CN-116878887-B - Method and device for judging stability of equipment state signal

CN116878887BCN 116878887 BCN116878887 BCN 116878887BCN-116878887-B

Abstract

The application provides a judging method and a judging device for the stability of equipment state signals, wherein the judging method comprises the steps of obtaining a state signal curve of equipment to be tested in a preset time length; and carrying out stability analysis on the equipment to be tested by utilizing the fuzzy evaluation model and combining the state signal curve so as to determine the target steady state level of the equipment to be tested within the preset time. The status signal curve is configured to reflect a trend of a characteristic parameter of the device under test over the run time. The factor set of the fuzzy evaluation model comprises deviation parameters corresponding to characteristic parameters, the evaluation set of the fuzzy evaluation model comprises a plurality of different preset steady state grades, and the plurality of preset steady state grades are divided according to a plurality of preset membership function values of the fuzzy evaluation model. According to the application, the stability analysis is automatically carried out on the equipment to be tested by utilizing the fuzzy evaluation model, so that the labor is saved, and the judgment criteria of the stability analysis are kept consistent.

Inventors

LI ZHEN
WANG ZHIYONG
DAI XIANYUAN
ZHANG RONG
LI WEIZHE
GUO YUFENG
CHEN RONGTIAN
CHEN HUIHUI
WANG HONGKAI
ZHANG WEI
YANG JIANGANG
GAO YUAN
CHEN XINGYUE
LIN YUCHEN
HE MINGYUAN

Assignees

福建福清核电有限公司

Dates

Publication Date: 20260512
Application Date: 20230602

Claims (10)

1. A method for determining the stability of a device status signal is characterized in that, Acquiring a state signal curve of equipment to be tested in a preset time length, wherein the state signal curve is configured to reflect the trend of the characteristic parameter variation of the equipment to be tested along with the running time; And carrying out stability analysis on the equipment to be tested by utilizing a fuzzy evaluation model and combining the state signal curve so as to determine a target steady state grade of the equipment to be tested within the preset duration, wherein the subfractions of the fuzzy evaluation model comprise deviation parameters corresponding to the characteristic parameters, an evaluation set of the fuzzy evaluation model comprises a plurality of different preset steady state grades, and the plurality of preset steady state grades are divided according to a plurality of preset membership function values of the fuzzy evaluation model.
2. The method according to claim 1, wherein the performing stability analysis on the device under test by using the fuzzy evaluation model and combining the state signal curves to determine a target steady state level of the device under test within the preset time period includes: Calculating a target membership function value corresponding to the state signal curve by using a fuzzy evaluation model; and determining the target steady state grade of the equipment to be tested in the preset time according to the target membership function value.
3. The method according to claim 2, wherein before the calculating the objective membership function value corresponding to the state signal curve using the fuzzy evaluation model, the method further comprises: judging whether the rotating speed of the equipment to be tested is stable within the preset duration; The calculating the target membership function value corresponding to the state signal curve by using the fuzzy evaluation model comprises the following steps: and if the rotating speed of the equipment to be tested is stable within the preset duration, calculating a target membership function value corresponding to the state signal curve by using a fuzzy evaluation model.
4. The method according to claim 3, wherein calculating the target membership function value corresponding to the state signal curve using a fuzzy evaluation model if the rotational speed of the device to be tested is stable within the preset time period comprises: if the rotating speed of the equipment to be tested is stable in the preset duration, dividing the state signal curve into a plurality of data analysis packets; calculating deviation parameters corresponding to the characteristic parameters in a plurality of data analysis packets; and determining a target membership function value corresponding to the state signal curve by using a fuzzy evaluation model based on deviation parameters corresponding to the characteristic parameters in a plurality of data analysis packages.
5. The method according to claim 4, wherein determining the target membership function value corresponding to the state signal curve using a fuzzy evaluation model based on the deviation parameters corresponding to the characteristic parameters in the plurality of data analysis packages comprises: selecting a maximum deviation parameter from deviation parameters corresponding to the characteristic parameters in a plurality of data analysis packages as a sub-factor of a fuzzy evaluation model; and calculating a target membership function value corresponding to the state signal curve based on the maximum deviation parameter by using a fuzzy evaluation model.
6. The method according to claim 4, wherein determining the target membership function value corresponding to the state signal curve using a fuzzy evaluation model based on the deviation parameters corresponding to the characteristic parameters in the plurality of data analysis packages comprises: Calculating a plurality of membership function values based on deviation parameters corresponding to the characteristic parameters in a plurality of data analysis packages by using a fuzzy evaluation model; And determining the maximum membership function value in the membership function values as a target membership function value corresponding to the state signal curve.
7. The method according to any one of claims 1 to 6, wherein the membership function in the fuzzy evaluation model is a gaussian membership function S, which is Wherein c and k are constants, x is a subfactor of the fuzzy evaluation model, and y is a membership function value corresponding to the subfactor.
8. A device for determining stability of a device status signal, comprising: The device comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a state signal curve of equipment to be tested in a preset time length, and the state signal curve is configured to reflect the trend of the characteristic parameter of the equipment to be tested along with the change of the running time; The analysis module is used for carrying out stability analysis on the equipment to be tested by utilizing a fuzzy evaluation model and combining the state signal curve so as to determine a target steady state grade of the equipment to be tested within the preset duration, wherein the subfractions of the fuzzy evaluation model comprise deviation parameters corresponding to the characteristic parameters, the evaluation set of the fuzzy evaluation model comprises a plurality of different preset steady state grades, and the plurality of preset steady state grades are divided according to a plurality of preset membership function values of the fuzzy evaluation model.
9. A computer readable storage medium having stored thereon executable instructions of a computer, wherein the executable instructions when executed by a processor implement the method of determining the stability of a device status signal according to any of claims 1 to 7.
10. An electronic device, comprising: A processor for executing the method for determining the stability of the device status signal according to any one of claims 1 to 7, and And the memory is used for storing executable instructions of the processor.

Description

Method and device for judging stability of equipment state signal Technical Field The application belongs to the technical field of measurement, and particularly relates to a method and a device for judging stability of equipment state signals. Background In the case of fault diagnosis of equipment such as turbines, generators, pumps or fans, the approach requires analysis of status signal trends of the equipment such as vibrations (which may be expressed in terms of changes in speed, displacement or acceleration etc.), temperature, pressure or flow. Most of the current state signal trend curves are drawn by a computer, and the trend of the state signal is judged manually according to the curves. When the state signals are more, the judgment is difficult to be carried out by manpower one by one, and a great amount of manpower is needed to carry out trend analysis and judgment. Especially when the trend of the status signal is unstable, the decision criteria of different persons are difficult to be kept consistent. Disclosure of Invention In view of the above, the present application provides a method and apparatus for determining stability of a device status signal, which automatically analyzes stability of a device to be tested by using a fuzzy evaluation model, thereby saving manpower and keeping the determination criteria of stability analysis consistent. The application provides a method for judging stability of equipment state signals, which comprises the steps of obtaining a state signal curve of equipment to be tested in a preset time period, and analyzing the stability of the equipment to be tested by utilizing a fuzzy evaluation model and combining the state signal curve so as to determine a target stable state level of the equipment to be tested in the preset time period. The status signal curve is configured to reflect a trend of a characteristic parameter of the device under test over the run time. The sub-factors of the fuzzy evaluation model comprise deviation parameters corresponding to the characteristic parameters. The evaluation set of the fuzzy evaluation model comprises a plurality of different preset steady state grades, and the plurality of preset steady state grades are divided according to a plurality of preset membership function values of the fuzzy evaluation model. In the scheme, the stability analysis is carried out on the equipment to be tested by utilizing the fuzzy evaluation model and combining the state signal curve, so that the target steady state level of the equipment to be tested in the preset time period can be automatically determined, the situation that the manual judgment workload is too large or a large number of people need to be invested to carry out analysis judgment when the state signals are too many is avoided, the manpower is further saved, and the foundation is laid for the deep learning of the signals. In a specific embodiment of the application, the method for analyzing the stability of the equipment to be tested by utilizing the fuzzy evaluation model and combining the state signal curve to determine the target steady state level of the equipment to be tested in the preset time comprises the steps of calculating the target membership function value corresponding to the state signal curve by utilizing the fuzzy evaluation model and determining the target steady state level of the equipment to be tested in the preset time according to the target membership function value. In one embodiment of the present application, before the calculating the target membership function value corresponding to the state signal curve by using the fuzzy evaluation model, the determining method further includes determining whether the rotation speed of the device to be tested is stable within a preset time period. The calculating the target membership function value corresponding to the state signal curve by using the fuzzy evaluation model comprises the step of calculating the target membership function value corresponding to the state signal curve by using the fuzzy evaluation model if the rotating speed of the equipment to be tested is stable within a preset time length. In a specific embodiment of the application, the calculating the target membership function value corresponding to the state signal curve by using the fuzzy evaluation model if the rotation speed of the device to be tested is stable within the preset time period comprises dividing the state signal curve into a plurality of data analysis packages if the rotation speed of the device to be tested is stable within the preset time period, calculating deviation parameters corresponding to characteristic parameters in the plurality of data analysis packages, and determining the target membership function value corresponding to the state signal curve by using the fuzzy evaluation model based on the deviation parameters corresponding to the characteristic parameters in the plurality of data analysis packages. In a sp