Generating Estimates of Failure Risk for a Vehicular Component in Situations of High-Dimensional and Low Sample Size Data
First Claim
1. 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) 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; and
(h) identifying the largest of the multiple prediction of failure values as an estimate of failure risk for the vehicular component;
wherein at least one of the steps is carried out by a computing device.
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Abstract
Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data are provided herein. A method includes 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; generating a first failure status predicting function of a first selected sub-time series; deleting, from the first input time series, the portion of the data points that corresponds to the first selected sub-time series; repeating the preceding two steps for a second selected sub-time series; generating a second failure status predicting function of each selected sub-time series; applying each second failure status predicting function to a second input time series to calculate prediction of failure values; and identifying the largest prediction of failure value as an estimate of failure risk for the vehicular component.
7 Citations
20 Claims
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1. A method comprising the following steps:
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(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; and (h) identifying the largest of the multiple prediction of failure values as an estimate of failure risk for the vehicular component; wherein at least one of the steps is carried out by a computing device. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. A computer program product, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to:
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(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; (b) 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; and (h) identify the largest of the multiple prediction of failure values as an estimate of failure risk for the vehicular component.
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13. A system comprising:
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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; and (h) identifying the largest of the multiple prediction of failure values as an estimate of failure risk for the vehicular component.
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14. A method comprising the following steps:
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(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; and (i) identifying the largest of the multiple prediction of failure values as an estimate of failure risk for the vehicular component; wherein at least one of the steps is carried out by a computing device. - View Dependent Claims (15, 16, 17, 18, 19, 20)
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Specification