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1. 发明授权

US06636842B1 System and method for controlling an industrial process utilizing process trajectories 有权
标题翻译：利用过程轨迹控制工业过程的系统和方法
公开(公告)号：US06636842B1
公开(公告)日：2003-10-21
申请号：US09616337
申请日：2000-07-15
申请人： Alexander Zambrano , Manuel Vazquez , Hugo E. Aponte , Alexis A Suarez , Jose Fernandes
发明人： Alexander Zambrano , Manuel Vazquez , Hugo E. Aponte , Alexis A Suarez , Jose Fernandes
IPC分类号： G05B1302
CPC分类号： G05B17/02 , G05B23/0245 , G05B2219/32018 , G05B2219/33296 , Y02P90/22
摘要： A method for monitoring an industrial process, including the steps of: providing a knowledge base comprising a two-dimensional representation of operational conditions for the process; obtaining process real time data from the industrial process for a plurality of process variables; selecting relevant features from the real time data; obtaining an operational condition classification from the relevant features; transforming the operational condition classification into a point; and displaying the point on said knowledge base.
摘要翻译：一种用于监测工业过程的方法，包括以下步骤：提供知识库，其包括用于该过程的操作条件的二维表示; 从多个过程变量的工业过程中获取过程实时数据; 从实时数据中选择相关特征; 从相关特征获取操作条件分类; 将作业条件分类转化为一个点; 并在所述知识库上显示该点。

2. 发明授权

US6108616A Process diagnosis system and method for the diagnosis of processes and states in an technical process 失效
标题翻译：过程诊断系统和方法，用于诊断技术过程中的过程和状态
公开(公告)号：US6108616A
公开(公告)日：2000-08-22
申请号：US123079
申请日：1998-07-27
申请人： Hans-Werner Borchers , Ralf Otte , Rainer Speh , Claus Weisang
发明人： Hans-Werner Borchers , Ralf Otte , Rainer Speh , Claus Weisang
IPC分类号： G05B19/048 , G05B23/02 , G05B15/02 , G05B19/42
CPC分类号： G05B23/0213 , G05B23/0254 , G05B23/0297 , G05B2219/31211 , G05B2219/31434 , G05B2219/33277 , G05B2219/33296 , Y10S706/907
摘要： The invention relates to a process diagnosis system and a method for the diagnosis of processes and states in a technical process, in particular for the diagnosis of a power station process. The invention is a structured multi-agent system which corresponds to the process and has a plurality of autonomous diagnostic agents. The diagnostic agents in each case contain a neural network, with whose aid a reference behavior of process components that are to be monitored can be learned. In addition, automatic adaptation to a new reference behavior is also possible by the diagnostic agents.
摘要翻译：本发明涉及一种用于在技术过程中诊断过程和状态的过程诊断系统和方法，特别是用于电站过程的诊断。本发明是对应于该过程并具有多个自主诊断剂的结构化多代理系统。诊断代理在每种情况下都包含一个神经网络，借助这些神经网络可以了解到要监视的过程组件的参考行为。此外，诊断剂也可以自动适应新的参考行为。

3. 发明申请

US20090300417A1 SYSTEM AND METHOD FOR ADVANCED CONDITION MONITORING OF AN ASSET SYSTEM 有权
标题翻译：资产系统高级状态监测系统与方法
公开(公告)号：US20090300417A1
公开(公告)日：2009-12-03
申请号：US12129632
申请日：2008-05-29
申请人： Piero Patrone Bonissone , John Erik Hershey , Robert James Mitchell, JR. , Rajesh Venkat Subbu, JR. , Avinash Vinayak Taware , Xiao Hu
发明人： Piero Patrone Bonissone , John Erik Hershey , Robert James Mitchell, JR. , Rajesh Venkat Subbu, JR. , Avinash Vinayak Taware , Xiao Hu
IPC分类号： G06F11/28
CPC分类号： G05B23/0254 , G05B2219/33295 , G05B2219/33296 , G07C3/00
摘要： A method for advanced condition monitoring of an asset system includes using a plurality of auto-associative neural networks to determine estimates of actual values sensed by at least one sensor in at least one of the plurality of operating regimes; determining a residual between the estimated sensed values and the actual values sensed by the at least one sensor from each of the plurality of auto-associative neural networks; and combining the residuals by using a fuzzy supervisory model blender; performing a fault diagnostic on the combined residuals; and determining a change of the operation of the asset system by analysis of the combined residuals. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.
摘要翻译：一种用于资产系统的高级状态监测的方法包括使用多个自动关联神经网络来确定至少一个传感器在所述多个操作方案中的至少一个中感测到的实际值的估计; 从所述多个自相关神经网络中的每一个确定估计的感测值与由所述至少一个传感器感测的实际值之间的残差; 并通过使用模糊监督模型混合器组合残差; 对组合残差进行故障诊断; 以及通过分析组合残差来确定资产系统的操作变化。必要时提供警报。智能传感器系统包括用于执行本发明的方法的车载处理单元。

4. 发明申请

US20080127793A1 Working process monitoring device 审中-公开
标题翻译：工作过程监控装置
公开(公告)号：US20080127793A1
公开(公告)日：2008-06-05
申请号：US11987441
申请日：2007-11-30
申请人： Kazutaka Ikeda
发明人： Kazutaka Ikeda
IPC分类号： B23Q15/20 , B23Q15/013
CPC分类号： G01H1/003 , G05B19/406 , G05B2219/33296 , G05B2219/37435 , G05B2219/43124 , G05B2219/45229 , Y10T83/145 , Y10T83/159
摘要： A working process monitoring device includes: a sensor unit for detecting at least one of vibrations and sound waves produced while processing a workpiece by a working machine rotatably driven by a driving unit; a signal input unit for extracting target signals from an electric signal outputted from the sensor unit; and an amount of characteristics extracting unit for extracting amounts of characteristics including a plurality of parameters from the target signals. Further, the working process monitoring device includes a material inspecting unit for detecting whether a material of a portion of the workpiece currently being processed is normal in quality or not, wherein the material inspecting unit employs a neural network trained by using amounts of characteristics extracted from target signals obtained while processing a workpiece made of a normal quality material.
摘要翻译：工作过程监控装置包括：传感器单元，用于检测通过由驱动单元可旋转地驱动的工作机器处理工件时产生的振动和声波中的至少一个; 信号输入单元，用于从传感器单元输出的电信号中提取目标信号; 以及一定量的特征提取单元，用于从目标信号中提取包括多个参数的特征量。此外，工作过程监视装置包括用于检测当前正在处理的工件的一部分材料的质量是否正常的材料检查单元，其中材料检查单元采用通过使用从在处理由正常质量材料制成的工件时获得的目标信号。

5. 发明申请

US20180164757A1 MACHINE LEARNING DEVICE, CNC DEVICE AND MACHINE LEARNING METHOD FOR DETECTING INDICATION OF OCCURRENCE OF CHATTER IN TOOL FOR MACHINE TOOL 审中-公开
公开(公告)号：US20180164757A1
公开(公告)日：2018-06-14
申请号：US15840211
申请日：2017-12-13
申请人： FANUC CORPORATION
发明人： Takahiko MATSUSHIMA , Yoshito MIYAZAKI
IPC分类号： G05B13/04 , G05B13/02
CPC分类号： G05B13/042 , G05B13/027 , G05B19/406 , G05B2219/33296 , G05B2219/37433 , G05B2219/37434 , G06N3/0454 , G06N3/088
摘要： A machine learning device for detecting an indication of an occurrence of chatter in a tool for a machine tool, includes a state observation unit which observes at least one state variable of a vibration of the machine tool itself, a vibration of a building in which the machine tool is installed, an audible sound, an acoustic emission and a motor control current value of the machine tool, in addition to a vibration of the tool; and a learning unit which generates a learning model based on the state variable observed by the state observation unit.

6. 发明授权

US5579232A System and method including neural net for tool break detection 失效
标题翻译：系统和方法包括用于工具断裂检测的神经网络
公开(公告)号：US5579232A
公开(公告)日：1996-11-26
申请号：US401333
申请日：1995-03-09
申请人： David W. Tong , Kenneth M. Martin , Jerry H. Carmichael , Edward N. Diei
发明人： David W. Tong , Kenneth M. Martin , Jerry H. Carmichael , Edward N. Diei
IPC分类号： G05B19/4065 , G06F19/00
CPC分类号： G05B19/4065 , G05B2219/33268 , G05B2219/33296 , G05B2219/34047 , G05B2219/37245 , Y10S706/912
摘要： A system and method for monitoring vibrations of a cutting tool uses a neural network for classifying signal features as break or non-break or, in another embodiment, as non-break or abnormal. A vibration signal is produced by an accelerometer, positioned to sense vibrations at the tool-workpiece interface. The signal is pre-processed to extract low frequency machining noise and detect the energy in a higher frequency band. The signal is then sampled and segments of the digitized signals are processed by digital logic into feature vectors for input to a trained neural net having two output nodes for classification. The use of a neural net provides performance improvement and economies over previously known heuristic methods of signal analysis.
摘要翻译：用于监测切割工具的振动的系统和方法使用神经网络将信号特征分类为断裂或非断裂，或者在另一个实施例中作为非断裂或异常。振动信号由加速度计产生，其被定位成感测在工具 - 工件界面处的振动。信号被预处理以提取低频加工噪声并检测更高频带中的能量。然后对信号进行采样，并将数字化信号的段由数字逻辑处理成特征向量，以输入到具有两个用于分类的输出节点的训练神经网络。神经网络的使用提供了相对于先前已知的启发式信号分析方法的性能改进和经济性。

7. 发明申请

US20170060356A1 NUMERICAL CONTROLLER WITH MENU 审中-公开
标题翻译：带菜单的数控系统
公开(公告)号：US20170060356A1
公开(公告)日：2017-03-02
申请号：US15233164
申请日：2016-08-10
申请人： FANUC CORPORATION
发明人： Rie Oota , Mamoru Kubo
IPC分类号： G06F3/0482 , G06N99/00
CPC分类号： G06F3/0482 , G05B19/409 , G05B2219/31264 , G05B2219/33296 , G05B2219/36089 , G05B2219/36127 , G06F3/04817 , G06N20/00
摘要： A numerical controller acquires state data including information indicating a machining state and information indicating a selected menu item, creates a machine learning model for determining a menu item display order in menu display based on the state data, and determines a menu item display order in the menu display based on the created machine learning model and the state data.
摘要翻译：数值控制器获取包括指示加工状态的信息的状态数据和指示所选择的菜单项的信息，基于状态数据创建用于确定菜单显示中的菜单项显示顺序的机器学习模型，并且确定菜单项显示顺序基于创建的机器学习模型和状态数据的菜单显示。

8. 发明授权

US07778724B2 Device for estimating machining dimension of machine tool 失效
标题翻译：机床加工尺寸估算装置
公开(公告)号：US07778724B2
公开(公告)日：2010-08-17
申请号：US11902546
申请日：2007-09-24
申请人： Kazutaka Ikeda
发明人： Kazutaka Ikeda
IPC分类号： G06F19/00
CPC分类号： G05B19/406 , G05B2219/33296 , G05B2219/37435 , Y10T82/2595 , Y10T409/309576
摘要： A device for estimating machining dimensions of a machine tool which employs tool members each being rotatably driven by a driving unit includes: a vibration sensor; a characteristics extracting unit for extracting amounts of characteristics from an output of the vibration sensor; a neural network for classifying the amounts of characteristics into categories; and a conversion unit. Amounts of characteristics of generated output by racing the tool member are used for training the neural network, and inputted again to the trained competitive learning neural network to excite neurons so that the relationships between Euclidean distances and machining dimensions of workpieces are registered in the conversion unit. The Euclidean distances are obtained between weight vectors of the excited neurons and respective corresponding training samples, and the machining dimensions are obtained when the workpieces are machined by the tool members at the same condition as the respective corresponding training samples are obtained.
摘要翻译：一种用于估计机床的加工尺寸的装置，其采用各自由驱动单元旋转驱动的工具构件，包括：振动传感器; 特征提取单元，用于从所述振动传感器的输出中提取特征量; 用于将特征量分类为类别的神经网络; 和转换单元。通过赛车工具构件产生的输出的特征量用于训练神经网络，并再次输入训练有素的竞争学习神经网络，以激发神经元，使得欧氏距离与工件加工尺寸之间的关系被记录在转换单元中。在激发的神经元的权重向量和相应的训练样本之间获得欧几里德距离，并且当在与获得相应的相应训练样本相同的条件下工具被加工时获得加工尺寸。

9. 发明申请

US20060259198A1 Intelligent system for detection of process status, process fault and preventive maintenance 审中-公开
标题翻译：用于检测过程状态，过程故障和预防性维护的智能系统
公开(公告)号：US20060259198A1
公开(公告)日：2006-11-16
申请号：US11441050
申请日：2006-05-26
申请人： Jozef Brcka , Deana Delp , Michael Grapperhaus , Paul Moroz
发明人： Jozef Brcka , Deana Delp , Michael Grapperhaus , Paul Moroz
IPC分类号： G05B19/04
CPC分类号： G05B23/0254 , G05B19/4184 , G05B2219/24019 , G05B2219/31357 , G05B2219/32234 , G05B2219/33296 , G05B2219/45031 , Y02P90/14 , Y02P90/18 , Y02P90/20 , Y02P90/26 , Y02P90/86
摘要： Embodiments of an intelligent modeling method and system monitor and perform analysis of semiconductor processing equipment as well as predict future states of that equipment based on the analysis, predict failures of the semiconductor processing equipment and/or determine equipment maintenance schedules.
摘要翻译：智能建模方法和系统的实施例基于分析，预测半导体处理设备的故障和/或确定设备维护计划来监测和执行半导体处理设备的分析以及预测该设备的未来状态。

10. 发明申请

US20180059639A1 CELL CONTROL SYSTEM 审中-公开
公开(公告)号：US20180059639A1
公开(公告)日：2018-03-01
申请号：US15685129
申请日：2017-08-24
申请人： FANUC CORPORATION
发明人： Manabu SAITOU
IPC分类号： G05B19/406 , G06N99/00
CPC分类号： G05B19/406 , G05B2219/33296 , G05B2219/36487 , G05B2219/37337 , G06N3/02 , G06N20/00 , Y02P90/14 , Y02P90/18 , Y02P90/20
摘要： A cell control system capable of estimating a cause of an alarm by estimating an influence of noise in a plurality of machines includes a machine operation instruction unit for transmitting an operation instruction to a managed manufacturing machine, a noise value collection unit for collecting detected noise information, an operation information collection unit for collecting operation information of a manufacturing machine, a learning unit for creating a learning model by performing machine learning using the collected operation information collected as an input signal and the detected noise information as an instruction signal, an estimation unit for analyzing the learning model to estimate operation information corresponding to a noise factor, and an operation instruction change unit for instructing the machine operation instruction unit to change instruction content based on the operation information corresponding to the noise factor.

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