度性能评估的结果改变燃烧控制变量来执行主动燃烧控制。
5.根据权利要求4所述的故障诊断方法,还包括:通过在执行所述主动燃烧控制之后从驾驶员接收评估结果来确定所述噪声、振动和声振粗糙度性能是否得到改善。
6.根据权利要求5所述的故障诊断方法,其中,当来自所述驾驶员的评估结果表明所述噪声、振动和声振粗糙度性能未得到改善时,向所述驾驶员通知除所述动力传动系组件之外的组件故障的概率。
7.根据权利要求1所述的故障诊断方法,其中,在所述车辆行驶预定距离之后自动执行启动所述故障诊断。
8.根据权利要求7所述的故障诊断方法,其中,在启动所述故障诊断之后,通知所述车辆的驾驶员切换到行驶模式以诊断所述车辆的动力传动系组件的故障。
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- 2019-06-28 EP EP19183188.2A patent/EP3637083A1/en not_active Ceased
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EP3637083A1 (en) | 2020-04-15 |
US20200118358A1 (en) | 2020-04-16 |
KR20200041098A (ko) | 2020-04-21 |
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