Systems and method for management and allocation of network assets

Patent metadata

Field	Value
Publication number	US-11209808-B2
Application number	US-201916418183-A
Country	US
Kind code	B2
Filing date	May 21, 2019
Priority date	May 21, 2019
Publication date	Dec 28, 2021
Grant date	Dec 28, 2021

Abstract

A method for generating a multi-layer predictive model includes collecting historical observable data from one or more pieces of equipment of a same type, wherein the historical observable data is collected at different hierarchical levels of the one or more pieces of equipment; collecting operational state indications of the pieces of equipment corresponding to the collected historical observable data; generating, from the collected historical observable data, a set of operational state models, wherein each operational state model corresponds to one of the different hierarchical levels; and generating, from outputs of the set of operational state models, a top-level operational model for the piece of equipment. The top-level operational model is operable to determine maintenance and replacement timing for the piece of equipment.

First claim

What is claimed is:

1. A method for generating a multi-layer predictive model, the method comprising:
collecting historical observable data from one or more pieces of equipment of a same type, wherein the historical observable data is collected at different hierarchical levels of the one or more pieces of equipment, wherein the different hierarchical levels comprise a component level, a circuit level, and a logical path level, wherein first historical observable data of the historical observable data pertains to the component level and includes temperature data, vibration data, and friction data, wherein the first historical observable data pertains to an integrated circuit, a capacitor, and a resistor of the one or more pieces of equipment, wherein second historical observable data of the historical observable data pertains to the circuit level and includes input voltage data and current data, and wherein third historical observable data of the historical observable data pertains to the logical path level and includes operational hours data;
collecting operational state indications of the one or more pieces of equipment corresponding to the collected historical observable data;
generating, from the collected historical observable data and the collected operational state indications, a set of operational state models, wherein each operational state model corresponds to one of the different hierarchical levels; and
generating, from outputs of the set of operational state models, a top-level operational model operable to determine maintenance and replacement timing for the one or more pieces of equipment.

2. The method of claim 1 , wherein the operational state indications comprise an operational state indication, a degraded state indication, and a failed state indication.

3. The method of claim 1 , further comprising:
collecting the historical observable data asynchronously between the different hierarchical levels; and
in response to collecting the historical observable data asynchronously:
generating a first covariance matrix between outputs of a first hierarchical level operational state model and outputs of a second hierarchical level operational state model;
generating a second covariance matrix between the outputs of the second hierarchical level operational state model and outputs of a third hierarchical level operational state model; and
generating the top-level operational model using the first covariance matrix and the second covariance matrix as input.

4. The method of claim 3 , further comprising temporally aligning the asynchronously collected historical observable data between the different hierarchical levels.

5. The method of claim 3 , wherein each of the first hierarchical level operational state model, the second hierarchical level operational state model, and the third hierarchical level operational state model, outputs a single state probability estimate for a sequence of input observable data.

6. The method of claim 3 , wherein the operational state indications are correlated to the asynchronously collected historical observable data for one of the different hierarchical levels.

7. The method of claim 3 , further comprising:
generating a top-level model output based on a product of a highest probability estimation state from each of the first covariance matrix and the second covariance matrix,
wherein the top-level model output is a probability estimate of a next operational state or a mean time between failure (MTBF) for the one or more pieces of equipment.

8. The method of claim 1 , further comprising:
collecting the historical observable data synchronously between the different hierarchical levels; and
in response to collecting the historical observable data synchronously:
generating the top-level operational model using a first hierarchical level operational state model, a second hierarchical level operational state model, and a third hierarchical level operational state model.

9. The method of claim 8 , further comprising generating a top-level model output based on a product of probability estimation states from outputs of each of the first hierarchical level operational state model, the second hierarchical level operational state model, and the third hierarchical level operational state model.

10. The method of claim 1 , wherein:
each operational state model of the set of operational state models is a machine learning model trained with the historical observable data collected from corresponding hierarchical levels; and
the top-level operational model is a machine learning model trained with outputs of the set of operational state models and corresponding operational state indications.

11. A computer-implemented method for estimating a next operational state of a piece of equipment, the computer-implemented method comprising:
collecting observable data from the piece of equipment, wherein the observable data is collected at different hierarchical levels of the piece of equipment, wherein the different hierarchical levels comprise a component level, a circuit level, and a logical path level, wherein first observable data of the observable data pertains to the component level and includes temperature data, vibration data, and friction data, wherein the first observable data pertains to an integrated circuit, a capacitor, and a resistor of the piece of equipment, wherein second observable data of the observable data pertains to the circuit level and includes input voltage data and current data, and wherein third observable data of the observable data pertains to the logical path level and includes operational hours data;
inputting the collected observable data to a predictive model at a set of operational state models corresponding to the different hierarchical levels;
generating an output from each operational state model of the set of operational state models, the output being a state probability estimate for each of the different hierarchical levels; and
generating, from a top-level operational model, an output based on the outputs of the set of operational state models, wherein the output from the top-level operational model is a probability estimate of a next operational state or a mean time between failure (MTBF) for the piece of equipment.

12. The computer-implemented method of claim 11 , wherein the next operational state comprises an operational state, a degraded state, or a failed state.

13. The computer-implemented method of claim 11 , further comprising:
determining that the observable data is collected asynchronously between the different hierarchical levels; and
in response to determining that the observable data is collected asynchronously:
generating a first covariance matrix between outputs of a first hierarchical level operational state model and outputs of a second hierarchical level operational state model;
generating a second covariance matrix between the outputs of the second hierarchical level operational state model and outputs of a third hierarchical level operational state model; and
generating the output of the top-level operational model based on a product of a highest probability estimation state from each of the first covariance matrix and the second covariance matrix, wherein the output of the top-level operational model is a probability estimate of a next operational state or a mean time between failure (MTBF) for the piece of equipment.

14. The computer-implemented method of claim 11 , further comprising:
determining that the observable data is collected synchronously between the different hierarchical levels; and
in response to determining that the

Assignees

• At & T Ip I Lp

Inventors

• Mappus Rudolph
• Morris Mark

Classifications

• G06F17/16

  Matrix or vector computation {, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization (matrix transposition G06F7/78)} · CPC title

• G06F17/18

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G05B23/0283

  Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] (preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06) · CPC title

• G05B23/0254

  based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks · CPC title

• G05B23/024 Primary

  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 title