公开(公告)号：US20140236874A1

公开(公告)日：2014-08-21

申请号：US14264113

申请日：2014-04-29

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Jeffrey A. Abell ,  John Patrick Spicer ,  Michael Anthony Wincek ,  Hui Wang ,  Debejyo Chakraborty

IPC: G06N99/00

CPC classification number: G06N99/005 ,  B23K20/10 ,  B23K20/106 ,  B23K20/12 ,  G05B1/00 ,  G05B1/01 ,  G05B1/04 ,  G05B11/00 ,  G05B11/01 ,  G05B11/011 ,  G05B19/00 ,  G05B19/02 ,  G05B19/04 ,  G05B19/18 ,  G05B23/0221

Abstract: A system includes host and learning machines. Each machine has a processor in electrical communication with at least one sensor. Instructions for predicting a binary quality status of an item of interest during a repeatable process are recorded in memory. The binary quality status includes passing and failing binary classes. The learning machine receives signals from the at least one sensor and identifies candidate features. Features are extracted from the candidate features, each more predictive of the binary quality status. The extracted features are mapped to a dimensional space having a number of dimensions proportional to the number of extracted features. The dimensional space includes most of the passing class and excludes at least 90 percent of the failing class. Received signals are compared to the boundaries of the recorded dimensional space to predict, in real time, the binary quality status of a subsequent item of interest.

Abstract translation: 系统包括主机和学习机。 每个机器具有与至少一个传感器电通信的处理器。 用于在可重复处理期间预测感兴趣的项目的二进制质量状态的指令被记录在存储器中。 二进制质量状态包括传递和失败的二进制类。 学习机器接收来自至少一个传感器的信号并识别候选特征。 从候选特征中提取特征，每个特征更加预测二进制质量状态。 提取的特征被映射到具有与提取的特征的数量成比例的数量的维数的维空间。 维空间包含大部分传递类，并排除失败类的至少90％。 将接收的信号与记录的维度空间的边界进行比较，以实时地预测随后的感兴趣的物品的二进制质量状态。

公开(公告)号：US20130105556A1

公开(公告)日：2013-05-02

申请号：US13632670

申请日：2012-10-01

Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

Inventor： Jeffrey A. Abell ,  John Patrick Spicer ,  Michael Anthony Wincek ,  Hui Wang ,  Debejyo Chakraborty

IPC: B23K20/10 ,  G06F15/18

CPC classification number: G06N99/005 ,  B23K20/10 ,  B23K20/106 ,  B23K20/12 ,  G05B1/00 ,  G05B1/01 ,  G05B1/04 ,  G05B11/00 ,  G05B11/01 ,  G05B11/011 ,  G05B19/00 ,  G05B19/02 ,  G05B19/04 ,  G05B19/18 ,  G05B23/0221

Abstract: A system includes host and learning machines in electrical communication with sensors positioned with respect to an item of interest, e.g., a weld, and memory. The host executes instructions from memory to predict a binary quality status of the item. The learning machine receives signals from the sensor(s), identifies candidate features, and extracts features from the candidates that are more predictive of the binary quality status relative to other candidate features. The learning machine maps the extracted features to a dimensional space that includes most of the items from a passing binary class and excludes all or most of the items from a failing binary class. The host also compares the received signals for a subsequent item of interest to the dimensional space to thereby predict, in real time, the binary quality status of the subsequent item of interest.

Abstract translation: 系统包括与相对于感兴趣的物体定位的传感器例如焊接和存储器进行电气通信的主机和学习机器。 主机执行来自内存的指令来预测项目的二进制质量状态。 学习机器从传感器接收信号，识别候选特征，并从相对于其他候选特征更加预测二进制质量状态的候选中提取特征。 学习机器将提取的特征映射到包含来自传递二进制类的大多数项目的维空间，并且从失败的二进制类中排除所有或大部分项目。 主机还将用于随后的感兴趣项目的接收信号与尺寸空间进行比较，从而实时地预测随后的感兴趣项目的二进制质量状态。