Mesh network routing based on availability of assets

• US 9,842,034 B2
• Filed: 09/14/2015
• Issued: 12/12/2017
• Est. Priority Date: 12/01/2014
• Status: Active Grant

• Current Assignee: Uptake Technologies, Inc.
• Original Assignee: Uptake Technologies, Inc.
• Inventors: Heliker, Brett, Nicholas, Brad
• Primary Examiner(s): Chou, Albert T
• Application Number:
  US14/853,189
• Publication Number:
  US 20170075778A1
• Time in Patent Office: 820 Days
• CPC Class Codes: G01D 3/08 with provision for safeguarding the apparatus, e.g. against abnormal operation, against breakdown
• CPC Class Codes: G01M 99/005 Testing of complete machines, e.g. washing-machines or mobile phones testing of machine parts G01M13/00; testing of electric apparatus or components G01R31/50
• CPC Class Codes: G01M 99/008 by doing functionality tests
• CPC Class Codes: G05B 19/18 Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form G05B19/418 takes precedence
• CPC Class Codes: G05B 23/024 Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
• CPC Class Codes: G05B 23/0254 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
• CPC Class Codes: G05B 23/0275 Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
• CPC Class Codes: G06F 11/008 Reliability or availability analysis
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0721 within a central processing unit [CPU]
• CPC Class Codes: G06F 11/0751 Error or fault detection not based on redundancy power supply failures G06F1/30; network fault management H04L41/06
• CPC Class Codes: G06F 11/0754 by exceeding limits
• CPC Class Codes: G06F 11/0772 Means for error signaling, e.g. using interrupts, exception flags, dedicated error registers
• CPC Class Codes: G06F 11/0787 Storage of error reports, e.g. persistent data storage, storage using memory protection
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 11/0793 Remedial or corrective actions recovery from an exception in an instruction pipeline G06F9/3861; by retry G06F11/1402; for recovering from a failure of a protocol instance or entity H04L69/40
• CPC Class Codes: G06F 11/2007 using redundant communication media
• CPC Class Codes: G06F 11/26 Functional testing
• CPC Class Codes: G06F 11/263 Generation of test inputs, e.g. test vectors, patterns or sequences ; with adaptation of the tested hardware for testability with external testers
• CPC Class Codes: G06F 2201/85 Active fault masking without idle spares active fault masking without idle spare hardware where processing functionality is redundant G06F11/2035
• CPC Class Codes: G06N 5/02 : Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/04 : Inference or reasoning models
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/04 : Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06312 : Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/0633 : Workflow analysis
• CPC Class Codes: G06Q 10/067 : Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/20 : Administration of product repair or maintenance
• CPC Class Codes: G06Q 50/04 : Manufacturing
• CPC Class Codes: G06Q 50/08 : Construction
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G07C 5/0808 : Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005
• CPC Class Codes: G07C 5/0825 : using optical means
• CPC Class Codes: G08B 21/18 : Status alarms G08B21/02 takes precedence
• CPC Class Codes: H04L 45/22 : Alternate routing
• CPC Class Codes: Y02P 90/30 : Computing systems specially adapted for manufacturing

Abstract

Disclosed herein are systems, devices, and methods related to assets and predictive models and corresponding workflows that are related to updating a routing table. In particular, examples involve based on a predictive model, determining that a given asset of a plurality of assets in a mesh network is likely to be unavailable within a given period of time in the future and in response to the determining, causing a routing configuration for at least one other asset in the mesh network to be updated.

Claims

1. A computer-implemented method comprising:

receiving, by an analytics system from a mesh network comprising a plurality of assets at a remote location, respective operating data for each asset in the mesh network that is indicative of the operating conditions of the asset, wherein each asset is equipped with a respective set of sensors for monitoring the operating conditions of the asset;

for each asset in the mesh network, executing a respective predictive model that takes the respective operating data for the asset as inputs and outputs a respective likelihood value for the asset indicating a likelihood that the asset will become unavailable within a given period of time in the future;

while executing the respective predictive model for each asset in the mesh network, determining that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that the given asset is likely to be unavailable within the given period of time in the future;

in response to the determining, communicating with the mesh network to cause a routing configuration for at least one other asset in the mesh network to be updated such that the given asset is removed from the routing configuration.

2. The computer-implemented method of claim 1,

wherein receiving the respective operating data for each asset in the mesh network comprises receiving respective sensor data for each asset in the mesh network;

wherein executing the respective predictive model for each asset in the mesh network comprises executing a health score model that takes the respective sensor data for the asset as inputs and outputs a respective health score for the asset indicating a likelihood that an operational failure will occur at the asset within the given period of time in the future; and

wherein determining that the respective likelihood value for the given asset has met threshold criteria indicating that the given asset is likely to be unavailable within the given period of time in the future comprises determining that the respective health score for the given asset has met threshold criteria indicating that the given asset is likely to have an operational failure within the given period of time in the future.

3. The computer-implemented method of claim 2, wherein determining that the respective health score for the given asset has met the threshold criteria comprises determining that the respective health score for the given asset has exceeded a health score threshold value for a certain period of time.

4. The computer-implemented method of claim 1, wherein determining that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that the given asset is likely to become unavailable within the given period of time in the future comprises determining that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that determining that the given asset is likely to become unavailable due to at least one of a failure at the given asset, the given asset going off-line, a scheduled down time for the given asset, or the given asset being unable to communicate over the mesh network.

5. The computer-implemented method of claim 1, wherein the plurality of assets comprises at least one of a transportation machine, an industrial machine, a medical machine, or a utility machine.

6. The computer-implemented method of claim 1, wherein communicating with the mesh network to cause the routing configuration for at least one other asset in the mesh network to be updated such that the given asset is removed from the routing configuration comprises:

transmitting an indication that the given asset is likely to become unavailable within the given period of time in the future to one of the plurality of assets in the mesh network and thereby causing the at least one other asset in the mesh network to remove the given asset from its routing configuration.

7. The computer-implemented method of claim 1, wherein communicating with the mesh network to cause the routing configuration for at least one other asset in the mesh network to be updated such that the given asset is removed from the routing configuration comprises:

updating the routing configuration for the at least one other asset in the mesh network by removing the given asset from the routing configuration; and

transmitting the updated routing configuration to the mesh network.

8. A non-transitory computer-readable medium having instructions stored thereon that are executable to cause a computing system to:

receive, from a mesh network comprising a plurality of assets at a remote location, respective operating data for each asset in the mesh network that is indicative of the operating conditions of the asset, wherein each asset is equipped with a respective set of sensors for monitoring the operating conditions of the asset;

for each asset in the mesh network, execute a respective predictive model that takes the respective operating data for the asset as inputs and outputs a respective likelihood value for the asset indicating a likelihood that the asset will become unavailable within a given period of time in the future;

while executing the respective predictive model for each asset in the mesh network, determine that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that the given asset is likely to be unavailable within the given period of time in the future; and

in response to the determining, communicate with the mesh network to cause a routing configuration for at least one other asset in the mesh network to be updated such that the given asset is removed from the routing configuration.

9. The computer-readable medium of claim 8,

wherein receiving the respective operating data for each asset in the mesh network comprises receiving respective sensor data for each asset in the mesh network;

wherein executing a respective predictive model for each asset in the mesh network comprises executing a health score model that takes the respective sensor data for the asset as inputs and outputs a respective health score for the asset indicating a likelihood that an operational failure will occur at the asset within the given period of time in the future; and

wherein determining that the respective likelihood value for the given asset has met threshold criteria indicating that the given asset is likely to be unavailable within the given period of time in the future comprises determining that the respective health score for the given asset has met threshold criteria indicating that the given asset is likely to have an operational failure within the given period of time in the future.

10. The computer-readable medium of claim 9, wherein determining that the respective health score for the given asset has met the threshold criteria comprises determining that the respective health score for the given asset has exceeded a health score threshold value for a certain period of time.

11. The computer-readable medium of claim 8, wherein determining that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that the given asset is likely to become unavailable within the given period of time in the future comprises determining that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that the given asset is likely to become unavailable due to at least one of a failure at the given asset, the given asset going off-line, a scheduled down time for the given asset, or the given asset being unable to communicate over the mesh network.

12. The computer-readable medium of claim 8, wherein the plurality of assets comprises at least one of a transportation machine, an industrial machine, a medical machine, or a utility machine.

13. The computer-readable medium of claim 8, wherein communicating with the mesh network to cause the routing configuration for at least one other asset in the mesh network to be updated comprises:

transmitting an indication that the given asset is likely to become unavailable within the given period of time in the future to the mesh network and thereby causing the at least one other asset in the mesh network to remove the given asset from its routing configuration.

14. The computer-readable medium of claim 8, wherein communicating with the mesh network to cause the routing configuration for at least one other asset in the mesh network to be updated such that the given asset is removed from the routing configuration comprises:

updating the routing configuration for the at least one other asset in the mesh network by removing the given asset from the routing configuration; and

transmitting a determination that the given asset is likely to be unavailable within the given period of time in the future to at least one other asset in the mesh network the updated routing configuration to the mesh network.

15. A computing system comprising:

at least one processor;

a non-transitory computer-readable medium; and

program instructions stored on the non-transitory computer-readable medium that are executable by the at least one processor to cause the computing system to;

receive, from a mesh network comprising a plurality of assets at a remote location, respective operating data for each asset in the mesh network that is indicative of the operating conditions of the asset, wherein each asset is equipped with a respective set of sensors for monitoring the operating conditions of the asset;

for each asset in the mesh network, execute a respective predictive model that takes the respective operating data for the asset as inputs and outputs a respective likelihood value for the asset indicating a likelihood that the asset will become unavailable within a given period of time in the future;

while executing the respective predictive model for each asset in the mesh network, determine that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that the given asset is likely to be unavailable within the given period of time in the future; and

in response to the determining, communicate with the mesh network to cause a routing configuration for at least one other asset in the mesh network to be updated such that the given asset is removed from the routing configuration.

16. The computing system of claim 15,

wherein receiving the respective operating data for each asset in the mesh network comprises receiving respective sensor data for each asset in the mesh network;

wherein executing a respective predictive model for each asset in the mesh network comprises executing a health score model that takes the respective sensor data for the asset as inputs and outputs a respective health score for the asset indicating a likelihood that an operational failure will occur at the asset within the given period of time in the future; and

wherein determining that the respective likelihood value for the given asset has met threshold criteria indicating that the given asset is likely to be unavailable within the given period of time in the future comprises determining that the respective health score for the given asset has met threshold criteria indicating that the given asset is likely to have an operational failure within the given period of time in the future.

17. The computing system of claim 16, wherein determining that the respective health score for the given asset has met the threshold criteria comprises determining that the respective health score for the given asset has exceeded a health score threshold value for a certain period of time.

18. The computing system of claim 15, wherein determining that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that the given asset is likely to become unavailable within the given period of time in the future comprises determining that the respective likelihood value for a given asset of the plurality of assets in the mesh network has met threshold criteria indicating that the given asset is likely to become unavailable due to at least one of a failure at the given asset, the given asset going off-line, a scheduled down time for the given asset, or the given asset being unable to communicate over the mesh network.

19. The computing system of claim 15, wherein the plurality of assets include at least one of a transportation machine, an industrial machine, a medical machine, or a utility machine.

20. The computing system of claim 15, wherein communicating with the mesh network to cause the routing configuration for at least one other asset in the mesh network to be updated comprises:

transmitting an indication that the given asset is likely to become unavailable within the given period of time in the future to the mesh network and thereby causing the at least one other asset in the mesh network to remove the given asset from its routing configuration.