A MOBILE PHONE WITH SYSTEM FAILURE PREDICTION USING LONG SHORT-TERM MEMORY NEURAL NETWORKS
    1.
    发明申请
    A MOBILE PHONE WITH SYSTEM FAILURE PREDICTION USING LONG SHORT-TERM MEMORY NEURAL NETWORKS 审中-公开
    使用长短期记忆神经网络的系统故障预测移动电话

    公开(公告)号:WO2017177018A1

    公开(公告)日:2017-10-12

    申请号:PCT/US2017/026377

    申请日:2017-04-06

    Abstract: Mobile phones and methods for mobile phone failure prediction include receiving respective log files from one or more mobile phone components, including at least one user application. The log files have heterogeneous formats. A likelihood of failure of one or more mobile phone components is determined based on the received log files by clustering the plurality of log files according to structural log patterns and determining feature representations of the log files based on the log clusters. A user is alerted to a potential failure if the likelihood of component failure exceeds a first threshold. An automatic system control action is performed if the likelihood of component failure exceeds a second threshold.

    Abstract translation: 用于手机故障预测的移动电话和方法包括从一个或多个移动电话组件接收各个日志文件,所述移动电话组件包括至少一个用户应用程序。 日志文件具有不同的格式。 基于接收到的日志文件,通过根据结构化日志模式对多个日志文件进行群集并且基于日志群集来确定日志文件的特征表示来确定一个或多个移动电话部件的故障的可能性。 如果组件故障的可能性超过第一阈值,则用户被警告潜在的故障。 如果组件故障的可能性超过第二阈值,则执行自动系统控制动作。

    SYSTEM FAILURE PREDICTION USING LONG SHORT-TERM MEMORY NEURAL NETWORKS
    2.
    发明申请
    SYSTEM FAILURE PREDICTION USING LONG SHORT-TERM MEMORY NEURAL NETWORKS 审中-公开
    使用长短期记忆神经网络的系统故障预测

    公开(公告)号:WO2017177012A1

    公开(公告)日:2017-10-12

    申请号:PCT/US2017/026370

    申请日:2017-04-06

    Abstract: Methods for system failure prediction include clustering log files according to structural log patterns. Feature representations of the log files are determined based on the log clusters. A likelihood of a system failure is determined based on the feature representations using a neural network. An automatic system control action is performed if the likelihood of system failure exceeds a threshold.

    Abstract translation:

    系统故障预测的方法包括根据结构日志模式对日志文件进行聚类。 日志文件的功能表示是根据日志群集确定的。 基于使用神经网络的特征表示来确定系统故障的可能性。 如果系统故障的可能性超过阈值,则执行自动系统控制动作。

Patent Agency Ranking