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    • 31. 发明申请
    • PREDICTION SYSTEM AND PROGRAM FOR PARTS SHIPMENT QUANTITY
    • 部分运输量的预测系统和程序
    • US20130332233A1
    • 2013-12-12
    • US13981094
    • 2011-02-23
    • Naoko KishikawaJun TateishiKenji Tamaki
    • Naoko KishikawaJun TateishiKenji Tamaki
    • G06Q30/02
    • G06Q30/0202G06Q10/08
    • There is provided a system or others capable of predicting a parts shipment quantity varying in accordance with an assumed parts-purchase period (such as a charge-free warranty period of a product) (having a period length with taking a product shipping date as a starting point during which a customer is assumed to purchase parts) so that prediction accuracy can be increased more than a conventional one. In the prediction system (1) for the parts shipment quantity, a server (10) has a prediction unit (100) to which product data (D1), parts shipment data (D2), and a prediction condition (D3) containing information of the assumed parts-purchase period are inputted and which performs a process of predicting a future shipment quantity for each parts and of outputting prediction result data (D0). The prediction unit (100) performs a process of predicting the future shipment quantity for each parts in accordance with the assumed parts-purchase period.
    • 提供了一种系统或其他能够预测根据假定的零件购买期间(例如,产品的免费保修期)变化的零件出货量(具有将产品发货日期视为 假设客户购买零件的起点),以便可以比传统的预测精度提高预测精度。 在用于零件装运量的预测系统(1)中,服务器(10)具有产品数据(D1),零件装运数据(D2)和预测条件(D3)的预测单元(100) 输入假定的零件购买期间,并执行预测每个零件的未来出货量并输出预测结果数据(D0)的处理。 预测单元(100)根据假定的零件购买期间进行各零件的未来出货量的预测处理。
    • 34. 发明申请
    • APPARATUS ABNORMALITY MONITORING METHOD AND SYSTEM
    • 装置不正常监测方法与系统
    • US20120136629A1
    • 2012-05-31
    • US13377242
    • 2010-05-13
    • Kenji TamakiToshiharu Miwa
    • Kenji TamakiToshiharu Miwa
    • G06F15/00
    • G05B23/0254
    • The present invention relates to an apparatus abnormality monitoring method, and it provides a technology that can achieve accurate abnormality detection, cause diagnosis, and others. This system relates to monitoring of a newly-installed apparatus (T) among a plurality of similar apparatuses 1. In a judgment model creation module 2, for each of a plurality of (K) already-installed similar apparatuses (a, b and others), individual judgment models (prediction models) are created, and a meta prediction model for predicting a coefficient and an intercept of these prediction models from feature item values and others of each of the apparatuses 1 is created. From this meta prediction model, a prediction model dedicated to the apparatus (T) (judgment model including the prediction model) is produced. By using this judgment model, a judgment module 3T monitors the state of the apparatus (T) to perform abnormality detection.
    • 本发明涉及一种装置异常监测方法,提供可以实现精确的异常检测,导致诊断等技术。 该系统涉及多台相似设备1中的新安装设备(T)的监视。在判断模型生成模块2中,对于已经安装的多个(K)的类似设备(a,b等) ),创建个体判断模型(预测模型),并且创建用于从特征项值和每个装置1中的其他值预测这些预测模型的系数和截距的元预测模型。 从该元预测模型,生成专用于装置(T)的预测模型(包括预测模型的判断模型)。 通过使用该判定模型,判断模块3T监视装置(T)的状态进行异常检测。
    • 35. 发明授权
    • Quality control system for manufacturing industrial products
    • 制造工业产品质量控制体系
    • US07209846B2
    • 2007-04-24
    • US11171394
    • 2005-07-01
    • Kenji TamakiYouichi Nonaka
    • Kenji TamakiYouichi Nonaka
    • G06F19/00
    • G06Q10/06
    • In a quality control system for manufacturing industrial products, the product quality history and the manufacturing process history are collected and collated to calculate the correlation magnitude between the two histories. The candidates for the cause of quality variation hidden in the manufacturing processes are listed, and the correlation magnitude between all combinations of the variates of the manufacturing process history are calculated. Further, by utilizing the manufacturing sequence history used for an input plan, a causation connecting structure model between the manufacturing processes of the manufacturing line is automatically generated and automatically analyzed thereby to automatically extract the fundamental cause of quality variation from the candidates for the cause of quality variation. By doing so, the cause of quality variation of industrial products manufactured through a complicated process can be traced in a complicated connecting structure in the manufacturing history data.
    • 在制造工业产品的质量控制体系中,收集和整理产品质量历史和制造过程历史,以计算两个历史之间的相关幅度。 列出制造过程中隐藏的质量变化原因的候选者,并且计算制造过程历史的变化的所有组合之间的相关大小。 此外,通过利用用于输入计划的制造顺序历史,生产线的制造过程之间的因果连接结构模型被自动生成并被自动分析,从而自动地从考生的原因中提取质量变化的根本原因 质量变化。 通过这样做,通过复杂工艺制造的工业产品的质量变化的原因可以在制造历史数据中以复杂的连接结构来描绘。
    • 36. 发明申请
    • Quality control system for manufacturing industrial products
    • 制造工业产品质量控制体系
    • US20060047454A1
    • 2006-03-02
    • US11171394
    • 2005-07-01
    • Kenji TamakiYouichi Nonaka
    • Kenji TamakiYouichi Nonaka
    • G06F19/00
    • G06Q10/06
    • In a quality control system for manufacturing industrial products, the product quality history and the manufacturing process history are collected and collated to calculate the correlation magnitude between the two histories. The candidates for the cause of quality variation hidden in the manufacturing processes are listed, and the correlation magnitude between all combinations of the variates of the manufacturing process history are calculated. Further, by utilizing the manufacturing sequence history used for an input plan, a causation connecting structure model between the manufacturing processes of the manufacturing line is automatically generated and automatically analyzed thereby to automatically extract the fundamental cause of quality variation from the candidates for the cause of quality variation. By doing so, the cause of quality variation of industrial products manufactured through a complicated process can be traced in a complicated connecting structure in the manufacturing history data.
    • 在制造工业产品的质量控制体系中,收集和整理产品质量历史和制造过程历史,以计算两个历史之间的相关幅度。 列出制造过程中隐藏的质量变化原因的候选者,并且计算制造过程历史的变化的所有组合之间的相关大小。 此外,通过利用用于输入计划的制造顺序历史,生产线的制造过程之间的因果连接结构模型被自动生成并被自动分析,从而自动地从考生的原因中提取质量变化的根本原因 质量变化。 通过这样做,通过复杂工艺制造的工业产品的质量变化的原因可以在制造历史数据中以复杂的连接结构来描绘。