Granted Patent B2

US 12,682,303 · App. 17/560,921 · Granted Jul 14, 2026

Inventors: Kai Hencken (Lörrach, DE); Massimo Scarpellini (Dalmine, IT); Marco Testa (Romano di Lombardia, IT)

Assignee: ABB Schweiz AG

G06Q10/0635G06F30/20G06F2111/08G06F2119/04

Abstract

Claims (96)

1 . A method for device monitoring, comprising:

a) providing a plurality of fleet measurement data for at least one device, wherein the plurality of fleet measurement data comprises a number of fleet device data measurements “Nfleet”;

b) providing a plurality of device measurement data for a monitored device, wherein the plurality of device measurement data comprises a number of monitor device data measurements “Ndata”;

c) determining a fleet distribution “Fdist” determined as a distribution of differences between consecutive measurements of the plurality of fleet measurement data;

d) determining a device distribution “Ddist” determined as a distribution of differences between consecutive measurements of the device measurement data;

e) determining an effective number of data measurements “Neff”, “Neff” being a parameter to weight “Fdist” relative to “Ddist”; and

f) determining a probability of failure of the monitored device, the determining comprising utilising the fleet distribution “Fdist,” the device distribution “Ddist,” the number of monitor device data measurements “Ndata,” and the effective number of data measurements “Neff”;

scheduling maintenance for the monitored device based at least in part on the probability of failure;

wherein step f) comprises:

A1) taking a start measurement datum from the plurality of device measurement data;

A2) selecting either the fleet distribution “Fdist” or the device distribution “Ddist”, comprising utilization of at least one probability of selection;

A3) selecting a data value from the distribution selected in step A2);

A4) determining a calculated device measurement value determined as the data value selected in step A3) added to the start measurement value for a first iteration or determined as the data value selected in step A3) added to an existing calculated device measurement value for iterations after the first iteration; and

A5) iterating steps A2) to A4) until after a number of iterations the calculated device measurement value reaches a threshold failure value indicative of a failure limit of the device measurement value, and wherein determination of the probability of failure comprises utilizing the number of iterations to reach the threshold failure value.

2 . The method according to claim 1 , wherein:

in step a) the plurality of fleet measurement data are for a plurality of devices, and

in step c) determination of the fleet distribution “Fdist” comprises determining differences between consecutive measurements for each of the devices.

3 . The method according to claim 1 , wherein in step e) determining the effective number of data measurements “Neff” comprises utilizing the fleet distribution “Fdist” and/or the device distribution “Ddist”.

4 . The method according to claim 1 , wherein in step e) the effective number of data measurements “Neff” is determined as a fixed value globally.

5 . The method according to claim 1 , wherein step f) further comprises:

A6) carrying out step A5) a plurality of times, and wherein determination of the probability of failure comprises utilizing the number of iterations to reach the threshold failure value for each of the plurality of times step A5) was carried out.

6 . The method according to claim 1 , wherein:

in step A2) a probability of selecting the fleet distribution “Fdist” is calculated on a basis of the effective number of data measurements divided by the sum of the effective number of data measurements and the number of monitor device data measurements “Neff/(Neff+Ndata)”, and

in step A2) a probability of selecting the device distribution “Ddist” is calculated on a basis of the number of monitor device data measurements divided by the sum of the effective number of data measurements and the number of monitor device data measurements “Ndata/(Neff+Ndata)”.

7 . The method according to claim 1 , wherein step A3) comprises randomly selecting the data value from the distribution selected in step A2).

8 . The method according to claim 1 , wherein step f) comprises:

B1) determining a combined distribution utilizing the fleet distribution “Fdist” and the device distribution “Ddist”.

9 . The method according to claim 8 , wherein data values in the combined distribution are determined by summing a product of the effective number of data measurements multiplied by the fleet distribution and a product of the number of monitor device data measurements multiplied by the device distribution, and the sum divided by a sum of the effective number of data measurements and the number of monitor device data measurements “(Neff*Fdist+Ndata*Ddist)/(Neff+Ndata)”.

10 . The method according to claim 8 , wherein data values in the combined distribution are determined by applying statistical inference, where the effective number of data measurements “Neff” and the number of monitor device data measurements “Ndata” are integral parts of the inference.

11 . An apparatus for device monitoring, wherein the apparatus is configured to carry out the method of claim 1 .

12 . A computer program element for controlling an apparatus that is configured to carry out the method of claim 1 , which when executed by a processor is configured to carry out the method of claim 1 .

13 . A non-transitory computer readable medium having stored the computer program element according to claim 12 .

14 . A method for device monitoring, comprising:

a) providing a plurality of fleet measurement data for at least one device, wherein the plurality of fleet measurement data comprises a number of fleet device data measurements “Nfleet”;

b) providing a plurality of device measurement data for a monitored device, wherein the plurality of device measurement data comprises a number of monitor device data measurements “Ndata”;

c) determining a fleet distribution “Fdist” determined as a distribution of differences between consecutive measurements of the plurality of fleet measurement data;

d) determining a device distribution “Ddist” determined as a distribution of differences between consecutive measurements of the device measurement data;

e) determining an effective number of data measurements “Neff”, “Neff” being a parameter to weight “Fdist” relative to “Ddist”; and

f) determining a probability of failure of the monitored device, the determining comprising utilising the fleet distribution “Fdist,” the device distribution “Ddist,” the number of monitor device data measurements “Ndata,” and the effective number of data measurements ‘Neff’;

scheduling maintenance for the monitored device based at least in part on the probability of failure;

wherein step f) comprises:

X1) taking a start measurement datum from the plurality of device measurement data;

X2) selecting either the fleet distribution “Fdist” or the device distribution “Ddist”, comprising utilization of at least one probability of selection; and

X3) selecting a data value from the distribution selected in step X2),

wherein the probability of failure of the monitored device is determined from a statistical simulation technique starting from the value selected in step X1) and using consecutive values selected in a plurality of steps X3);

in step X2) a probability of selecting the fleet distribution “Fdist” is calculated on a basis of the effective number of data measurements divided by the sum of the effective number of data measurements and the number of monitor device data measurements “Neff/(Neff+Ndata)”, and

in step A2) a probability of selecting the device distribution “Ddist” is calculated on a basis of the number of monitor device data measurements divided by the sum of the effective number of data measurements and the number of monitor device data measurements “Ndata/(Neff+Ndata)”.

15 . The method according to claim 14 , wherein the statistical simulation technique is a Monte Carlo method involving the simulation of a large number of possible future paths.

16 . The method according to claim 14 , wherein step X3) comprises randomly selecting the data value from the distribution selected in step X2).

17 . A method for device monitoring, comprising:

a) providing a plurality of fleet measurement data for at least one device, wherein the plurality of fleet measurement data comprises a number of fleet device data measurements “Nfleet”;

b) providing a plurality of device measurement data for a monitored device, wherein the plurality of device measurement data comprises a number of monitor device data measurements “Ndata”;

c) determining a fleet distribution “Fdist” determined as a distribution of differences between consecutive measurements of the plurality of fleet measurement data;

d) determining a device distribution “Ddist” determined as a distribution of differences between consecutive measurements of the device measurement data;

e) determining an effective number of data measurements “Neff”, “Neff” being a parameter to weight “Fdist” relative to “Ddist”; and

f) determining a probability of failure of the monitored device, the determining comprising utilising the fleet distribution “Fdist,” the device distribution “Ddist,” the number of monitor device data measurements “Ndata,” and the effective number of data measurements “Neff”;

scheduling maintenance for the monitored device based at least in part on the probability of failure;

wherein step f) comprises:

Y1) taking a start measurement datum from the plurality of device measurement data; and

Y2) selecting either the fleet distribution “Fdist” or the device distribution “Ddist”, comprising utilization of at least one probability of selection,

wherein the probability of failure of the monitored device is determined comprising utilization of a mathematically known first-passage distribution for the initial value determined in Y1) and the distribution determined in Y2);

in step Y2) a probability of selecting the fleet distribution “Fdist” is calculated on a basis of the effective number of data measurements divided by the sum of the effective number of data measurements and the number of monitor device data measurements “Neff/(Neff+Ndata)”, and

in step Y2) a probability of selecting the device distribution “Ddist” is calculated on a basis of the number of monitor device data measurements divided by the sum of the effective number of data measurements and the number of monitor device data measurements “Ndata/(Neff+Ndata)”.

18 . A method for device monitoring, comprising:

a) providing a plurality of fleet measurement data for at least one device, wherein the plurality of fleet measurement data comprises a number of fleet device data measurements “Nfleet”;

b) providing a plurality of device measurement data for a monitored device, wherein the plurality of device measurement data comprises a number of monitor device data measurements “Ndata”;

c) determining a fleet distribution “Fdist” determined as a distribution of differences between consecutive measurements of the plurality of fleet measurement data;

d) determining a device distribution “Ddist” determined as a distribution of differences between consecutive measurements of the device measurement data;

e) determining an effective number of data measurements “Neff”, “Neff” being a parameter to weight “Fdist” relative to “Ddist”; and

f) determining a probability of failure of the monitored device, the determining comprising utilising the fleet distribution “Fdist,” the device distribution “Ddist,” the number of monitor device data measurements “Ndata,” and the effective number of data measurements “Neff”;

scheduling maintenance for the monitored device based at least in part on the probability of failure;

wherein step f) comprises:

B1) determining a combined distribution utilizing the fleet distribution “Fdist” and the device distribution “Ddist”;

B2) taking a start measurement datum from the plurality of device measurement data;

B3) selecting a data value from the combined distribution determined in step B1);

B4) determining a calculated device measurement value determined as the data value selected in step B3) added to the start measurement value for a first iteration or determined as the data value selected in step B3) added to an existing calculated device measurement value for iterations after the first iteration; and

B5) iterating steps B3) to B4) until after a number of iterations the calculated device measurement value reaches a threshold failure value indicative of a failure limit of the device measurement value, and wherein determination of the probability of failure comprises utilizing the number of iterations to reach the threshold failure value.

19 . The method according to claim 18 , wherein step f) further comprises:

B6) carrying out step B5) a plurality of times, and wherein determination of the probability of failure comprises utilizing the number of iterations to reach the threshold failure value for each of the plurality of times step B5) was carried out.

20 . The method according to claim 18 , wherein step B3) comprises randomly selecting the data value from the combined distribution.

21 . A method for device monitoring, comprising:

a) providing a plurality of fleet measurement data for at least one device, wherein the plurality of fleet measurement data comprises a number of fleet device data measurements “Nfleet”;

b) providing a plurality of device measurement data for a monitored device, wherein the plurality of device measurement data comprises a number of monitor device data measurements “Ndata”;

c) determining a fleet distribution “Fdist” determined as a distribution of differences between consecutive measurements of the plurality of fleet measurement data;

d) determining a device distribution “Ddist” determined as a distribution of differences between consecutive measurements of the device measurement data;

e) determining an effective number of data measurements “Neff”, “Neff” being a parameter to weight “Fdist” relative to “Ddist”; and

f) determining a probability of failure of the monitored device, the determining comprising utilising the fleet distribution “Fdist,” the device distribution “Ddist,” the number of monitor device data measurements “Ndata,” and the effective number of data measurements “Neff”;

scheduling maintenance for the monitored device based at least in part on the probability of failure;

wherein step f) comprises:

D1) determining a first probability of failure “Prob1” comprising utilisation of the fleet distribution “Fdist”;

D2) determining a second probability of failure “Prob2” comprising utilization of the device distribution “Ddist”; and

D3) determining the failure of the monitored device comprising utilization of the first probability of failure “Prob1” and the second probability of failure “Prob2”;

wherein step D3) comprises determining a combined probability by summing a product of the effective number of data measurements multiplied by the first probability of failure and a product of the number of monitor device data measurements multiplied by the second probability of failure, and the sum divided by a sum of the effective number of data measurements and the number of monitor device data measurements “(Neff Prob1+Ndata*Prob2)/(Neff+Ndata)”.

22 . The method according to claim 21 , wherein:

step D1) comprises modifying a parameter of a synthetic distribution until the synthetic distribution statistically represents the fleet distribution “Fdist”, and

step D2) comprises modifying a parameter of a synthetic distribution until the synthetic distribution statistically represents the device distribution “Ddist”.

Assignments (1)

Priority Claims (1)

Continuity (2)

References Cited (17)