Generating Estimates of Failure Risk for a Vehicular Component

摘要

Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component are provided herein. A method includes splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each sub-time series comprises multiple data points of the input time series that correspond to measurements derived from the vehicular component; determining a weight applied to each of the sub-time series based on a pre-determined weight associated with the input time series; applying a failure or non-failure classification label to each of the sub-time series and the input time series; calculating a performance measure for the input time series; determining an updated weight associated with the input time series; and generating an estimate of failure risk for the vehicular component based on the classification label applied to each input time series and the updated weight.

主权项

1. A method comprising the following steps:
splitting an input time series pertaining to a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each of the multiple sub-time series comprises multiple data points of the input time series that correspond to one of multiple measurements derived from the vehicular component; determining a weight applied to each of the multiple sub-time series based on a pre-determined weight associated with the input time series, wherein said determining comprises associating the pre-determined weight associated with the input time series with each of the multiple sub-time series; applying one of two classification labels to each of the multiple sub-time series based on a relationship of the data points of the given sub-time series and a selected threshold value, wherein the classification labels comprise (i) associated with a failure of the vehicular component and (ii) not associated with a failure of the vehicular component; applying one of the two classification labels to the input time series to correspond to the label applied to a majority of the multiple sub-time series; calculating a performance measure for the input time series based on (i) the weight applied to each of the multiple sub-time series, and (ii) a determination as to whether the correct classification label has been applied to each of the multiple sub-time series; determining an updated weight associated with the input time series based on (i) the pre-determined weight associated with the input time series, (ii) the weight applied to each of the multiple sub-time series, and/or (iii) the performance measure; and generating an estimate of failure risk for the vehicular component based on (i) the classification label applied to each input time series and (ii) the updated weight associated with the input time series; wherein at least one of the steps is carried out by a computing device.