Generating Cumulative Wear-Based Indicators for Vehicular Components
First Claim
1. A method comprising the following steps:
- assigning a failure class label to each data point, from a set of multiple data points derived from measurements associated with a vehicular component across a fleet of multiple vehicles, that (a) is associated with a failure-caused vehicular component replacement, and (b) is within a pre-specified number of runtime hours of the failure-caused vehicular component replacement;
assigning a non-failure class label to each data point, from the set of the multiple lo data points, that (a) is associated with a failure-caused vehicular component replacement, and (b) is not within the pre-specified number of runtime hours of the failure-caused vehicular component replacement;
assigning a non-failure class label to each data point, from the set of the multiple data points, that is associated with a scheduled vehicular component replacement;
assigning a non-failure class label to each data point, from the set of the multiple data points, that is associated with an actively running instance of the vehicular component as yet to be replaced;
estimating a failure probability for the vehicular component at each of the multiple data points over a pre-specified future runtime of the vehicular component based on the class label assigned to each of the multiple data points;
determining a cumulative hazard function for the vehicular component based on the failure probability, wherein said cumulative hazard function assesses the amount of accumulated risk that the vehicular component has faced from a given start time until the present time; and
generating a cumulative wear-based indicator for the vehicular component by executing a regression function at a given time based on (i) the cumulative hazard function, (ii) one or more selected parameters, and (iii) a determination as to whether the vehicular component (a) was previously replaced due to a failure, (b) was previously replaced due to a non-failure scheduled replacement, or (c) is actively running as yet to be replaced;
wherein at least one of the steps is carried out by a computing device.
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Accused Products
Abstract
Methods, systems, and computer program products for generating wear-based indicators are provided herein. A method includes assigning a failure label to each data point associated with a component associated with a failure-caused component replacement within a pre-specified number of runtime hours of the failure-caused component replacement; assigning a non-failure label to each data point associated with a failure-caused component replacement and not within the pre-specified number of runtime hours; assigning a non-failure label to each data point associated with a scheduled component replacement; assigning a non-failure label to each data point associated with an actively running instance of the component as yet to be replaced; estimating a failure probability for the component over a pre-specified future runtime; determining a cumulative hazard function for the component based on the failure probability; and generating a cumulative wear-based indicator for the component by executing a regression function based on the cumulative hazard function.
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Citations
20 Claims
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1. A method comprising the following steps:
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assigning a failure class label to each data point, from a set of multiple data points derived from measurements associated with a vehicular component across a fleet of multiple vehicles, that (a) is associated with a failure-caused vehicular component replacement, and (b) is within a pre-specified number of runtime hours of the failure-caused vehicular component replacement; assigning a non-failure class label to each data point, from the set of the multiple lo data points, that (a) is associated with a failure-caused vehicular component replacement, and (b) is not within the pre-specified number of runtime hours of the failure-caused vehicular component replacement; assigning a non-failure class label to each data point, from the set of the multiple data points, that is associated with a scheduled vehicular component replacement; assigning a non-failure class label to each data point, from the set of the multiple data points, that is associated with an actively running instance of the vehicular component as yet to be replaced; estimating a failure probability for the vehicular component at each of the multiple data points over a pre-specified future runtime of the vehicular component based on the class label assigned to each of the multiple data points; determining a cumulative hazard function for the vehicular component based on the failure probability, wherein said cumulative hazard function assesses the amount of accumulated risk that the vehicular component has faced from a given start time until the present time; and generating a cumulative wear-based indicator for the vehicular component by executing a regression function at a given time based on (i) the cumulative hazard function, (ii) one or more selected parameters, and (iii) a determination as to whether the vehicular component (a) was previously replaced due to a failure, (b) was previously replaced due to a non-failure scheduled replacement, or (c) is actively running as yet to be replaced; 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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assign a failure class label to each data point, from a set of multiple data points derived from measurements associated with a vehicular component across a fleet of multiple vehicles, that (a) is associated with a failure-caused vehicular component replacement, and (b) is within a pre-specified number of runtime hours of the failure-caused vehicular component replacement; assign a non-failure class label to each data point, from the set of the multiple data points, that (a) is associated with a failure-caused vehicular component replacement, and (b) is not within the pre-specified number of runtime hours of the failure-caused vehicular component replacement; assign a non-failure class label to each data point, from the set of the multiple data points, that is associated with a scheduled vehicular component replacement; assign a non-failure class label to each data point, from the set of the multiple data points, that is associated with an actively running instance of the vehicular component as yet to be replaced; estimate a failure probability for the vehicular component at each of the multiple data points over a pre-specified future runtime of the vehicular component based on the class label assigned to each of the multiple data points; determine a cumulative hazard function for the vehicular component based on the failure probability, wherein said cumulative hazard function assesses the amount of accumulated risk that the vehicular component has faced from a given start time until the present time; and generate a cumulative wear-based indicator for the vehicular component by executing a regression function at a given time based on (i) the cumulative hazard function, (ii) one or more selected parameters, and (iii) a determination as to whether the vehicular component (a) was previously replaced due to a failure, (b) was previously replaced due to a non-failure scheduled replacement, or (c) is actively running as yet to be replaced. - View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
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Specification