Apparatus for prediction of the residual lifetime of an electrical system

Patent metadata

Field	Value
Publication number	US-11520324-B2
Application number	US-201916511018-A
Country	US
Kind code	B2
Filing date	Jul 15, 2019
Priority date	Jul 16, 2018
Publication date	Dec 6, 2022
Grant date	Dec 6, 2022

Abstract

An apparatus for prediction of the residual lifetime of an electrical system includes: an input unit; a processing unit; and an output unit. The input unit provides at least one sensor data from at least one sensor to the processing unit, the at least one sensor data including a measurement of at least one physical parameter of at least one component of an electrical system. Each sensor data is associated with a corresponding sensor and relates to a measurement of one physical parameter of a corresponding component of the electrical system. Each sensor data extends over a plurality of time windows and is assigned to a certain data class, such that sensor data at a particular time window has a value that falls into one of the data classes. For each sensor data the processing unit assigns the sensor data at each time window into a corresponding measurement window.

First claim

What is claimed is:

1. A prediction system for prediction of the residual lifetime of an electrical system, the prediction system comprising:
a sensor; and
a processor,
wherein the sensor is configured to receive sensor data comprising a measurement of a physical parameter of a component of an electrical system,
wherein the sensor data is associated with the sensor and relates to the measurement of the physical parameter of the component of the electrical system,
wherein the sensor data extends over a plurality of time windows and the sensor data at each of the plurality of time windows is assigned to a certain data class of a plurality of data classes, such that the sensor data at each of the plurality of time windows has a value that falls into one of the plurality of data classes and wherein the plurality of data classes encompasses a data range within which the sensor data can vary,
wherein for the sensor data the processor is configured to assign the sensor data at each of the plurality of time windows into a corresponding one of the plurality of data classes on a basis that the sensor data at a particular one of the plurality of time windows has a value that falls into the corresponding one of the plurality of data classes,
wherein for the physical parameter of the component, the processor is configured to determine a load spectrum, the load spectrum comprising a sum of all of the plurality of data classes including data values and duration in each of the plurality of data classes for the physical parameter of the component,
wherein the processor is configured to determine the total duration of the sensor data with each of the plurality of data classes by summing up the plurality of time windows of the sensor data within each of the plurality of data classes,
wherein the processor is configured to determine a predicted lifetime for the component comprising the application of an aging model to the load spectrum for the physical parameter of the component, and
wherein the processor is further configured to continuously update a planning of system maintenance for the electrical system based on the predicted lifetime for the component.

2. The prediction system according to claim 1 , wherein the processor is configured to determine a predicted residual lifetime of the electrical system comprising a determination of the component from a plurality of components that has a shortest predicted lifetime.

3. The prediction system according to claim 1 , wherein the determination of the predicted lifetime of the component comprises utilization of at least one safety margin.

4. The prediction system according to claim 3 , comprising a plurality of components wherein each of the plurality of components has a different safety margin of the at least one safety margin.

5. The prediction system according to claim 4 , wherein the processor is configured to determine a predicted lifetime for each component of the plurality of components.

6. The prediction system according to claim 1 , wherein the physical parameter comprises one or more of temperature, humidity, electrical current, electrical resistance, vibration, acceleration, or reaction time.

7. The prediction system according to claim 1 , wherein the sensor data comprises one or more of temperature measurement data, humidity measurement data, electrical current measurement data, electrical resistance measurement data, vibration measurement data, acceleration measurement data, or reaction time measurement data.

8. The prediction system according to claim 1 , wherein the sensor comprises one or more of a temperature sensor, humidity sensor, electrical current sensor, electrical resistance sensor, vibration sensor, accelerometer, or reaction time sensor.

9. The prediction system according to claim 1 , wherein the load spectrum for the physical parameter of the component defines a percentage of time within one of the plurality of data classes.

10. The prediction system according to claim 1 , wherein, for the physical parameter of the component, the processor is configured to determine a plurality of rates of change of the sensor data,
wherein each time window of the plurality of time windows has an associated rate of change of sensor data assigned to it, and
wherein determination of the predicted lifetime for the component comprises utilization of the plurality of rates of change of sensor data for the physical parameter of the component.

11. The prediction system according to claim 1 , wherein the aging model comprises one or more of Palmgren-Miner's rule, Arrhenius, Coffin-Manson, or Eyring.

12. The prediction system according to claim 1 , comprising measurement of a plurality of physical parameters of the component, wherein the aging model used for one of the plurality of physical parameters of the component is different from the aging model used for a different one of the plurality of physical parameters of the component.

13. The prediction system according to claim 1 , wherein the component comprises a busbar.

14. The prediction system according to claim 1 , wherein the electrical system comprises a switchgear or circuit breaker.

15. The prediction system according to claim 1 , wherein the processor is further configured to indicate a service requirement by the electrical system, when the processor determines that the component has met or exceeded the predicted lifetime.

16. A method for prediction of a residual lifetime of an electrical system, comprising:
a) providing sensor data from a sensor to a processor, the sensor data comprising a measurement of a physical parameter of a component of an electrical system, the sensor data being associated with the sensor and relating to the measurement of the physical parameter of the component of the electrical system, the sensor data extending over a plurality of time windows and the sensor data at each of the plurality of time windows being assigned to a data class of a plurality of data classes, such that the sensor data at each of the plurality of time windows has a value that falls into one of the plurality of data classes and wherein the plurality of data classes encompasses a data range within which the sensor data can vary;
b) assigning for the sensor data by the processor the sensor data at each of the plurality of time windows into a corresponding one of the plurality of data classes on a basis that the sensor data at a particular one of the plurality of time windows has a value that falls into the corresponding one of the plurality of data classes;
c) determining for each physical parameter of a component by the processor a load spectrum, the load spectrum comprising a sum of all of the plurality of data classes including data values and duration in each of the plurality of data classes for the physical parameter of the component, wherein the processor is configured to determine the total duration of the sensor data within each of the plurality of data classes by summing up the plurality of time windows of the sensor data within each of the plurality of data classes;
d) determining by the processor a predicted lifetime for the component comprising application of an aging model from at least one aging model to the corresponding load spectrum for each physical parameter of the component; and
g) continuously updating, by the processor, a planning of system maintenance for the electrical system based on the predicted lifetime for the component.

17. The method according to claim 16 , further comprising:
when the processor determines that the component has met or exceeded the predi

Assignees

• Abb Schweiz Ag

Inventors

• Szasz Paul
• Koenig Kai
• Wildermuth Stephan
• Gebhardt Joerg

Classifications

• G05B23/0283 Primary

  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

• G01R31/327

  Testing of circuit interrupters, switches or circuit-breakers · CPC title

• G01R31/00 Primary

  Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere ({measuring superconductive properties G01R33/1238;} testing line transmission systems H04B3/46; testing or measuring semiconductors or solid state devices during manufacture {H10P74/00}) · CPC title

• G01R31/003

  Environmental or reliability tests (of individual semiconductors G01R31/2642; of PCB's G01R31/2817; of IC's G01R31/2855; of other circuits G01R31/2849) · CPC title

• G07C3/02

  Registering or indicating working or idle time only · CPC title