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1. 发明授权

US08005771B2 Segment-based change detection method in multivariate data stream 有权
标题翻译：多变量数据流中基于段的变化检测方法
公开(公告)号：US08005771B2
公开(公告)日：2011-08-23
申请号：US12236587
申请日：2008-09-24
申请人： Terrence Chen , Chao Yuan , Abdul Saboor Sheikh , Claus Neubauer
发明人： Terrence Chen , Chao Yuan , Abdul Saboor Sheikh , Claus Neubauer
IPC分类号： G06F15/18
CPC分类号： G06K9/00536 , G06K9/6284
摘要： A method and framework are described for detecting changes in a multivariate data stream. A training set is formed by sampling time windows in a data stream containing data reflecting normal conditions. A histogram is created to summarize each window of data, and data within the histograms are clustered to form test distribution representatives to minimize the bulk of training data. Test data is then summarized using histograms representing time windows of data and data within the test histograms are clustered. The test histograms are compared to the training histograms using nearest neighbor techniques on the clustered data. Distances from the test histograms to the test distribution representatives are compared to a threshold to identify anomalies.
摘要翻译：描述了用于检测多变量数据流中的变化的方法和框架。通过在包含反映正常条件的数据的数据流中采样时间窗口来形成训练集。创建直方图以总结每个数据窗口，并且将直方图中的数据进行聚类以形成测试分发代表以最小化训练数据的大部分。然后使用表示数据的时间窗口的直方图来汇总测试数据，并且将测试直方图中的数据聚类。将测试直方图与使用最近邻技术的聚类数据的训练直方图进行比较。将测试直方图与测试分布代表的距离与阈值进行比较以识别异常。

2. 发明申请

US20090091443A1 Segment-Based Change Detection Method in Multivariate Data Stream 有权
标题翻译：多变量数据流中基于段的变化检测方法
公开(公告)号：US20090091443A1
公开(公告)日：2009-04-09
申请号：US12236587
申请日：2008-09-24
申请人： Terrence Chen , Chao Yuan , Abdul Saboor Sheikh , Claus Neubauer
发明人： Terrence Chen , Chao Yuan , Abdul Saboor Sheikh , Claus Neubauer
IPC分类号： G08B21/00
CPC分类号： G06K9/00536 , G06K9/6284
摘要： A method and framework are described for detecting changes in a multivariate data stream. A training set is formed by sampling time windows in a data stream containing data reflecting normal conditions. A histogram is created to summarize each window of data, and data within the histograms are clustered to form test distribution representatives to minimize the bulk of training data. Test data is then summarized using histograms representing time windows of data and data within the test histograms are clustered. The test histograms are compared to the training histograms using nearest neighbor techniques on the clustered data. Distances from the test histograms to the test distribution representatives are compared to a threshold to identify anomalies.
摘要翻译：描述了用于检测多变量数据流中的变化的方法和框架。通过在包含反映正常条件的数据的数据流中采样时间窗口来形成训练集。创建直方图以总结每个数据窗口，并且将直方图中的数据进行聚类以形成测试分发代表以最小化训练数据的大部分。然后使用表示数据的时间窗口的直方图来汇总测试数据，并且将测试直方图中的数据聚类。将测试直方图与使用最近邻技术的聚类数据的训练直方图进行比较。将测试直方图与测试分布代表的距离与阈值进行比较以识别异常。

3. 发明授权

US07953577B2 Method and apparatus for improved fault detection in power generation equipment 失效
标题翻译：发电设备故障检测方法及装置
公开(公告)号：US07953577B2
公开(公告)日：2011-05-31
申请号：US11202861
申请日：2005-08-12
申请人： Chao Yuan , Claus Neubauer , Zehra Cataltepe
发明人： Chao Yuan , Claus Neubauer , Zehra Cataltepe
IPC分类号： G06F11/30 , G21C17/00
CPC分类号： G05B23/0254 , G05B23/0297
摘要： A method and apparatus for detecting faults in power plant equipment is discloses using sensor confidence and an improved method of identifying the normal operating range of the power generation equipment as measured by those sensors. A confidence is assigned to a sensor in proportion to the residue associated with that sensor. If the sensor has high residue, a small confidence is assigned to the sensor. If a sensor has a low residue, a high confidence is assigned to that sensor, and appropriate weighting of that sensor with other sensors is provided. A feature space trajectory (FST) method is used to model the normal operating range curve distribution of power generation equipment characteristics. Such an FST method is illustratively used in conjunction with a minimum spanning tree (MST) method to identify a plurality of nodes and to then connect those with line segments that approximate a curve.
摘要翻译：一种用于检测发电厂设备故障的方法和装置公开了使用传感器置信度和一种通过这些传感器测量的识别发电设备的正常工作范围的改进方法。与传感器相关的残差成比例地分配给传感器的置信度。如果传感器具有高残留量，那么传感器的信心就很小。如果传感器具有较低的残留量，则会将高置信度分配给该传感器，并提供该传感器与其他传感器的适当加权。使用特征空间轨迹（FST）方法对发电设备特性的正常工作范围曲线分布进行建模。这种FST方法被说明性地与最小生成树（MST）方法一起使用以识别多个节点，然后将它们与近似于曲线的线段连接。

4. 发明授权

US07844558B2 Incremental learning of nonlinear regression networks for machine condition monitoring 有权
标题翻译：用于机器状态监测的非线性回归网络的增量学习
公开(公告)号：US07844558B2
公开(公告)日：2010-11-30
申请号：US11866554
申请日：2007-10-03
申请人： Chao Yuan , Claus Neubauer
发明人： Chao Yuan , Claus Neubauer
IPC分类号： G06F17/00 , G06N5/00
CPC分类号： G06F11/0751 , G05B23/024 , G06F11/0706
摘要： A method for identifying a potential fault in a system includes obtaining a set of training data. A first kernel is selected from a library of two or more kernels and the first kernel is added to a regression network. A next kernel is selected from the library of two or more kernels and the next kernel is added to the regression network. The regression network is refined. A potential fault is identified in the system using the refined regression network.
摘要翻译：用于识别系统中的潜在故障的方法包括获得一组训练数据。从两个或多个内核的库中选择第一个内核，并将第一个内核添加到回归网络。从两个或多个内核的库中选择下一个内核，并将下一个内核添加到回归网络。回归网络细化。使用精确回归网络在系统中识别出潜在的故障。

5. 发明授权

US07552035B2 Method to use a receiver operator characteristics curve for model comparison in machine condition monitoring 有权
标题翻译：在机器状态监测中使用接收机操作员特征曲线进行模型比较的方法
公开(公告)号：US07552035B2
公开(公告)日：2009-06-23
申请号：US10977220
申请日：2004-10-28
申请人： Zehra Cataltepe , Claus Neubauer , Chao Yuan
发明人： Zehra Cataltepe , Claus Neubauer , Chao Yuan
IPC分类号： G06F17/10
CPC分类号： G05B23/0243
摘要： A method to use a receiver operator characteristics curve for model comparison in machine condition monitoring. The method and systems of using this method may be used to evaluate different monitoring models. These models may be used to monitor a variety of different systems such as power plant systems or magnetic resonance imaging systems. The methods use training data and designate one or more points in the data as a false negative, thereby permitting a receiver operator characteristics analysis to be performed. Multiple receiver operator characteristics analyses may be performed either on different models or on different points within a single model, thereby permitting the receiver operator characteristics analyses to be used to select a beneficial model for monitoring a particular system.
摘要翻译：一种在机器状态监测中使用接收机操作员特征曲线进行模型比较的方法。使用该方法的方法和系统可用于评估不同的监测模型。这些模型可用于监测各种不同的系统，例如发电厂系统或磁共振成像系统。该方法使用训练数据并将数据中的一个或多个点指定为假阴性，从而允许执行接收者操作员特征分析。可以在单个模型中的不同模型或不同点执行多个接收者操作者特征分析，从而允许接收者操作员特征分析用于选择用于监视特定系统的有益模型。

6. 发明授权

US07035763B2 Systems and methods for selecting training data and generating fault models for use in use sensor-based monitoring 失效
标题翻译：选择训练数据和生成故障模型以用于使用基于传感器的监测的系统和方法
公开(公告)号：US07035763B2
公开(公告)日：2006-04-25
申请号：US10932577
申请日：2004-09-02
申请人： Chao Yuan , Claus Neubauer , Hans-Gerd Brummel , Ming Fang , Zehra Cataltepe
发明人： Chao Yuan , Claus Neubauer , Hans-Gerd Brummel , Ming Fang , Zehra Cataltepe
IPC分类号： G06F15/00 , G06F19/00
CPC分类号： G05B9/02 , G05B23/0254
摘要： A system for generating a sensor model for use in sensor-based monitoring is provided. The system includes a segmenting module for segmenting a collection of sensor vectors into a plurality of bins comprising distinct sensor vectors. The system also includes a set-generating module for generating a set of statistically significant sensor vectors for each bin. The system further includes a consistency determination module for generating at least one consistent set of sensor vectors from the sets of statistically significant sensor vectors. Additionally, the system includes a model-generating module for generating a sensor model based upon the at least one consistent set.
摘要翻译：提供了一种用于生成用于基于传感器的监测的传感器模型的系统。该系统包括分段模块，用于将传感器向量的集合分成多个包含不同传感器向量的箱。该系统还包括用于为每个仓生成一组统计上显着的传感器向量的集合生成模块。所述系统还包括一致性确定模块，用于从所述统计学上有意义的传感器向量集合生成至少一个一致的传感器向量集合。另外，该系统包括用于基于至少一个一致集合生成传感器模型的模型生成模块。

7. 发明申请

US20050062599A1 Tool for sensor management and fault visualization in machine condition monitoring 有权
标题翻译：机器状态监测中的传感器管理和故障可视化工具
公开(公告)号：US20050062599A1
公开(公告)日：2005-03-24
申请号：US10932576
申请日：2004-09-02
申请人： Claus Neubauer , Zehra Cataltepe , Chao Yuan , Jie Cheng , Ming Fang , Wesley McCorkle
发明人： Claus Neubauer , Zehra Cataltepe , Chao Yuan , Jie Cheng , Ming Fang , Wesley McCorkle
IPC分类号： G05B23/02 , G08B29/00
CPC分类号： G05B23/0272 , Y04S10/522
摘要： A tool for sensor management and fault visualization in machine condition monitoring. The method and system are able to monitor a plurality of sensors at one time. The sensors may be used in a power plant system monitoring system. The method and system may display a fault status for each sensor in the plurality of sensors in a single display, wherein the fault status for each sensor is displayed over time. The method and system also provide a mechanism that permits a user to examine details of each sensor in the plurality of sensors at any given time. In addition, the method and system are capable of categorizing each fault in the fault status using one or more properties or categorizing criteria. The method and system also permit sensors to be tested such that different operating models may be examined by utilizing different sensors.
摘要翻译：机器状态监测中的传感器管理和故障可视化工具。该方法和系统能够一次监视多个传感器。传感器可用于发电厂系统监控系统。该方法和系统可以在单个显示器中的多个传感器中的每个传感器显示故障状态，其中每个传感器的故障状态随时间显示。该方法和系统还提供了允许用户在任何给定时间检查多个传感器中的每个传感器的细节的机构。此外，该方法和系统能够使用一个或多个属性或分类标准对故障状态中的每个故障进行分类。该方法和系统还允许测试传感器，使得可以通过利用不同的传感器检查不同的操作模型。

8. 发明申请

US20050055609A1 Joint approach of out-of-range detection and fault detection for power plant monitoring 有权
标题翻译：电站监控超范围检测和故障检测的联合方法
公开(公告)号：US20050055609A1
公开(公告)日：2005-03-10
申请号：US10932573
申请日：2004-09-02
申请人： Chao Yuan , Zehra Cataltepe , Claus Neubauer , Ming Fang
发明人： Chao Yuan , Zehra Cataltepe , Claus Neubauer , Ming Fang
IPC分类号： G05B23/02 , G06F11/00
CPC分类号： G05B23/0254 , G05B23/0235
摘要： A joint approach of out-of-range detection and fault detection for power plant monitoring. The method initially determines whether a sensor is an independent sensor or a dependent sensor. If the sensor is an independent sensor, then an operating range is established for each independent sensor. A reading from each independent sensor is then compared with the operating range that has been established. If the reading is out-of-range, an alarm may be activated. If the reading is not out-of-range, then this reading is used to determine an expected operating range for each dependent sensor. A reading from each dependent sensor is then compared with the predicted operating range. Again, if the reading from the dependent sensor is out of the expected range, an alarm may be sounded.
摘要翻译：电站监控的超范围检测和故障检测的联合方法。该方法最初确定传感器是独立传感器还是依赖传感器。如果传感器是独立的传感器，则为每个独立的传感器建立一个工作范围。然后将每个独立传感器的读数与已建立的操作范围进行比较。如果读数超出范围，则可能会激活报警。如果读数不超出范围，则该读数用于确定每个相关传感器的预期工作范围。然后将每个从属传感器的读数与预测的工作范围进行比较。再次，如果来自从属传感器的读数超出预期范围，则可能会发出警报。

9. 发明申请

US20120304008A1 SUPERVISED FAULT LEARNING USING RULE-GENERATED SAMPLES FOR MACHINE CONDITION MONITORING 有权
标题翻译：使用规则生成样本进行机器状态监测的监督故障学习
公开(公告)号：US20120304008A1
公开(公告)日：2012-11-29
申请号：US13394919
申请日：2010-09-13
申请人： Holger Hackstein , Claus Neubauer , Chao Yuan
发明人： Holger Hackstein , Claus Neubauer , Chao Yuan
IPC分类号： G06F11/28
CPC分类号： G05B23/0229 , G05B23/024 , G05B23/0283
摘要： A machine fault diagnosis system is provided. The system combines a rule-based predictive maintenance strategy with a machine learning system. A simple set of rules defined manually by human experts is used to generate artificial training feature vectors to portray machine fault conditions for which only a few real data points are available. Those artificial training feature vectors are combined with real training feature vectors and the combined set is used to train a supervised pattern recognition algorithm such as support vector machines. The resulting decision boundary closely approximates the underlying real separation boundary between the fault and normal conditions.
摘要翻译：提供机器故障诊断系统。该系统将基于规则的预测维护策略与机器学习系统相结合。人类专家手动定义的一套简单的规则用于生成人工训练特征向量，以描绘只有少数实际数据点可用的机器故障条件。将这些人工训练特征向量与真实训练特征向量相结合，组合集合用于训练支持向量机等监督模式识别算法。所得到的决策边界与故障和正常条件之间的基本实际分离边界紧密相近。

10. 发明申请

US20090234607A1 EVALUATING ANOMALY FOR ONE-CLASS CLASSIFIERS IN MACHINE CONDITION MONITORING 有权
标题翻译：在机器状态监测中评估一类分类器的异常
公开(公告)号：US20090234607A1
公开(公告)日：2009-09-17
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： G06F19/00
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。

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