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81. 发明申请

WO2012128994A1 SYSTEM AND METHOD FOR VERIFICATION AND VALIDATION OF REDUNDANCY SOFTWARE IN PLC SYSTEMS 审中-公开
标题翻译： PLC系统中冗余软件验证和验证的系统与方法
公开(公告)号：WO2012128994A1
公开(公告)日：2012-09-27
申请号：PCT/US2012/028857
申请日：2012-03-13
申请人： SIEMENS CORPORATION , JI, Kun , SONG, Zhen
发明人： JI, Kun , SONG, Zhen
IPC分类号： G06F11/36
CPC分类号： G06F11/3664 , G06F8/35
摘要： Formal methods are instituted to verify and validate the finite state machine (FSM) of PLC redundancy software. The method and system is implemented through each phase in the lifecycle of the redundancy software; that is, the requirement phase, design phase, implementation phase and, finally, integration phase (including system integration). At each step along the way, the verification and validation process uses tools such as a checklist-based review and inspection, a requirement traceability analysis, formal verification (model checking) and the like to ensure that the created redundancy software is error-free and will perform as intended when implemented in the redundant PLC system
摘要翻译：建立正式的方法来验证和验证PLC冗余软件的有限状态机（FSM）。该方法和系统通过冗余软件生命周期中的每个阶段实现; 即需求阶段，设计阶段，实施阶段，最后是整合阶段（包括系统集成）。在此过程的每个步骤中，验证和验证过程都使用诸如基于清单的检查和检查，需求可追溯性分析，形式验证（模型检查）等工具，以确保创建的冗余软件无错误，将在冗余PLC系统中实现时按照要求执行

82. 发明申请

WO2012050854A1 RECONSTRUCTION OF PHASED ARRAY DATA 审中-公开
标题翻译：重新组合数据阵列数据
公开(公告)号：WO2012050854A1
公开(公告)日：2012-04-19
申请号：PCT/US2011/053573
申请日：2011-09-28
申请人： SIEMENS CORPORATION , SIEMENS ENERGY, INC. , ZHANG, Jingdan , KNORR, Moritz Michael , LI, Guozhen , ZHOU, Shaohua Kevin , RASSELKORDE, El Mahjoub , ABBASI, Waheed A. , METALA, Michael J.
发明人： ZHANG, Jingdan , KNORR, Moritz Michael , LI, Guozhen , ZHOU, Shaohua Kevin , RASSELKORDE, El Mahjoub , ABBASI, Waheed A. , METALA, Michael J.
IPC分类号： G06T17/00
CPC分类号： G06T17/00
摘要： An image reconstruction method includes receiving volume data comprising a plurality of sampling points (410), determining a first conditioning of the sampling points suppressing low amplitudes and conserving maximum amplitudes (411), determining a second conditioning of the sampling points wherein an influence of a sampling point depends on its distance to a grid point in a sampling grid (412), determining a kernel comprising a plurality of weighting functions for the first conditioning and the second conditioning to determine an energy spread of each of the plurality of sampling points without determining a shape or size of the kernel (413), and outputting a reconstructed volume according to the energy spread of each of the plurality of sampling points (414)
摘要翻译：一种图像重建方法，包括接收包括多个采样点（410）的体积数据，确定抑制低幅度并节省最大幅度的采样点的第一条件（411），确定采样点的第二条件，其中影响采样点取决于其到采样网格中与网格点的距离（412），确定包含用于第一条件的多个加权函数的内核和第二条件，以确定多个采样点中的每一个的能量扩展，而不确定核心的形状或大小（413），并且根据多个采样点（414）中的每一个的能量扩展输出重构的音量，

83. 发明申请

WO2012047529A1 ADAPTIVE REMOTE MAINTENANCE OF ROLLING STOCKS 审中-公开
标题翻译：自适应远程维护滚动条
公开(公告)号：WO2012047529A1
公开(公告)日：2012-04-12
申请号：PCT/US2011/052718
申请日：2011-09-22
申请人： SIEMENS CORPORATION , SIEMENS AKTIENGESELLSCHAFT , RAILEANU, Ciprian Dan , DEJORI, Mathäus , POPP, Frank , TOENSING, Ekkehard
发明人： RAILEANU, Ciprian Dan , DEJORI, Mathäus , POPP, Frank , TOENSING, Ekkehard
IPC分类号： G06F11/30
CPC分类号： B61L27/0094 , G06Q10/08 , G06Q50/28
摘要： Adaptive remote maintenance of rolling stocks is provided by machine-learning (28) of rules. Existing rules or models are automatically updated. Machine learning (28) is applied to establish a more efficient rule set. Rules may be replaced, generalized, or otherwise adapted based on interaction (26) by the dispatchers with the results of the current rules. The acceptance or discarding of an event by the dispatcher is used as a ground truth for supervised machine learning (28) of a new rule. The machine learning (28) uses user feedback to update the rule set.
摘要翻译：通过机器学习（28）的规则提供自适应远程维护。现有规则或模型将自动更新。机器学习（28）适用于建立更有效的规则集。规则可以根据调度员与当前规则的结果进行交互（26）进行替换，推广或以其他方式进行调整。调度人员对事件的接受或丢弃被用作新规则的监督机器学习（28）的基本实例。机器学习（28）使用用户反馈来更新规则集。

84. 发明申请

WO2011002954A1 METHOD, SYSTEM AND APPARATUS FOR PROVIDING AUTOMATION MANAGEMENT SERVICES 审中-公开
标题翻译：用于提供自动化管理服务的方法，系统和装置
公开(公告)号：WO2011002954A1
公开(公告)日：2011-01-06
申请号：PCT/US2010/040700
申请日：2010-07-01
申请人： SIEMENS CORPORATION , LO, Ren-Yi
发明人： LO, Ren-Yi
IPC分类号： H04L12/24 , G05B19/418
CPC分类号： H04L41/042 , G05B19/41845 , G05B19/4186 , H04L41/12 , H04L41/5058 , H04L43/00 , Y02P90/16 , Y02P90/185
摘要： An automation management system is deployed and operates on a plurality of network devices in a network in a distributed manner. The plurality of network devices may comprise a plurality of routers, for example. Automation management services are deployed and operate on the network devices in accordance with a service-oriented architecture model. At least one of the services may comprise a first instance of the service deployed on a first network device and a second instance of the service deployed on a second network device. Methods for deploying and operating an automation management system on a plurality of network devices in a network are also provided.
摘要翻译：自动化管理系统被部署并以分布式方式在网络中的多个网络设备上操作。多个网络设备可以包括例如多个路由器。根据面向服务的架构模型，自动化管理服务在网络设备上部署和运行。服务中的至少一个可以包括部署在第一网络设备上的服务的第一实例和部署在第二网络设备上的服务的第二实例。还提供了在网络中的多个网络设备上部署和操作自动化管理系统的方法。

85. 发明申请

WO2009042074A1 AUTOMATED VIEW CLASSIFICATION WITH ECHOCARDIOGRAPHIC DATA FOR GATE LOCALIZATION OR OTHER PURPOSES 审中-公开
标题翻译：用于门控定位或其他目的的自动化视图分类
公开(公告)号：WO2009042074A1
公开(公告)日：2009-04-02
申请号：PCT/US2008/010902
申请日：2008-09-18
申请人： SIEMENS MEDICAL SOLUTIONS USA, INC. , COMANICIU, Dorin , PARK, Jin Hyeong , ZHOU, Shaohua Kevin , SIMOPOULOS, Constantine , OTSUKI, Joanne
发明人： PARK, Jin Hyeong , ZHOU, Shaohua Kevin , SIMOPOULOS, Constantine , OTSUKI, Joanne
IPC分类号： G06K9/62 , G06T7/00
CPC分类号： G06K9/6257 , G06K9/6292 , G06K2209/05
摘要： A view represented by echocardiographic data is classified (64). A probabilistic boosting network (30) is used to classify the view. The probabilistic boosting network (30) may include multiple levels where each level has a multi-class local structure classifier (32) and a plurality of local-structure detectors (34) corresponding to the respective multiple classes. In each level, the local structure is classified (50) as a particular view and then the local structure is detected (54) to determine (56) whether the currently selected local structure corresponds to the class.
摘要翻译：由超声心动图数据表示的视图被分类（64）。概率增强网络（30）用于对视图进行分类。概率提升网络（30）可以包括多个级别，其中每个级别具有对应于相应的多个类别的多类局部结构分类器（32）和多个局部结构检测器（34）。在每个级别中，将本地结构分类为（50）作为特定视图，然后检测到本地结构（54）以确定（56）当前选择的本地结构是否对应于该类。

86. 发明申请

WO2008127539A1 MACHINE CONDITION MONITORING USING DISCONTINUITY DETECTION 审中-公开
标题翻译：使用不连续检测的机器状态监控
公开(公告)号：WO2008127539A1
公开(公告)日：2008-10-23
申请号：PCT/US2008/003717
申请日：2008-03-20
申请人： SIEMENS CORPORATE RESEARCH, INC. , NEUBAUER, Claus , YUAN, Chao
发明人： NEUBAUER, Claus , YUAN, Chao
IPC分类号： G05B23/02
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。

87. 发明申请

WO2008127535A1 MACHINE CONDITION MONITORING USING PATTERN RULES 审中-公开
标题翻译：使用模式规则进行机器状况监测
公开(公告)号：WO2008127535A1
公开(公告)日：2008-10-23
申请号：PCT/US2008/003642
申请日：2008-03-20
申请人： SIEMENS CORPORATE RESEARCH, INC. , SIEMENS AKTIENGESELLSCHAFT , HACKSTEIN, Holger , NEUBAUER, Claus , YUAN, Chao
发明人： HACKSTEIN, Holger , NEUBAUER, Claus , YUAN, Chao
IPC分类号： G05B23/02
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。

88. 发明申请

WO2007067521A1 USE OF SEQUENTIAL CLUSTERING FOR INSTANCE SELECTION IN MACHINE CONDITION MONITORING 审中-公开
标题翻译：在机器状态监测中使用连续选择的顺序聚类
公开(公告)号：WO2007067521A1
公开(公告)日：2007-06-14
申请号：PCT/US2006/046361
申请日：2006-12-05
申请人： SIEMENS CORPORATE RESEARCH, INC. , BALDERER, Christian , YUAN, Chao , NEUBAUER, Claus
发明人： BALDERER, Christian , YUAN, Chao , NEUBAUER, Claus
IPC分类号： G05B17/02 , G05B23/02
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 k d-tree. A distance threshold is used to limit the geometric size the clusters. Each node of the k d-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akd-tree组合，减少了从大型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。

89. 发明申请

WO2007067471A1 EVALUATING ANOMALY FOR ONE-CLASS CLASSIFIERS IN MACHINE CONDITION MONITORING 审中-公开
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：WO2007067471A1
公开(公告)日：2007-06-14
申请号：PCT/US2006/046199
申请日：2006-12-04
申请人： SIEMENS CORPORATE RESEARCH, INC. , NEUBAUER, Claus , YUAN, Chao
发明人： NEUBAUER, Claus , YUAN, Chao
IPC分类号： G05B19/418 , G05B19/406 , G05B19/23 , G06F17/18 , G06K9/62
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 R 1 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 R 1 .
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到包含所有可接受的测量的区域R 1的边界确定距离。该距离用于测量异常的程度。通过沿着从异常的线搜索到区域R 1 1中最接近的可接受的测量来找到该距离。

90. 发明申请

WO2007064860A1 ROBUST SENSOR CORRELATION ANALYSIS FOR MACHINE CONDITION MONITORING 审中-公开
标题翻译：用于机器状态监测的鲁棒传感器相关分析
公开(公告)号：WO2007064860A1
公开(公告)日：2007-06-07
申请号：PCT/US2006/045959
申请日：2006-12-01
申请人： SIEMENS CORPORATE RESEARCH, INC. , YUAN, Chao , BALDERER, Christian , HUANG, Tzu Kuo , NEUBAUER, Claus
发明人： YUAN, Chao , BALDERER, Christian , HUANG, Tzu Kuo , NEUBAUER, Claus
IPC分类号： G05B23/02
CPC分类号： G05B19/4065 , G05B23/024 , G05B2219/37508
摘要： A method for monitoring machine conditions is based on machine learning through the use of a statistical model. A correlation coefficient is calculated using weights assigned to each sample that indicate the likelihood that that sample is an outlier. The resulting correlation coefficient is more robust against outliers. The calculation of the weight is based on the Mahalanobis distance from the sample to the sample mean. Additionally, hierarchical clustering is applied to intuitively reveal group information among sensors. By specifying a similarity threshold, the user can easily obtain desired clustering results.
摘要翻译：用于监测机器状况的方法是基于通过使用统计模型的机器学习。使用分配给每个样本的权重来计算相关系数，指示该样本是异常值的可能性。所得到的相关系数对异常值更强。重量的计算基于从样品到样品平均值的马氏距离。另外，应用层次聚类来直观地显示传感器之间的组信息。通过指定相似性阈值，用户可以容易地获得所需的聚类结果。

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