公开(公告)号：US07949497B2

公开(公告)日：2011-05-24

申请号：US12077255

申请日：2008-03-18

申请人： Chao Yuan ,  Claus Neubauer

发明人： Chao Yuan ,  Claus Neubauer

IPC分类号： G06F11/30

CPC分类号： G05B23/0232

摘要： Condition signals of machines are observed and one or more discontinuities are detected in the condition signals. The discontinuities in the condition signals are compensated for (e.g., by applying a shifting factor to models of the signals) and trends of the compensated condition signals are determined. The trends are used to predict future fault conditions in machines. Kalman filters comprising observation models and evolution models are used to determine the trends. Discontinuity in observed signals is detected using hypothesis testing.

摘要翻译： 观察机器的状态信号，并在条件信号中检测到一个或多个不连续性。 条件信号中的不连续性被补偿（例如，通过将移位因子应用于信号的模型），并且确定补偿状态信号的趋势。 趋势用于预测机器中未来的故障状况。 包含观察模型和进化模型的卡尔曼滤波被用于确定趋势。 使用假设检验检测观察信号的不连续性。

公开(公告)号：US07567878B2

公开(公告)日：2009-07-28

申请号：US11563241

申请日：2006-11-27

申请人： Chao Yuan ,  Claus Neubauer

发明人： Chao Yuan ,  Claus Neubauer

IPC分类号： G01N37/00

CPC分类号： G05B19/4065 ,  G05B23/024 ,  G05B23/0281 ,  G05B2219/31357 ,  G05B2219/34477 ,  G06K9/6284 ,  Y02P90/14

摘要： A method for monitoring machine conditions provides additional information using a one-class classifier in which an evaluation function is learned. In the method, a distance is determined from an anomaly measurement x to a boundary of a region R1 containing all acceptable measurements. The distance is used as a measure of the extent of the anomaly. The distance is found by searching along a line from the anomaly to a closest acceptable measurement within the region R1.

摘要翻译： 用于监视机器状况的方法使用其中学习评估功能的一类分类器提供附加信息。 在该方法中，从异常测量x到包含所有可接受测量的区域R1的边界确定距离。 该距离用于测量异常的程度。 通过沿着从异常线到一个最接近的区域R1内可接受的测量的线来搜索距离。

公开(公告)号：US20080086283A1

公开(公告)日：2008-04-10

申请号：US11866535

申请日：2007-10-03

申请人： Chao Yuan ,  Claus Neubauer

发明人： Chao Yuan ,  Claus Neubauer

IPC分类号： G06F17/18

CPC分类号： G05B23/024

摘要： 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.

摘要翻译： 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。

公开(公告)号：US08112381B2

公开(公告)日：2012-02-07

申请号：US12251714

申请日：2008-10-15

申请人： Chao Yuan ,  Claus Neubauer ,  Chellury R. Sastry ,  Stefan Galler

发明人： Chao Yuan ,  Claus Neubauer ,  Chellury R. Sastry ,  Stefan Galler

IPC分类号： G06F9/44 ,  G06N7/02 ,  G06N7/06

CPC分类号： G06N7/005

摘要： Machine condition monitoring on a system utilizes a wireless sensor network to gather data from a large number of sensors. The data is processed using a multivariate statistical model to determine whether the system has deviated from a normal condition. The wireless sensor network permits the acquisition of a large number of distributed data points from plural system modalities, which, in turn, yields enhanced prediction accuracy and a reduction in false alarms.

摘要翻译： 系统上的机器状况监测利用无线传感器网络从大量传感器收集数据。 使用多变量统计模型处理数据，以确定系统是否偏离正常状态。 无线传感器网络允许从多个系统模态获取大量分布式数据点，这反过来又产生增强的预测精度和减少误报。

公开(公告)号：US07930122B2

公开(公告)日：2011-04-19

申请号：US12408882

申请日：2009-03-23

申请人： Chao Yuan ,  Claus Neubauer

发明人： Chao Yuan ,  Claus Neubauer

IPC分类号： G01N37/00

CPC分类号： G05B19/4065 ,  G05B23/024 ,  G05B23/0281 ,  G05B2219/31357 ,  G05B2219/34477 ,  G06K9/6284 ,  Y02P90/14

摘要： A method for monitoring machine conditions provides additional information using a one-class classifier in which an evaluation function is learned. In the method, a distance is determined from an anomaly measurement x to a boundary of a region R1 containing all acceptable measurements. The distance is used as a measure of the extent of the anomaly. The distance is found by searching along a line from the anomaly to a closest acceptable measurement within the region R1.

摘要翻译： 用于监视机器状况的方法使用其中学习评估功能的一类分类器提供附加信息。 在该方法中，从异常测量x到包含所有可接受测量的区域R1的边界确定距离。 该距离用于测量异常的程度。 通过沿着从异常线到一个最接近的区域R1内可接受的测量的线来搜索距离。

公开(公告)号：US07716152B2

公开(公告)日：2010-05-11

申请号：US12048381

申请日：2008-03-14

申请人： Christian Balderer ,  Claus Neubauer ,  Chao Yuan

发明人： Christian Balderer ,  Claus Neubauer ,  Chao Yuan

IPC分类号： G06N5/00

CPC分类号： G05B17/02 ,  G05B23/0248 ,  G06K9/622 ,  G06K9/6256

摘要： A method is provided for selecting a representative set of training data for training a statistical model in a machine condition monitoring system. The method reduces the time required to choose representative samples from a large data set by using a nearest-neighbor sequential clustering technique in combination with a kd-tree. A distance threshold is used to limit the geometric size the clusters. Each node of the kd-tree is assigned a representative sample from the training data, and similar samples are subsequently discarded.

摘要翻译： 提供了一种用于在机器状态监视系统中选择用于训练统计模型的代表性训练数据集合的方法。 该方法通过使用最近邻序列聚类技术与kd-tree结合来减少从大数据集中选择代表性样本所需的时间。 距离阈值用于限制集群的几何尺寸。 从训练数据中分配kd-tree的每个节点代表性样本，随后丢弃类似的样本。

公开(公告)号：US07565262B2

公开(公告)日：2009-07-21

申请号：US11866535

申请日：2007-10-03

申请人： Chao Yuan ,  Claus Neubauer

发明人： Chao Yuan ,  Claus Neubauer

IPC分类号： G06F17/18

CPC分类号： G05B23/024

摘要： 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.

摘要翻译： 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。

公开(公告)号：US20090037155A1

公开(公告)日：2009-02-05

申请号：US12077541

申请日：2008-03-20

申请人： Bernhard Glomann ,  Chao Yuan ,  Claus Neubauer

发明人： Bernhard Glomann ,  Chao Yuan ,  Claus Neubauer

IPC分类号： G06N3/02 ,  G06F17/10

CPC分类号： G05B23/0221

摘要： 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.

摘要翻译： 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。

公开(公告)号：US20080288213A1

公开(公告)日：2008-11-20

申请号：US12077255

申请日：2008-03-18

申请人： Chao Yuan ,  Claus Neubauer

发明人： Chao Yuan ,  Claus Neubauer

IPC分类号： G21C17/00

CPC分类号： G05B23/0232

摘要： Condition signals of machines are observed and one or more discontinuities are detected in the condition signals. The discontinuities in the condition signals are compensated for (e.g., by applying a shifting factor to models of the signals) and trends of the compensated condition signals are determined. The trends are used to predict future fault conditions in machines. Kalman filters comprising observation models and evolution models are used to determine the trends. Discontinuity in observed signals is detected using hypothesis testing.

摘要翻译： 观察机器的状态信号，并在条件信号中检测到一个或多个不连续性。 条件信号中的不连续性被补偿（例如，通过将移位因子应用于信号的模型），并且确定补偿状态信号的趋势。 趋势用于预测机器中未来的故障状况。 包含观察模型和进化模型的卡尔曼滤波被用于确定趋势。 使用假设检验检测观察信号的不连续性。

公开(公告)号：US20080255773A1

公开(公告)日：2008-10-16

申请号：US12077279

申请日：2008-03-18

申请人： Chao Yuan ,  Claus Neubauer

发明人： Chao Yuan ,  Claus Neubauer

IPC分类号： G06F19/00

CPC分类号： G05B23/0229

摘要： 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.

摘要翻译： 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。