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    • 3. 发明申请
    • TRANSFERRING FAILURE SAMPLES USING CONDITIONAL MODELS FOR MACHINE CONDITION MONITORING
    • 使用条件模型传输故障样本进行机器状态监测
    • US20170031792A1
    • 2017-02-02
    • US15303243
    • 2014-04-16
    • Siemens Aktiengesellschaft
    • Chao YUANAmit CHAKRABORTYHolger HACKSTEINHans WEBER
    • G06F11/22G06N99/00
    • G06F11/2257G05B23/0224G06N99/005
    • A method for predicting failure modes in a machine includes learning (31) a multivariate Gaussian distribution for each of a source machine and a target machine from data samples from one or more independent sensors of the source machine and the target machine, learning (32) a multivariate Gaussian conditional distribution for each of the source machine and the target machine from data samples from one or more dependent sensors of the source machine and the target machine using the multivariate Gaussian distribution for the independent sensors, transforming (33) data samples for the independent sensors from the source machine to the target machine using the multivariate Gaussian distributions for the source machine and the target machine, and transforming (34) data samples for the dependent sensors from the source machine to the target machine using the transformed independent sensor data samples and the conditional Gaussian distributions for the source machine and the target machine.
    • 一种用于预测机器中的故障模式的方法包括从来源机器和目标机器的一个或多个独立传感器的数据样本学习(31)源机器和目标机器中的每一个的多变量高斯分布,学习(32) 来自源机器和目标机器的来自一个或多个从属传感器的数据样本中的每个源机器和目标机器的多变量高斯条件分布,使用用于独立传感器的多元高斯分布,将(33)数据样本转换为 使用源机器和目标机器的多元高斯分布从源机器到目标机器的独立传感器,以及使用变换后的独立传感器数据采样器(34)将来自源机器的依赖传感器的数据样本转换到目标机器 以及源机器和目标机器的条件高斯分布。
    • 5. 发明授权
    • Abnormality diagnosis device and abnormality diagnosis system for servo control device
    • 伺服控制装置异常诊断装置及异常诊断系统
    • US09348332B2
    • 2016-05-24
    • US13978270
    • 2011-03-29
    • Shogo Morita
    • Shogo Morita
    • G05B23/02G05B9/02G06F11/22
    • G05B23/0272G05B9/02G05B23/027G06F11/2257
    • An abnormality diagnosis device includes a factor-data storage unit that stores therein factor data including a candidate of an alarm-generating factor corresponding to an alarm number; a stored-data storage unit that stores therein, as stored data, statistical data acquired from a statistical data server including the alarm number corresponding to a system configuration number of a servo control device classified based on a system configuration of the servo control device, and a candidate of the alarm-generating factor corresponding to the alarm number, and a generation probability of the candidate; a display unit; and an abnormality-diagnosis processing unit that acquires the alarm number from the servo control device when an alarm is generated, and based on the alarm number, the factor data, and the stored data, adds the generation probability to each candidate and displays, on the display unit, the candidate with the generation probability added.
    • 异常诊断装置包括因子数据存储单元,其存储包括与报警号相对应的报警生成因子的候选的因子数据; 存储数据存储单元,作为存储数据存储从包括与基于伺服控制装置的系统配置分类的伺服控制装置的系统配置号对应的报警号的统计数据服务器获取的统计数据,以及 对应于报警号的报警产生因子的候选者和候选者的生成概率; 显示单元; 以及异常诊断处理单元,其在生成警报时从伺服控制装置获取警报号,并且基于警报号,因子数据和存储的数据,将生成概率相加给每个候选者并显示在 显示单元,添加生成概率的候选者。
    • 7. 发明申请
    • MANAGEMENT SYSTEM FOR MANAGING COMPUTER SYSTEM COMPRISING MULTIPLE MONITORING-TARGET DEVICES
    • 用于管理包含多个监视目标设备的计算机系统的管理系统
    • US20160103727A1
    • 2016-04-14
    • US14971187
    • 2015-12-16
    • HITACHI, LTD.
    • Jun NAKAJIMAMasataka NAGURA
    • G06F11/07
    • G06F11/079G06F11/0709G06F11/0727G06F11/0751G06F11/0793G06F11/1484G06F11/2257G06F11/3051
    • A management system manages a computer system including multiple monitoring-target devices. A storage device of the management system stores a general rule, general plan information, unresolved information, and configuration information. A control device of the management system creates multiple expanded rules based on the general rule and the configuration information, and if an event related to any of the multiple monitoring-target devices has occurred, identifies, based on the multiple expanded rules, a first conclusion event constituting a candidate for the cause of the occurred event, creates, based on the general plan information, one or more expanded plans, which are recovery plans that can be implemented if the first conclusion event is a cause, identifies an unresolved event based on the unresolved information, identifies a risk site based on the identified unresolved event, and displays data showing the first conclusion event, expanded plan, and risk site.
    • 管理系统管理包括多个监控目标设备的计算机系统。 管理系统的存储装置存储一般规则,一般计划信息,未解决的信息和配置信息。 管理系统的控制装置基于通用规则和配置信息创建多个扩展规则,并且如果发生与多个监控目标设备中的任一个相关的事件,则基于多个扩展规则识别第一结论 构成事件原因的候选人的事件根据总体规划信息创建一个或多个扩展计划,即如果第一个结论事件是原因可以实现的恢复计划,则基于以下原因识别未解决的事件: 未解决的信息,基于所识别的未解决事件识别风险站点,并显示显示第一个结论事件,扩展计划和风险站点的数据。
    • 10. 发明申请
    • ERROR PREDICTION WITH PARTIAL FEEDBACK
    • 错误预测与部分反馈
    • US20150019912A1
    • 2015-01-15
    • US13937271
    • 2013-07-09
    • Xerox Corporation
    • William Michael DarlingGuillaume M. BouchardCedric Archambeau
    • G06F11/22
    • G06F11/2257G06F11/079G06F11/2236G06F11/3684
    • A method for performing data processing through a pipeline of components includes receiving a set of training observations, each including partial user feedback relating to error in data output by the pipeline for respective input data. Some pipeline components commit errors for at least some of the input data, contributing to an error in the respective output data. A prediction model models a probability of a pipeline component committing an error, given input data. Model parameters are learned using the training observations. For a new observation which includes input data and, optionally, partial user feedback indicating that an error has occurred in processing the new input data, without specifying which pipeline component(s) contributed to the observed error in the output data, a prediction is made as to which of the pipeline components contributed to the error in the output (if any).
    • 一种通过组件流水线执行数据处理的方法包括:接收一组训练观测值,每组训练观测值包括与用于相应输入数据的流水线输出的数据中的错误有关的部分用户反馈。 一些流水线组件针对至少一些输入数据提交错误,导致相应输出数据中的错误。 给定输入数据,预测模型建立了一个管道组件提交错误的概率。 使用训练观察学习模型参数。 对于包括输入数据和可选地,指示在处理新的输入数据时已经发生错误的部分用户反馈的新观察,而不指定对输出数据中观察到的误差有贡献的流水线分量,进行预测 哪些管道组件导致输出中的错误(如果有的话)。