CN114048261A - Vibration vector early warning value determination method and system based on data description - Google Patents
Vibration vector early warning value determination method and system based on data description Download PDFInfo
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Abstract
The invention relates to a method and a system for determining a vibration vector early warning value based on data description, wherein the method comprises the following steps: obtaining vibration waveform data of the rotating equipment, wherein the vibration waveform data comprises vibration waveform real-time data and vibration waveform historical data when the rotating equipment normally operates; carrying out order analysis on the vibration waveform data to obtain a vibration vector; based on the historical data of the vibration waveform of the rotating equipment in normal operation, learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method; judging whether the vibration vector corresponding to the vibration waveform real-time data is in the early warning value range in real time; historical data and real-time data of the vibration vectors are collected, a data description method is used for learning the early warning value range of the vibration vectors under each characteristic frequency according to the historical data, then real-time early warning is carried out on faults of the rotating machinery according to the real-time data, and the early warning value range is obtained based on the historical data of the rotating machinery, so that the results are more targeted and more accurate.
Description
Technical Field
The invention relates to the field of equipment safety monitoring, in particular to a method and a system for determining a vibration vector early warning value based on data description.
Background
Rotating machines are important basic equipment in industrial (e.g., power plant) production, and safe and stable operation is essential. With the improvement of the demand of equipment operation and maintenance management, the anomaly detection technology is beneficial to changing the traditional after-the-fact maintenance into state maintenance, thereby becoming a new trend in equipment state monitoring. Vibration monitoring is one of the most common methods of monitoring the condition of rotating machines. Vibration sensors are commonly used in power plants for rotating equipment such as steam turbines, induced draft fans, circulating water pumps, etc., and provide a large amount of real-time and historical data. By monitoring the vibration signals, the early abnormality of the equipment can be found, and major production accidents are avoided. The existing vibration monitoring method is mainly based on the fixed threshold value alarm of the characteristic frequency amplitude, and has the following problems: firstly, the threshold value is set too low, and normal production can be influenced by frequent alarming; second, if the threshold setting is too high, then a catastrophic failure may have occurred at the time of alarm triggering, losing the opportunity for maintenance.
Disclosure of Invention
The invention provides a method and a system for determining a vibration vector early warning value based on data description, aiming at the technical problems in the prior art, simultaneously considering amplitude and phase as vibration vectors to carry out intelligent anomaly detection, collecting historical data and real-time data of the vibration vectors, learning the early warning value range of the vibration vectors under each characteristic frequency by using a data description method according to the historical data, and carrying out real-time early warning on the fault of a rotating machine according to the real-time data, wherein the early warning value range is obtained based on the historical data of the rotating machine, so that the result is more targeted and more accurate.
According to a first aspect of the present invention, there is provided a method for determining a vibration vector warning value based on data description, including: step 1, obtaining vibration waveform data of rotating equipment, wherein the vibration waveform data comprises vibration waveform real-time data and vibration waveform historical data when the rotating equipment normally runs;
and 4, judging whether the vibration vector corresponding to the vibration waveform real-time data is in the pre-alarm value range in real time.
On the basis of the technical scheme, the invention can be improved as follows.
Optionally, the process of acquiring the vibration waveform data in step 1 includes:
step 101, acquiring vibration data through a vibration sensor arranged on the rotating equipment;
and 102, denoising and smoothing the vibration data to obtain the vibration waveform data.
Optionally, the step 2 of performing order analysis on the vibration waveform data to obtain a vibration vector includes:
and performing fast Fourier transform on the vibration waveform data to obtain a series of characteristic frequencies and corresponding amplitudes and phases thereof, wherein the characteristic frequencies comprise pass frequency values and harmonic waves thereof, and the vibration vector [ A, phi ] is formed by the amplitude A and the phase phi under each characteristic frequency.
Optionally, step 2 further includes: and converting the vibration vector to a rectangular coordinate system.
Optionally, the vibration vector [ A, phi ] is used]The conversion formula for converting to the rectangular coordinate system is as follows:
[X1,X2]representing the transformed vibration vector.
Optionally, in step 3, a method based on support vector description is used to determine the warning value range of the vibration vector.
Optionally, when the vibration vector corresponding to the vibration waveform real-time data in step 4 performs fault early warning in the early warning value range, if a wrong fault alarm information occurs, the vibration vector corresponding to the wrong fault alarm information is taken as a normal operation sample, and the early warning value range of the vibration vector is learned and updated.
According to a second aspect of the present invention, there is provided a vibration vector warning value determination system based on data description, including: the system comprises a vibration waveform data acquisition module, an order analysis module, a learning module and an early warning module;
the vibration waveform data acquisition module is used for acquiring vibration waveform data of the rotating equipment, wherein the vibration waveform data comprises vibration waveform real-time data and vibration waveform historical data when the rotating equipment normally runs;
the order analysis module is used for carrying out order analysis on the vibration waveform data to obtain vibration vectors;
the learning module is used for learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method based on the historical vibration waveform data when the rotating equipment normally operates;
and the early warning module is used for judging whether the vibration vector corresponding to the vibration waveform real-time data is in the early warning value range in real time.
According to a third aspect of the present invention, there is provided an electronic device comprising a memory, a processor for implementing the steps of the method for determining a vibration vector advance warning value based on data description when executing a computer management class program stored in the memory.
According to a fourth aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer management-like program which, when executed by a processor, implements the steps of a vibration vector advance warning value determination method described based on data.
According to the vibration vector early warning value determining method, the vibration vector early warning value determining system, the electronic equipment and the storage medium, amplitude and phase are taken as vibration vectors to carry out intelligent abnormal detection, historical data and real-time data of the vibration vectors are collected, the early warning value range of the vibration vectors under each characteristic frequency is learned by using a data description method according to the historical data, then real-time early warning is carried out on faults of rotating machinery according to the real-time data, and the early warning value range is obtained based on the historical data of the rotating machinery, so that the results are more targeted and more accurate; the vibration vector is converted into a rectangular coordinate system through mathematical transformation, so that the early warning visualization can be facilitated, and the calculation amount in the early warning value determination process can be simplified; and updating the data after real-time early warning judgment to historical data at regular time, and learning and updating the early warning value range of the vibration vector, particularly wrong early warning detection results, so that the early warning value range of the vibration vector is more and more accurate.
Drawings
Fig. 1 is a flowchart of an embodiment of a method for determining a vibration vector warning value based on data description according to the present invention;
FIG. 2 is a schematic structural diagram of an embodiment of a steam turbine shafting support of the steam turbine of the steam generating unit provided by the invention;
fig. 3(a) is a cascade chart of normal and abnormal conditions in an embodiment of the method and system for determining a vibration vector warning value based on data description according to the present invention;
fig. 3(b) is a cascade chart of normal and abnormal conditions in an embodiment of the method and system for determining a vibration vector warning value according to the present invention;
fig. 3(c) is a schematic diagram of amplitude ranges at different characteristic frequencies in an application embodiment of the vibration vector warning value determination method and system based on data description provided in the present invention;
fig. 4(a) is a schematic diagram of a vibration vector in polar coordinates in an application embodiment of a vibration vector early warning value determination method and system based on data description provided in the present invention;
fig. 4(b) is a schematic diagram of a vibration vector transformed to a rectangular coordinate in an application embodiment of the vibration vector early warning value determination method and system based on data description provided in the present invention, respectively;
fig. 5(a) is a schematic diagram of alarm thresholds learned by three methods in an application embodiment of a vibration vector early warning value determination method and system based on data description provided in the present invention;
fig. 5(b) is a schematic diagram of visualization of anomaly detection based on the SVDD method in an application embodiment of the vibration vector early warning value determination method and system based on data description provided in the present invention;
FIG. 6 is a schematic diagram of a hardware structure of a possible electronic device according to the present invention;
fig. 7 is a schematic diagram of a hardware structure of a possible computer-readable storage medium according to the present invention.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
The invention provides a method for determining a vibration vector early warning value based on data description, which comprises the following steps:
And 2, performing order analysis on the vibration waveform data to obtain a vibration vector.
And 3, learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method based on the vibration waveform historical data of the rotating equipment in normal operation.
And 4, judging whether the vibration vector corresponding to the vibration waveform real-time data is in the early warning value range in real time. And judging the real-time vibration signal so as to realize fault early warning.
Through field actual data analysis, the vibration vectors under certain characteristic frequencies are distributed in a certain range on a polar coordinate graph when the rotary machine normally operates, and the vibration vectors deviate from the range when a fault occurs. Based on the characteristic, the invention provides a vibration vector early warning value determining method based on data description, which comprises the steps of collecting historical data and real-time data of a vibration vector, learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method according to the historical data, and early warning the fault of the rotating machine in real time according to the real-time data, wherein the early warning value range is obtained based on the historical data of the rotating machine, so that the result is more targeted and more accurate.
Example 1
In one possible embodiment, the process of acquiring vibration waveform data includes:
step 101, collecting vibration data through a vibration sensor arranged on a rotating device, wherein the sampling frequency needs to meet fast Fourier transform.
The vibration data are divided into historical data and real-time data, wherein the historical data are used for determining a threshold value of a vibration vector, the real-time data perform real-time early warning on the state of the rotating equipment based on the determined threshold value of the vibration vector, and the real-time data are updated to the historical data at regular time.
And 102, denoising and smoothing the vibration data to obtain vibration waveform data, wherein each waveform represents a sample.
And 2, performing order analysis on the vibration waveform data to obtain a vibration vector.
In a possible embodiment, the order analysis of the vibration waveform data to obtain the vibration vector is an order analysis based on fast fourier transform, and the order analysis includes:
and performing fast Fourier transform on the vibration waveform data to obtain a series of characteristic frequencies and corresponding amplitudes and phases of the characteristic frequencies, wherein the characteristic frequencies comprise pass frequency values and harmonic waves thereof and are expressed as 1X (1 frequency multiplication), 2X (2 frequency multiplication) and the like. The vibration vector [ A, phi ] is composed of the amplitude A and the phase phi at each characteristic frequency.
In a possible embodiment, step 2 further includes: the vibration vectors are converted to a cartesian coordinate system.
In one possible embodiment, the vibration vectors [ A, φ ] are combined]The conversion formula for converting to the rectangular coordinate system is as follows:
[X1,X2]representing the transformed vibration vector.
From amplitude A and phase
A certain vibration vector of the composition is expressed as a point in polar coordinates by the numberThe vibration vector is converted into a rectangular coordinate system through mathematical transformation, and firstly, the early warning visualization is facilitated, and secondly, the calculated amount in the early warning value determination process is simplified.
And 3, learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method based on the vibration waveform historical data of the rotating equipment in normal operation.
In a possible embodiment mode, in step 3, the early warning value range of the vibration vector is determined by a method based on the support vector description.
And 4, judging whether the vibration vector corresponding to the vibration waveform real-time data is in the early warning value range in real time.
In a possible embodiment, when the vibration vector corresponding to the vibration waveform real-time data performs fault early warning in the early warning value range, if wrong fault warning information occurs, the vibration vector corresponding to the wrong fault warning information is taken as a normal operation sample, and the early warning value range of the vibration vector is learned and updated.
Example 2
And the vibration waveform data acquisition module is used for acquiring vibration waveform data of the rotating equipment, wherein the vibration waveform data comprises vibration waveform real-time data and vibration waveform historical data when the rotating equipment normally runs.
And the order analysis module is used for carrying out order analysis on the vibration waveform data to obtain a vibration vector.
And the learning module is used for learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method based on the vibration waveform historical data of the rotating equipment in normal operation.
And the early warning module is used for judging whether the vibration vector corresponding to the vibration waveform real-time data is in the early warning value range in real time.
It can be understood that the vibration vector early warning value determination system based on data description provided by the present invention corresponds to the vibration vector early warning value determination method based on data description provided in the foregoing embodiments, and the relevant technical features of the vibration vector early warning value determination system based on data description may refer to the relevant technical features of the vibration vector early warning value determination method based on data description, and are not described herein again.
Example 3
Fig. 3(a) and 3(b) are respectively order spectrum waterfall graphs of normal and abnormal conditions in an application embodiment of the vibration vector early warning value determination method and system based on data description provided by the present invention, fig. 3(c) is respectively a schematic diagram of amplitude ranges under different characteristic frequencies in an application embodiment of the vibration vector early warning value determination method and system based on data description provided by the present invention, fig. 4(a) -4(b) are respectively schematic diagrams of vibration vectors under polar coordinates and vibration vectors transformed to rectangular coordinates in an application embodiment of the vibration vector early warning value determination method and system based on data description provided by the present invention, and fig. 4(a) and 4(b) are both 1X vibration vector distributions, including normal and abnormal. Fig. 5(a) and 5(b) are schematic diagrams respectively illustrating an alarm threshold learned by three methods and a visualization based on SVDD method anomaly detection in an application embodiment of the vibration vector early warning value determination method and system based on data description provided by the present invention.
As can be seen from FIG. 3(c), the amplitude of 1X is much higher than the other characteristic frequencies, and the amplitude range of 1X is similar in the normal and early abnormal cases. This indicates that the conventional fixed threshold alarm method based on the amplitude of the characteristic frequency has difficulty in detecting early abnormalities. However, when the amplitude and phase of 1X are considered, the difference between the normal state and the early abnormal state is significant, as shown in fig. 4 (a). The present invention uses one point in polar coordinates to represent the vibration vector at a characteristic frequency. The vibration vector is composed of an amplitude part and a phase part. Fig. 4(b) shows the result of conversion of the 1X vibration vector from polar coordinates to rectangular coordinates. It can be seen that the difference between normal and abnormal is evident in the converted data. In this embodiment, the normal samples are clustered in a certain range, the abnormal samples significantly deviate from the range, and the abnormal samples are also scattered. On the basis, the boundary between the abnormity and the normal is determined by a certain method, and the abnormity can be detected.
The SVDD, Minimum Covariance (MCD) and isolated forest (iForest) methods are respectively used to learn the normal range of the vibration vector, and the results are shown in fig. 5(a) and 5(b), where fig. 5(a) and 5(b) describe the learned alarm threshold and the anomaly detection visualization based on the data. As can be seen, the boundary range described by SVDD is more accurate, and from FIG. 5(b), it can be seen that the learned normal boundary range can accurately distinguish between normal and abnormal, indicating that the method is feasible.
Referring to fig. 6, fig. 6 is a schematic view of an embodiment of an electronic device according to an embodiment of the invention. As shown in fig. 6, an embodiment of the present invention provides an electronic device, which includes a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1320 and executable on the processor 1320, where the processor 1320 executes the computer program 1311 to implement the following steps: obtaining vibration waveform data of the rotating equipment, wherein the vibration waveform data comprises vibration waveform real-time data and vibration waveform historical data when the rotating equipment normally runs; carrying out order analysis on the vibration waveform data to obtain a vibration vector; based on the historical data of the vibration waveform of the rotating equipment in normal operation, learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method; and judging whether the vibration vector corresponding to the real-time vibration waveform data is in the early warning value range in real time.
Referring to fig. 7, fig. 7 is a schematic diagram of an embodiment of a computer-readable storage medium according to the present invention. As shown in fig. 7, the present embodiment provides a computer-readable storage medium 1400, on which a computer program 1411 is stored, which computer program 1411, when executed by a processor, implements the steps of: obtaining vibration waveform data of the rotating equipment, wherein the vibration waveform data comprises vibration waveform real-time data and vibration waveform historical data when the rotating equipment normally operates; carrying out order analysis on the vibration waveform data to obtain a vibration vector; based on the historical data of the vibration waveform of the rotating equipment in normal operation, learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method; and judging whether the vibration vector corresponding to the vibration waveform real-time data is in the early warning value range in real time.
According to the vibration vector early warning value determining method, system and storage medium based on data description, amplitude and phase are taken as vibration vectors to perform intelligent abnormal detection, historical data and real-time data of the vibration vectors are collected, the early warning value range of the vibration vectors under each characteristic frequency is learned by using a data description method according to the historical data, real-time early warning is performed on faults of rotating machinery according to the real-time data, and the early warning value range is obtained based on the historical data of the rotating machinery, so that the results are more targeted and more accurate; the vibration vector is converted into a rectangular coordinate system through mathematical transformation, so that the early warning visualization can be facilitated, and the calculation amount in the early warning value determination process can be simplified; and updating the data subjected to real-time early warning judgment to historical data at regular time, and learning and updating the early warning value range of the vibration vector, particularly wrong early warning detection results, so that the early warning value range of the vibration vector is more and more accurate.
It should be noted that, in the above embodiments, the description of each embodiment has a respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to the related description of other embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A vibration vector early warning value determination method based on data description is characterized by comprising the following steps:
step 1, obtaining vibration waveform data of rotating equipment, wherein the vibration waveform data comprises vibration waveform real-time data and vibration waveform historical data when the rotating equipment normally runs;
step 2, carrying out order analysis on the vibration waveform data to obtain a vibration vector;
step 3, learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method based on the historical vibration waveform data of the rotating equipment in normal operation;
and 4, judging whether the vibration vector corresponding to the vibration waveform real-time data is in the early warning value range in real time.
2. The method for determining the vibration vector warning value according to claim 1, wherein the step 1 of obtaining the vibration waveform data comprises:
step 101, acquiring vibration data through a vibration sensor arranged on the rotating equipment;
and 102, denoising and smoothing the vibration data to obtain the vibration waveform data.
3. The method for determining the early warning value of the vibration vector according to claim 1, wherein the step 2 of performing order analysis on the vibration waveform data to obtain the vibration vector comprises:
and performing fast Fourier transform on the vibration waveform data to obtain a series of characteristic frequencies and corresponding amplitudes and phases thereof, wherein the characteristic frequencies comprise pass frequency values and harmonic waves thereof, and the vibration vector [ A, phi ] is formed by the amplitude A and the phase phi under each characteristic frequency.
4. The method for determining the vibration vector warning value according to claim 1, wherein the step 2 further comprises: and converting the vibration vector to a rectangular coordinate system.
5. The method of claim 4, wherein the vibration vector [ A, φ ] is determined]The conversion formula for converting to the rectangular coordinate system is as follows:
[X1,X2]representing the transformed vibration vector.
6. The method for determining the warning value of the vibration vector according to claim 1, wherein a method based on support vector description is used in step 3 to determine the warning value range of the vibration vector.
7. The method for determining the early warning value of the vibration vector according to claim 1, wherein when the vibration vector corresponding to the real-time vibration waveform data performs fault early warning in the early warning value range in the step 4, if wrong fault warning information occurs, the vibration vector corresponding to the wrong fault warning information is taken as a normal operation sample, and the early warning value range of the vibration vector is learned and updated.
8. A vibration vector early warning value determination system based on data description is characterized by comprising: the system comprises a vibration waveform data acquisition module, an order analysis module, a learning module and an early warning module;
the vibration waveform data acquisition module is used for acquiring vibration waveform data of the rotating equipment, wherein the vibration waveform data comprises vibration waveform real-time data and vibration waveform historical data when the rotating equipment normally runs;
the order analysis module is used for carrying out order analysis on the vibration waveform data to obtain a vibration vector;
the learning module is used for learning the early warning value range of the vibration vector under each characteristic frequency by using a data description method based on the historical vibration waveform data when the rotating equipment normally operates;
and the early warning module is used for judging whether the vibration vector corresponding to the vibration waveform real-time data is in the early warning value range in real time.
9. An electronic device, comprising a memory, and a processor, wherein the processor is configured to implement the steps of the method for determining a vibration vector warning value based on a data description according to any one of claims 1 to 7 when executing a computer management class program stored in the memory.
10. A computer-readable storage medium, having stored thereon a computer management class program, which when executed by a processor, carries out the steps of the data description based vibration vector alert value determination method according to any one of claims 1 to 7.
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