会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 33. 发明申请
    • Bayesian Sensor Estimation For Machine Condition Monitoring
    • 贝叶斯传感器估计机器状态监测
    • US20080086283A1
    • 2008-04-10
    • US11866535
    • 2007-10-03
    • Chao YuanClaus Neubauer
    • Chao YuanClaus Neubauer
    • G06F17/18
    • G05B23/024
    • A method for monitoring a system includes receiving a set of training data. A Gaussian mixture model is defined to model a probability distribution for a particular sensor of the system from among a plurality of sensors of the system based on the received training data. The Gaussian mixture model includes a sum of k mixture components, where k is a positive integer. Sensor data is received from the plurality of sensors of the system. An expectation-maximization technique is performed to estimate an expected value for the particular sensor based on the defined Gaussian mixture model and the received sensor data from the plurality of sensors.
    • 一种用于监视系统的方法包括接收一组训练数据。 高斯混合模型被定义为基于接收到的训练数据从系统的多个传感器中的系统的特定传感器的概率分布建模。 高斯混合模型包括k个混合分量的和,其中k是正整数。 从系统的多个传感器接收传感器数据。 执行期望最大化技术以基于所定义的高斯混合模型和来自多个传感器的接收的传感器数据来估计特定传感器的期望值。
    • 37. 发明授权
    • Bayesian sensor estimation for machine condition monitoring
    • 贝叶斯传感器估计机器状态监测
    • US07565262B2
    • 2009-07-21
    • US11866535
    • 2007-10-03
    • Chao YuanClaus Neubauer
    • Chao YuanClaus Neubauer
    • G06F17/18
    • G05B23/024
    • A method for monitoring a system includes receiving a set of training data. A Gaussian mixture model is defined to model a probability distribution for a particular sensor of the system from among a plurality of sensors of the system based on the received training data. The Gaussian mixture model includes a sum of k mixture components, where k is a positive integer. Sensor data is received from the plurality of sensors of the system. An expectation-maximization technique is performed to estimate an expected value for the particular sensor based on the defined Gaussian mixture model and the received sensor data from the plurality of sensors.
    • 一种用于监视系统的方法包括接收一组训练数据。 高斯混合模型被定义为基于接收到的训练数据从系统的多个传感器中的系统的特定传感器的概率分布建模。 高斯混合模型包括k个混合分量的和,其中k是正整数。 从系统的多个传感器接收传感器数据。 执行期望最大化技术以基于所定义的高斯混合模型和来自多个传感器的接收的传感器数据来估计特定传感器的期望值。
    • 38. 发明申请
    • Machine condition monitoring using a flexible monitoring framework
    • 机器状态监测采用灵活的监控框架
    • US20090037155A1
    • 2009-02-05
    • US12077541
    • 2008-03-20
    • Bernhard GlomannChao YuanClaus Neubauer
    • Bernhard GlomannChao YuanClaus Neubauer
    • G06N3/02G06F17/10
    • G05B23/0221
    • A flexible framework and a corresponding user interface allow a user to configure a machine condition monitoring system. A user-configurable computation framework offers flexibility in designing the machine condition monitoring system. In this framework, every computation based on machine attributes is represented as an input-output system. A simple computation can be easily defined by specifying the computation type, number of inputs, structure, and parameters. The user can use the determined output attributes of computations as input attributes in other computations. Ultimately, the computations are aggregated by the framework configured by the user to produce an output computation attribute that indicates a machine condition or predicts a machine condition.
    • 灵活的框架和相应的用户界面允许用户配置机器状况监控系统。 用户可配置的计算框架提供了设计机器状态监控系统的灵活性。 在此框架中,基于机器属性的每个计算都表示为输入 - 输出系统。 可以通过指定计算类型,输入数量,结构和参数来轻松定义简单的计算。 用户可以使用确定的计算输出属性作为其他计算中的输入属性。 最终,计算通过由用户配置的框架来聚合,以产生指示机器状况或预测机器状况的输出计算属性。
    • 40. 发明申请
    • Machine condition monitoring using pattern rules
    • 使用模式规则进行机器状态监控
    • US20080255773A1
    • 2008-10-16
    • US12077279
    • 2008-03-18
    • Chao YuanClaus Neubauer
    • Chao YuanClaus Neubauer
    • G06F19/00
    • G05B23/0229
    • Pattern rules are created by comparing a condition signal pattern to a plurality of known signal patterns and determining a machine condition pattern rule based at least in part on the comparison of the condition signal pattern to one of the plurality of known signal patterns. A matching score based on the comparison of the condition signal pattern to one of the plurality of known signal patterns as well as a signal pattern duration is determined. The machine condition pattern rule is then defined for nonparametric condition signal patterns as a multipartite threshold rule with a first threshold based on the determined matching score and a second threshold based on the determined signal duration. For parametric signal patterns, one or more parameters of the signal pattern are determined and the machine condition pattern rule is further defined with a third threshold based on the determined one or more parameters.
    • 通过将条件信号模式与多个已知信号模式进行比较并至少部分地基于条件信号模式与多种已知信号模式之一的比较来确定机器状态模式规则来创建模式规则。 确定基于条件信号模式与多个已知信号模式中的一个的比较以及信号模式持续时间的匹配分数。 然后,根据所确定的匹配分数和基于确定的信号持续时间的第二阈值,将非参数条件信号模式定义为具有第一阈值的多部分阈值规则的机器状态模式规则。 对于参数信号模式,确定信号模式的一个或多个参数,并且基于所确定的一个或多个参数,用第三阈值进一步定义机器状态模式规则。