Self-adaptive health monitoring systems including networks of tensor networks

Patent metadata

Field	Value
Publication number	US-2025158878-A1
Application number	US-202418941966-A
Country	US
Kind code	A1
Filing date	Nov 8, 2024
Priority date	Nov 10, 2023
Publication date	May 15, 2025
Grant date	—

Abstract

A computer system includes memory hardware configured to store computer-executable instructions, and a network of multiple tensor networks, each tensor network including multiple nodes. The computer system includes processor hardware configured to execute the computer-executable instructions to transmit a ping request from a first tensor network of the multiple tensor networks to a second tensor network of the multiple tensor networks, contract multiple nodes of the second tensor network in response to the ping request, to generate a probability distribution indicative of a state information of the second tensor network, transmit the probability distribution from the second tensor network to the first tensor network, and update the first tensor network to connect the probability distribution with at least one of multiple nodes of the first tensor network.

First claim

What is claimed is:

1 . A computer system comprising:
memory hardware configured to store computer-executable instructions, and a network of multiple tensor networks, each tensor network including multiple nodes; and processor hardware configured to execute the computer-executable instructions to: transmit a ping request from a first tensor network of the multiple tensor networks to a second tensor network of the multiple tensor networks; contract multiple nodes of the second tensor network in response to the ping request, to generate a probability distribution indicative of a state information of the second tensor network; transmit the probability distribution from the second tensor network to the first tensor network; and update the first tensor network to connect the probability distribution with at least one of multiple nodes of the first tensor network.

2 . The computer system of claim 1 , wherein the processor hardware is configured to contract the multiple nodes of the first tensor network and the probability distribution, to generate a contraction result indicative of a state information of the first tensor network based on contextual information of the second tensor network.

3 . The computer system of claim 2 , wherein the processor hardware is configured to:
compare the contraction result to a specified nominal range associated with the first tensor network; and identify a failure condition in response to the contraction result being outside of the specified nominal range associated with the first tensor network.

4 . The computer system of claim 1 , wherein:
the first tensor network includes at least one first physical component node and multiple sensor nodes connected with the at least one first physical component node; and the second tensor network includes at least one second physical component node and multiple sensor nodes connected with the at least one second physical component node.

5 . The computer system of claim 4 , wherein at least one anchor is connected with at least one of the multiple sensor nodes of the first tensor network.

6 . The computer system of claim 4 , wherein at least one external leg is connected with at least one of the multiple sensor nodes of the first tensor network.

7 . The computer system of claim 4 , wherein:
the first physical component node is a pump component; and the second physical component node is a pipe component.

8 . The computer system of claim 7 , wherein:
the multiple nodes of the first tensor network include a pump current sensor node and a pump pressure sensor node; and the multiple nodes of the second tensor network include a pipe pressure sensor node and a pipe flowrate sensor node.

9 . The computer system of claim 8 , wherein:
the processor hardware is configured to identify a failure condition according to the probability distribution; and the failure condition includes at least one of a leak condition and a clog condition.

10 . The computer system of claim 8 , wherein:
the pump component and the pipe component are components of an autonomous ship vessel; and the network of tensor networks is configured to facilitate self-adaptive health monitoring for the autonomous ship vessel.

11 . A method for executing a network of tensor networks, the method comprising:
transmitting a ping request from a first tensor network to a second tensor network, wherein the first tensor network and the second tensor network belong to a network of multiple tensor networks, and each of the multiple tensor networks includes multiple nodes; contracting multiple nodes of the second tensor network in response to the ping request, to generate a probability distribution indicative of a state information of the second tensor network; transmitting the probability distribution from the second tensor network to the first tensor network; and updating the first tensor network to connect the probability distribution with at least one of multiple nodes of the first tensor network.

12 . The method of claim 11 , further comprising contracting the multiple nodes of the first tensor network and the probability distribution, to generate a contraction result indicative of a state information of the first tensor network based on contextual information of the second tensor network.

13 . The method of claim 12 , further comprising:
comparing the contraction result to a specified nominal range associated with the first tensor network; and identifying a failure condition in response to the contraction result being outside of the specified nominal range associated with the first tensor network.

14 . The method of claim 11 , wherein:
the first tensor network includes at least one first physical component node and multiple sensor nodes connected with the at least one first physical component node; and the second tensor network includes at least one second physical component node and multiple sensor nodes connected with the at least one second physical component node.

15 . The method of claim 14 , wherein at least one anchor is connected with at least one of the multiple sensor nodes of the first tensor network.

16 . The method of claim 14 , wherein at least one external leg is connected with at least one of the multiple sensor nodes of the first tensor network.

17 . The method of claim 14 , wherein:
the first physical component node is a pump component; and the second physical component node is a pipe component.

18 . The method of claim 17 , wherein:
the multiple nodes of the first tensor network include a pump current sensor node and a pump pressure sensor node; and the multiple nodes of the second tensor network include a pipe pressure sensor node and a pipe flowrate sensor node.

19 . The method of claim 18 , further comprising identifying a failure condition according to the probability distribution, wherein the failure condition includes at least one of a leak condition and a clog condition.

20 . The method of claim 18 , wherein:
the pump component and the pipe component are components of an autonomous ship vessel; and the network of tensor networks is configured to facilitate self-adaptive health monitoring for the autonomous ship vessel.

Assignees

• Univ Michigan Regents

Inventors

• Singer David
• Manohar Alexander
• Arrigan Connor

Classifications

• H04L43/10

  Active monitoring, e.g. heartbeat, ping or trace-route · CPC title

• H04L67/12

  specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks · CPC title

• H04L41/08 Primary

  Configuration management of networks or network elements (address allocation H04L61/50) · CPC title