Method and system for monitoring a remote system

Patent metadata

Field	Value
Publication number	US-2022012644-A1
Application number	US-201917296302-A
Country	US
Kind code	A1
Filing date	Oct 21, 2019
Priority date	Dec 3, 2018
Publication date	Jan 13, 2022
Grant date	—

Abstract

This disclosure relates to methods and apparatus for monitoring a remote system. In one arrangement, a plurality of measurement data units are obtained. Each measurement data unit represents a time series of measurements made by a sensor system at the remote system. A first trained machine learning model is used to identify a subset of the measurement data units that have a higher average probability of corresponding to an abnormal state of the remote system than the other measurement data units. Data representing the identified measurement data units is sent over a communications network to a central data processing system. An abnormal state of the remote system is detected by using a second trained machine learning model at the central data processing system to process the data representing the identified measurement data units.

First claim

1 . A method of monitoring a remote system, comprising:
obtaining a plurality of measurement data units, each measurement data unit representing a time series of measurements made by a sensor system at the remote system; using a first trained machine learning model to identify a subset of the measurement data units that have a higher average probability of corresponding to an abnormal state of the remote system than the other measurement data units; sending data representing the identified measurement data units over a communications network to a central data processing system; and detecting an abnormal state of the remote system by using a second trained machine learning model at the central data processing system to process the data representing the identified measurement data units.

2 . The method of claim 1 , wherein the first trained machine learning model estimates a probability of the remote system being in the abnormal state during a time period corresponding to each measurement data unit and the identification of the subset of measurement data units comprises identifying measurement data units corresponding to time periods in which the estimated probability is above a predetermined threshold.

3 . The method of claim 2 , wherein the predetermined threshold is dynamically adjusted by the central data processing system via the communications network.

4 . The method of claim 3 , wherein the predetermined threshold is lowered by the central data processing system in response to the second trained machine learning model detecting an increase in a probability of an abnormal state of the remote system.

5 . The method of any of claim 2 , wherein the first trained machine learning model uses logistic regression to estimate the probabilities.

6 . The method of claim 1 , wherein the remote system comprises a mechanical apparatus.

7 . The method of claim 6 , wherein the sensor system comprises an accelerometer.

8 . The method of claim 7 , wherein the accelerometer is attached to a moving part of the mechanical apparatus.

9 . The method of claim 6 , wherein the mechanical apparatus is a hand-operated water pump comprising a movable handle for hand-operating the water pump.

10 . The method of claim 9 , wherein the sensor system comprises an accelerometer configured to measure a component of acceleration of the handle parallel to a longitudinal axis of the handle.

11 . The method of claim 1 , further comprising pre-processing the measurement data units before the first trained machine learning model identifies the subset of the measurement data units.

12 . The method of claim 11 , wherein the pre-processing comprises determining a period of a largest periodic component of the times series of measurements in each measurement data unit, and the pre-processing comprises removing measurement data units in which a period of the determined largest periodic component is below a predetermined threshold period.

13 . The method of claim 12 , wherein the remote system comprises a hand-operated water pump and the predetermined threshold period equals 0.5 s.

14 . The method of claim 11 , wherein the pre-processing comprises applying a high pass filter to each measurement data unit.

15 . The method of claim 11 , wherein the pre-processing comprises transforming the measurement data units to represent the time series of measurements in the frequency domain.

16 . The method of claim 1 , wherein the second trained machine learning model comprises a support vector machine or a random forest classifier model.

17 . The method of claim 1 , wherein the remote system comprises an electrical infrastructure system.

18 . The method of claim 1 , wherein the remote system comprises a biological system.

19 . The method of claim 18 , wherein the biological system is a human or animal and the sensor system is configured to measure one or more parameters relevant to a state of health of the human or animal.

20 . A system for monitoring a remote system, comprising:
a local data acquisition unit comprising a sensor system and a local data processing unit; and a central data processing system;
wherein:
the local data processing unit is configured to:
obtain a plurality of measurement data units, each measurement data unit representing a time series of measurements made by the sensor system at the remote system;
use a first trained machine learning model to identify a subset of the measurement data units that have a higher average probability of corresponding to an abnormal state of the remote system than the other measurement data units; and
send data representing the identified measurement data units over a communications network to the central data processing system; and
the central data processing system is configured to:
detect an abnormal state of the remote system by using a second trained machine learning model to process the data representing the identified measurement data units received from the local data acquisition unit.

Assignees

• Univ Oxford Innovation Ltd

Inventors

• Clifton David
• Thompson Patrick
• Greeff Heloise
• Manandhar Achut

Classifications

• G06N20/20 Primary

  Ensemble learning · CPC title

• G05B23/0235

  based on a comparison with predetermined threshold or range, e.g. "classical methods", carried out during normal operation; threshold adaptation or choice; when or how to compare with the threshold · CPC title

• F04B2201/12

  Parameters of driving or driven means · CPC title

• F04B9/14

  Pumps characterised by muscle-power operation {(hand-held spraying or dispensing apparatus using pumps or bulbs B05B11/00)} · CPC title

• G16H50/30

  for calculating health indices; for individual health risk assessment · CPC title