公开(公告)号：US10146611B2

公开(公告)日：2018-12-04

申请号：US14440906

申请日：2013-11-13

申请人： Linxia Liao ,  Kun Ji

发明人： Linxia Liao ,  Kun Ji

IPC分类号： G06F11/07 ,  G05B19/418

摘要： A method for controlling a system including a plurality of subsystems, includes receiving operational data from the plurality of subsystems of the system (S21). A future condition of each of the plurality of subsystems is estimated from the received operational data (S22). A control strategy for delaying a need for system maintenance is generated based on the estimated future condition of each of the plurality of subsystems (S23). An operation of the system is controlled based on the generated control strategy (S24).

公开(公告)号：US20150301882A1

公开(公告)日：2015-10-22

申请号：US14440906

申请日：2013-11-13

申请人： Linxia LIAO ,  Kun JI

发明人： Linxia Liao ,  Kun Ji

IPC分类号： G06F11/07

CPC分类号： G06F11/0793 ,  G05B19/4184 ,  G06F11/0709 ,  G06F11/0751 ,  G06F11/079 ,  Y02P80/114 ,  Y02P90/14 ,  Y02P90/86

摘要： A method for controlling a system including a plurality of subsystems, includes receiving operational data from the plurality of subsystems of the system (S21). A future condition of each of the plurality of subsystems is estimated from the received operational data (S22). A control strategy for delaying a need for system maintenance is generated based on the estimated future condition of each of the plurality of subsystems (S23). An operation of the system is controlled based on the generated control strategy (S24).

摘要翻译： 一种用于控制包括多个子系统的系统的方法，包括从系统的多个子系统接收操作数据（S21）。 根据接收到的操作数据来估计多个子系统中的每一个的未来状况（S22）。 基于多个子系统中的每一个的估计未来状况，生成用于延迟系统维护需求的控制策略（S23）。 基于生成的控制策略来控制系统的操作（S24）。

公开(公告)号：US20130197854A1

公开(公告)日：2013-08-01

申请号：US13744792

申请日：2013-01-18

申请人： Linxia Liao

发明人： Linxia Liao

IPC分类号： G06F17/18

CPC分类号： G06F17/18 ,  G05B23/0224 ,  G05B23/0243 ,  G05B23/0283 ,  G07C3/08

摘要： A machine tool system is diagnosed by identifying a fault class to which an input measurement vector belongs. The fault class corresponds to a group of weight vectors in a code book of a self organized map that describes the machine tool system based on training data. Probabilities that the input measurement vector belongs to a given class are estimated based on the posterior probability of the weight vectors of the code book corresponding to the given class given the input measurement vector. Training data to create the code book may be collected under a first operating condition while the input measurement vector is collected under a second operating condition.

摘要翻译： 通过识别输入测量向量所属的故障等级来诊断机床系统。 故障类对应于基于训练数据描述机床系统的自组织映射的代码簿中的一组权重向量。 基于给定输入测量向量的给定类对应的代码簿的权重向量的后验概率来估计输入测量向量属于给定类的概率。 可以在第一操作条件下收集用于创建代码簿的训练数据，同时在第二操作条件下收集输入测量向量。

公开(公告)号：US20130060524A1

公开(公告)日：2013-03-07

申请号：US13301157

申请日：2011-11-21

申请人： Linxia Liao

发明人： Linxia Liao

IPC分类号： G06F15/00

CPC分类号： G05B23/0254

摘要： A method for detecting an anomaly in a machine under test includes monitoring operational data from a control unit of the machine under test. An operational state of the machine under test is identified based on the monitored operational data. Sensor data is monitored from one or more sensors installed within or near to the machine under test. A model corresponding to the identified operational state of the machine under test is consulted to identify one or more key parameters and corresponding normal operating ranges for each determined key parameter. It is determined when a key parameter of the one or more key parameters is not within its corresponding normal operating range based on the monitored sensor data.

摘要翻译： 用于检测被测试机器中的异常的方法包括从被测试机器的控制单元监视操作数据。 基于受监视的操作数据识别被测机器的操作状态。 传感器数据由安装在被测机器内或附近的一个或多个传感器进行监控。 参考与被测试的机器的识别的操作状态对应的模型，以识别每个确定的关键参数的一个或多个关键参数和对应的正常操作范围。 基于所监视的传感器数据确定一个或多个关键参数的关键参数何时不在其对应的正常操作范围内。

公开(公告)号：US20140039806A1

公开(公告)日：2014-02-06

申请号：US13950372

申请日：2013-07-25

申请人： Linxia Liao ,  Zachery Edmondson

发明人： Linxia Liao ,  Zachery Edmondson

IPC分类号： G01M99/00

CPC分类号： G01M99/00 ,  G01M13/00 ,  G05B23/0283 ,  G06N3/126

摘要： A method for estimating a remaining useful life of a system includes monitoring sensor data from sensors deployed within a system. A plurality of features are extracted from the sensor data. Tree graphs are generated including mathematical operators and features as nodes and a advanced feature is produced from each of the tree graphs by transforming the tree graphs into equations. A recursive operation including analyzing a fitness of each of the advanced features, performing crossover/mutation on the tree graphs, producing advanced features from the altered tree graphs, and analyzing the fitness of the altered tree graphs to produce at least one final advanced feature is performed. A remaining useful life of the system is calculated based on the final advanced feature.

摘要翻译： 一种用于估计系统的剩余使用寿命的方法包括从部署在系统内的传感器监测传感器数据。 从传感器数据中提取多个特征。 生成树形图，包括数学运算符和特征作为节点，并通过将树图转换为方程从每个树形图生成高级特征。 一种递归操作，包括分析每个高级特征的适应度，在树形图上执行交叉/突变，从改变的树图生成高级特征，以及分析改变的树图以产生至少一个最终高级特征的适应度。 执行。 基于最终的高级功能计算系统的剩余使用寿命。

公开(公告)号：US08301406B2

公开(公告)日：2012-10-30

申请号：US12508836

申请日：2009-07-24

申请人： Jay Lee ,  Linxia Liao

发明人： Jay Lee ,  Linxia Liao

IPC分类号： G01R23/16

CPC分类号： G06K9/6251 ,  G05B23/0254 ,  G06K9/00496 ,  G06K9/6226 ,  G06K9/6256

摘要： A method of prognosing a mechanical system to predict when a failure may occur is disclosed. Measurement data corresponding to the mechanical system is used to extract one or more features by decomposing the measurement data into a feature space. A prediction model is then selected from a plurality of prediction models for the one or more features based at least on part on a degradation status of the mechanical system and a reinforcement learning model. A predicted feature space is generated by applying the selective prediction model to the feature space as well as a confidence value by comparing the predicted feature space with a normal baseline distribution, a faulty baseline distribution, or a combination thereof. A status of mechanical system based at least in part on the confidence value is then provided.

摘要翻译： 公开了一种预测机械系统来预测发生故障的方法。 与机械系统对应的测量数据用于通过将测量数据分解为特征空间来提取一个或多个特征。 然后至少部分地基于机械系统的退化状态和强化学习模型，从用于所述一个或多个特征的多个预测模型中选择预测模型。 通过将预测特征空间与正常基线分布，有缺陷的基线分布或其组合进行比较，将选择性预测模型应用于特征空间以及置信度值来生成预测特征空间。 然后提供至少部分基于置信度值的机械系统的状态。

公开(公告)号：US20100023307A1

公开(公告)日：2010-01-28

申请号：US12508836

申请日：2009-07-24

申请人： Jay Lee ,  Linxia Liao

发明人： Jay Lee ,  Linxia Liao

IPC分类号： G06G7/48

CPC分类号： G06K9/6251 ,  G05B23/0254 ,  G06K9/00496 ,  G06K9/6226 ,  G06K9/6256

摘要： A method of prognosing a mechanical system to predict when a failure may occur is disclosed. Measurement data corresponding to the mechanical system is used to extract one or more features by decomposing the measurement data into a feature space. A prediction model is then selected from a plurality of prediction models for the one or more features based at least on part on a degradation status of the mechanical system and a reinforcement learning model. A predicted feature space is generated by applying the selective prediction model to the feature space as well as a confidence value by comparing the predicted feature space with a normal baseline distribution, a faulty baseline distribution, or a combination thereof. A status of mechanical system based at least in part on the confidence value is then provided.

摘要翻译： 公开了一种预测机械系统来预测发生故障的方法。 与机械系统对应的测量数据用于通过将测量数据分解为特征空间来提取一个或多个特征。 然后至少部分地基于机械系统的退化状态和强化学习模型，从用于所述一个或多个特征的多个预测模型中选择预测模型。 通过将预测特征空间与正常基线分布，有缺陷的基线分布或其组合进行比较，将选择性预测模型应用于特征空间以及置信度值来生成预测特征空间。 然后提供至少部分基于置信度值的机械系统的状态。