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    • 3. 发明授权
    • Decomposition technique for remaining useful life prediction
    • 剩余使用寿命预测的分解技术
    • US08725456B1
    • 2014-05-13
    • US12454024
    • 2009-05-05
    • Bhaskar SahaKai F. GoebelAbhinav SaxenaJose R. Celaya
    • Bhaskar SahaKai F. GoebelAbhinav SaxenaJose R. Celaya
    • G06F17/18G06F19/00
    • G05B23/0283
    • The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation. Remaining life is computed by subtracting the instance when the extrapolated damage reaches the failure threshold from the instance when the prediction is made.
    • 这里公开的预测工具分解了将组件或子系统的剩余使用寿命(RUL)估计为两个单独的回归问题的问题:特征对损伤映射和操作条件损坏率映射。 这些映射最初是以离线模式生成的。 使用一个或多个回归算法从测量(和从这些导出的特征),操作条件和地面真实信息中产生这些映射中的每一个。 这种分解技术允许对过程中存在的不确定不确定来源进行明确的量化和管理。 接下来,地图在线模式下使用,其中运行时数据(传感器测量和操作条件)与离线模式中生成的映射结合使用,以估计当前的损坏状态以及未来的损坏积累。 当外推损伤达到故障阈值时,从进行预测的情况下减去实例,计算剩余寿命。