Crowdsourced failure mode prediction

US10482689B2 · US · B2

Patent metadata
FieldValue
Publication numberUS-10482689-B2
Application numberUS-201615572334-A
CountryUS
Kind codeB2
Filing dateDec 31, 2016
Priority dateDec 31, 2016
Publication dateNov 19, 2019
Grant dateNov 19, 2019

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  1. Title

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  2. Abstract

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  5. First independent claim

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Abstract

Official abstract text for this publication.

In an example, there is disclosed a smart sensor for monitoring a vehicle, including: a sensor array comprising a mechanical input sensor; a network interface; a processor; and one or more logic elements providing a data engine to: collect an vibration input from the mechanical input sensor; and report the data from the mechanical input sensor to a data aggregator via the network interface. There is also disclosed a data aggregation server engine to aggregate and correlate inputs from the smart sensor.

First claim

Opening claim text (preview).

What is claimed is: 1. A fleet health monitoring server, comprising: a network interface configured to communicatively couple the apparatus to a sensor network of mechanical or acoustic sensors attached to vehicles of a fleet of similar vehicles; a data store comprising a sensor input profile for vehicles of the fleet, the input profile comprising an aggregation of crowd-sourced sensor inputs into a normal behavior model of the mechanical or acoustic sensors of the sensor network; and one or more logic elements comprising a data aggregator engine to: receive an input from a mechanical or acoustic sensor of the sensor network; and incorporate the input into an input profile associated with the vehicles of the fleet. 2. The computing apparatus of claim 1 , wherein the vehicles are autonomous vehicles. 3. The computing apparatus of claim 1 , wherein the data aggregator engine is further to make a failure prediction for the vehicles. 4. The computing apparatus of claim 3 , wherein making the failure prediction comprises determining that the input is substantially deviant from the input profile. 5. The computing apparatus of claim 1 , wherein the data aggregator engine is further to create or modify a maintenance schedule for the vehicles based at least in part on the input profile. 6. The computing apparatus of claim 1 , wherein the data aggregator engine is further to create or modify an actuarial database for the vehicles based at least in part on the input profile. 7. The computing apparatus of claim 1 , wherein the data aggregator engine is further to eliminate false positive failure predictions over time. 8. The computing apparatus of claim 1 , wherein the data aggregator engine is further to recommend a design improvement for the vehicles based at least in part on the input profile. 9. The computing apparatus of claim 1 , wherein the data aggregator engine is further to extrapolate a failure prediction for the vehicles based on an input profile associated with a different vehicle model. 10. The computing apparatus of claim 1 , wherein the input is vibration input. 11. The computing apparatus of claim 1 , wherein the input is an acoustic input. 12. The computing apparatus of claim 1 , wherein the input is a joint acoustic/vibration input. 13. One or more tangible, non-transitory computer-readable storage mediums having stored thereon executable instructions to instruct a processor to: access a network interface configured to communicatively couple the processor to a sensor network of mechanical or acoustic sensors attached to vehicles of a fleet of similar vehicles; provide a data store comprising a sensor input profile for vehicles of the fleet, the input profile comprising an aggregation of crowd-sourced sensor inputs into a normal behavior model of the mechanical or acoustic sensors of the sensor network; and provide a data aggregator engine to: receive an input from a mechanical or acoustic sensor of the sensor network; and incorporate the input into an input profile associated with the vehicles of the fleet. 14. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the vehicles are autonomous vehicles. 15. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the data aggregator engine is further to make a failure prediction for the vehicles. 16. The one or more tangible, non-transitory computer-readable storage mediums of claim 15 , wherein making the failure prediction comprises determining that the input is substantially deviant from the input profile. 17. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the data aggregator engine is further to create or modify a maintenance schedule for the vehicles based at least in part on the input profile. 18. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the data aggregator engine is further to create or modify an actuarial database for the vehicles based at least in part on the input profile. 19. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the data aggregator engine is further to eliminate false positive failure predictions over time. 20. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the data aggregator engine is further to recommend a design improvement for the vehicles based at least in part on the input profile. 21. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the data aggregator engine is further to extrapolate a failure prediction for the vehicles based on an input profile associated with a different vehicle model. 22. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the input is vibration input. 23. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the input is an acoustic input. 24. The one or more tangible, non-transitory computer-readable storage mediums of claim 13 , wherein the input is a joint acoustic/vibration input. 25. A computer-implemented method, comprising: accessing a network interface configured to communicatively couple an apparatus to a sensor network of mechanical or acoustic sensors attached to vehicles of a fleet of similar vehicles; providing a data store comprising a sensor input profile for vehicles of the fleet, the input profile comprising an aggregation of crowd-sourced sensor inputs into a normal behavior model of the mechanical or acoustic sensors of the sensor network; and receiving an input from a mechanical or acoustic sensor of the sensor network; and incorporating the input into an input profile associated with the vehicles of the fleet.

Assignees

Inventors

Classifications

  • Indicating maintenance · CPC title

  • G07C5/085Primary

    using electronic data carriers · CPC title

  • communicating information to a remotely located station (transmission systems for measured values G08C) · CPC title

  • Diagnosing performance data (testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005) · CPC title

  • Wheeled or endless-tracked vehicles (G01M17/08 takes precedence) · CPC title

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What does patent US10482689B2 cover?
In an example, there is disclosed a smart sensor for monitoring a vehicle, including: a sensor array comprising a mechanical input sensor; a network interface; a processor; and one or more logic elements providing a data engine to: collect an vibration input from the mechanical input sensor; and report the data from the mechanical input sensor to a data aggregator via the network interface. The…
Who is the assignee on this patent?
Intel Corp
What technology area does this patent fall under?
Primary CPC classification G07C5/085. Mapped technology areas include Physics.
When was this patent published?
Publication date Tue Nov 19 2019 00:00:00 GMT+0000 (Coordinated Universal Time) (B2). Legal status and post-grant events are not shown on this page.
What related patents are in patentsdb?
We list 10 related publications on this page (citations in our corpus or others sharing the same primary CPC).