Generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data

A computer program product, the computer program product comprising a non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to:

• (a) split a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each of the multiple sub-time series comprises a portion of the multiple data points in the first input time series;
• (h) generate, based on a full likelihood model fitting across the multiple data points in the first input time series, a first failure status predicting function of a first selected sub-time series from the multiple sub-time series that has the best fit to the multiple data points;
• (c) delete, from the first input time series, the portion of the multiple data points that corresponds to the first selected sub-time series, thereby generating a modified first input time series;
• (d) generate, based on a full likelihood model fitting across the multiple data points in the modified first input time series, a first failure status predicting function of a second selected sub-time series from the multiple sub-time series that has the best fit to the multiple data points excluding the deleted portion;
• (e) delete, from the modified first input time series, the portion of the multiple data points that corresponds to the second selected sub-time series, thereby generating a further modified first input time series;
• (f) generate, based on a partial likelihood model fitting across a given sub-set of the multiple data points in the first input time series, a second failure status predicting function of each selected sub-time series that has the best fit to the given sub-set of the multiple data points;
• (g) apply the second failure status predicting function of each selected sub-time series to a second input time series derived from the vehicular component to calculate multiple prediction of failure values for the second input time series;
• (h) identify the largest of the multiple prediction of failure values as an estimate of failure risk for the vehicular component;
• and
• (i) replace the vehicular component in each of the multiple vehicles based at least in part on the estimate of failure risk for the vehicular component.

A system comprising:

• a memory;
• and
• at least one processor coupled to the memory and configured for:
• (a) splitting a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each of the multiple sub-time series comprises a portion of the multiple data points in the first input time series;
• (b) generating, based on a full likelihood model fitting across the multiple data points in the first input time series, a first failure status predicting function of a first selected sub-time series from the multiple sub-time series that has the best fit to the multiple data points;
• (c) deleting, from the first input time series, the portion of the multiple data points that corresponds to the first selected sub-time series, thereby generating a modified first input time series;
• (d) generating, based on a full likelihood model fitting across the multiple data points in the modified first input time series, a first failure status predicting function of a second selected sub-time series from the multiple sub-time series that has the best fit to the multiple data points excluding the deleted portion;
• (e) deleting, from the modified first input time series, the portion of the multiple data points that corresponds to the second selected sub-time series, thereby generating a further modified first input time series;
• (f) generating, based on a partial likelihood model fitting across a given sub-set of the multiple data points in the first input time series, a second failure status predicting function of each selected sub-time series that has the best fit to the given sub-set of the multiple data points;
• (g) applying the second failure status predicting function of each selected sub-time series to a second input time series derived from the vehicular component to calculate multiple prediction of failure values for the second input time series;
• (h) identifying the largest of the multiple prediction of failure values as an estimate of failure risk for the vehicular component;
• and
• (i) replacing the vehicular component in each of the multiple vehicles based at least in part on the estimate of failure risk for the vehicular component.

A method comprising the following steps:

• (a) splitting a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series, wherein each of the multiple sub-time series comprises a portion of the multiple data points in the first input time series;
• (b) generating, based on a full likelihood model fitting across the multiple data points in the first input time series, a first failure status predicting function of a first selected sub-time series from the multiple sub-time series that has the best fit to the multiple data points;
• (c) deleting, from the first input time series, the portion of the multiple data points that corresponds to the first selected sub-time series, thereby generating a modified first input time series;
• (d) generating, based on a full likelihood model fitting across the multiple data points in the modified first input time series, a first failure status predicting function of a second selected sub-time series from the multiple sub-time series that has the best fit to the multiple data points excluding the deleted portion;
• (e) deleting, from the modified first input time series, the portion of the multiple data points that corresponds to the second selected sub-time series, thereby generating a further modified first input time series;
• (f) repeating, for a given number of additional selected sub-time series from the multiple sub-time series:
• generating, based on a full likelihood model fitting across the multiple data points in the further modified first input time series, a first failure status predicting function of an additional selected sub-time series from the multiple sub-time series that has the best fit to the multiple data points excluding the deleted portions;
• and
• deleting, from the further modified first input time series, the portion of the multiple data points that corresponds to the additional selected sub-time series;
• (g) generating, based on a partial likelihood model fitting across a given sub-set of the multiple data points in the first input time series, a second failure status predicting function of multiple combinations of two or more of the selected sub-time series that has the best fit to the given sub-set of the multiple data points;
• (h) applying the second failure status predicting function of each of the multiple combinations of two or more selected sub-time series to a second input time series derived from the vehicular component to calculate multiple prediction of failure values for the second input time series;
• (i) identifying the largest of the multiple prediction of failure values as an estimate of failure risk for the vehicular component;
• and
• (i) replacing the vehicular component in each of the multiple vehicles based at least in part on the estimate of failure risk for the vehicular component;
• wherein the steps are carried out by at least one computing device.