CN112082720A - Method for determining early warning value of vibration fault of fixed-speed rotating machine - Google Patents
Method for determining early warning value of vibration fault of fixed-speed rotating machine Download PDFInfo
- Publication number
- CN112082720A CN112082720A CN202010919626.8A CN202010919626A CN112082720A CN 112082720 A CN112082720 A CN 112082720A CN 202010919626 A CN202010919626 A CN 202010919626A CN 112082720 A CN112082720 A CN 112082720A
- Authority
- CN
- China
- Prior art keywords
- sample
- vibration
- early warning
- determining
- rotating machine
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M7/00—Vibration-testing of structures; Shock-testing of structures
- G01M7/02—Vibration-testing by means of a shake table
- G01M7/025—Measuring arrangements
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a method for determining an early warning value of vibration fault of a rotating machine with fixed rotating speed. Therefore, the method can change vibration monitoring into the concept of vectors under each frequency multiplication and characteristic frequency, thereby monitoring the equipment state more comprehensively and providing a reliable basis for equipment predictive maintenance.
Description
Technical Field
The invention belongs to the technical field of mechanical fault detection, and particularly relates to a method for determining an early warning value of a vibration fault of a rotating machine with a fixed rotating speed.
Background
For rotating equipment, most faults are vibration-related. Therefore, vibration monitoring is a common technical means in the monitoring of the state of a rotating machine, and can realize early fault early warning and fault diagnosis. The vibration monitoring mainly comprises amplitude, frequency and phase, and the amplitude of the vibration is usually only considered in practical application, namely the size of the vibration is taken as the basis of the vibration monitoring and equipment state analysis, and the method is effective in most cases, but the vibration phenomenon can be covered in some special cases. At present, vibration phase monitoring is mainly realized by hardware measurement, and the method has certain limitation in consideration of hardware price and equipment operating environment. In turbomachinery vibration monitoring, some countries have adopted vector models (vibration target maps) combining vibration amplitude and angle to detect themselves, and the method takes a certain area as a safety area, once a vibration vector point falls outside the safety area, a unit alarms, but the safety area needs to be determined by human experience.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a method for determining the early warning value of the vibration fault of the rotating machinery with fixed rotating speed.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for determining an early warning value of vibration fault of a fixed-speed rotating machine is characterized by comprising the following steps:
s1: determining vector monitoring safety regions under each frequency multiplication;
s2: and (5) making an alarm strategy according to the vector monitoring safety region.
In order to optimize the technical scheme, the specific measures adopted further comprise:
further, S1 specifically includes:
s11: acquiring vibration original data in normal operation, converting the original data into a series of frequency multiplication components by adopting Fourier transform, and recording vibration vectors of the frequency multiplication components of each sample;
s12: under all samples, finding out the sample central point of each frequency multiplication component by adopting an optimization algorithm;
s13: after the sample center point under the specific frequency multiplication is determined, the distances between the remaining sample points and the sample center point are calculated, and a safety region is defined according to the distances between the sample points and the sample center point.
Further, in S12, the objective function of the optimization algorithm is as follows:
in the formula, N represents the number of samples, XiDenotes the ith sample point, XcRepresenting a sample center point; the objective function represents that a sample central point is found from all sample points, and the sum of distances from all the rest sample points to the sample central point is minimum.
Further, in S13, the vibration vector is expressed as points on polar coordinates, and the distance between the points on polar coordinates is expressed as follows:
in the formula, X1＝[θ1,ρ1]And X2＝[θ2,ρ2]Respectively representing two points on polar coordinates, and theta and rho respectively representing an angle and a distance;
after the sample center point under a specific frequency multiplication is determined, calculating the distances from all the rest sample points to the sample center point and recording the distance as a matrix D ═ dist (X)1,Xc),…,dist(XN,Xc)](ii) a The elements in the matrix D are arranged from small to large, Q1Representing the upper quartile, Q3Denotes the lower quartile, IQR ═ Q1-Q3Represents the quarterwave, therefore, the safety region is defined in terms of the distance of the sample point from the sample center point:
dist_safe＝Q3+η*IQR (3)
where eta is a constant, when dist (X)i,Xc)>When dist _ safe, i ═ 1.. N, the sample point is considered to fall outside the safe area.
Further, S2 specifically includes:
s21: continuously monitoring online vibration data, and defining the distance level of falling out of a safe area;
s22: and recording the times of exceeding each far and near level, and setting the triggering condition of the alarm according to the times.
Further, in S21, in the process of monitoring the online vibration data, P times of continuous sampling are performed, and the distance level of falling out of the safety region is defined as follows:
in the formula, the three levels of alam _1, alam _2 and alam _3 from near to far are eta1<η2<η3。
Further, in S22, the numbers of times of exceeding alarm _1, alarm _2, and alarm _3 are denoted by p1、p2、p3And if any of the following conditions is met, triggering an alarm:
wherein, 0 is less than or equal to gamma3＜γ2＜γ1≤1。
The invention has the beneficial effects that: the invention utilizes normal vibration original data to obtain series vibration components (amplitude and phase) through Fourier transform, combines the vibration amplitude and the phase of each vibration component into a vector, and automatically determines a safe area through moment operation. Therefore, the method can change vibration monitoring into the concept of vectors under each frequency multiplication and characteristic frequency, thereby monitoring the equipment state more comprehensively and providing a reliable basis for equipment predictive maintenance.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a #7X normal sample vibration vector diagram of the present invention.
Fig. 3 is a #7X fault sample vibration vector diagram of the present invention.
Detailed Description
The present invention will now be described in further detail with reference to the accompanying drawings.
The method for determining the early warning value of the vibration fault of the fixed-speed rotating machine shown in the figure 1 comprises the following steps:
s1: determining vector monitoring safety regions under each frequency multiplication and characteristic frequency:
acquiring historical vibration data of equipment in normal operation, converting original data into a series of frequency multiplication components by adopting Fourier transform, and recording vibration vectors of each component of each sample; under all samples, finding out the central point of each component sample by adopting an optimization algorithm, wherein an objective function is as follows:
wherein: n denotes the number of samples, XiDenotes the ith sample point, XcRepresenting a center point; the above objective function means that the center point is found from all the sample points, and the sum of the distances from all the other samples to the point is the smallest.
The vibration vector may represent points on polar coordinates, and the distance between points on polar coordinates is formulated as follows:
wherein: x1＝[θ1，ρ1]And X2＝[θ2，ρ2]Respectively representing two points on polar coordinates;
after determining the center points of all samples at a particular frequency multiplication, the distances from all remaining points to the center points are calculated and recorded as a matrix D ═ dist (X)1,Xc),…,dist(XN,Xc)](ii) a The elements in the matrix D are arranged from small to large, Q1Represents the upper quartile (the first 25% cut-off), Q3Denotes the lower quartile (cut point of the first 75%), IQR ═ Q1-Q3Representing the interquartile range, therefore, the safety region is defined in terms of the distance of the sample point from the center point:
dist_safe＝Q3+η*IQR (3)
where eta is a constant, when dist (X)i,Xc)>dist _ safe, the point is considered to fall outside the safe area;
therefore, vector monitoring of vibration amplitude and phase under frequency multiplication and characteristic frequency is realized by using the vibration original waveform.
S2: and (3) alarm strategy: the on-line vibration data is monitored continuously for a period of time (P times of continuous sampling), and the far and near levels alarm _1, alarm _2 and alarm _3 falling out of the safe region are defined, and can be in multiple stages, wherein eta is1<η2<η3；
The times of exceeding alarm _1, alarm _2, alarm _3 during a certain monitoring process are respectively denoted as p1、p2、p3(ii) a The alarm is triggered when the following arbitrary conditions are met, wherein gamma is more than or equal to 03<γ2<γ1≤1：
Therefore, a safe region of a specific frequency multiplication vibration vector and a flexible alarm strategy are defined.
Example 1:
taking X-direction vibration of a steam turbine #7 rotor of a certain power plant as an example, the rotating speed of the steam turbine is 3000 r/min, the sampling frequency is 128 points/r, 177 samples are normal samples, each sample comprises 1024 points, and the FFT length is 1024 when Fourier transform is carried out. Taking 1 octave as an example, η is 1.5 in the safety region definition, as shown in fig. 2.
Taking fault data (201 pieces in total) at the same rotating speed for testing, and correspondingly taking a 1-time-multiplication and vibration vector monitoring graph as shown in FIG. 3; the alarm policy parameter settings are shown in table 1.
TABLE 1 alarm parameter settings
Obtaining a triggering alarm condition:
here, it can be seen that the failure data is far from the safety area because the test is performed with the failure data.
The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above-mentioned embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may be made by those skilled in the art without departing from the principle of the invention.
Claims (7)
1. A method for determining an early warning value of vibration fault of a fixed-speed rotating machine is characterized by comprising the following steps:
s1: determining vector monitoring safety regions under each frequency multiplication;
s2: and (5) making an alarm strategy according to the vector monitoring safety region.
2. The method for determining the early warning value of the vibration fault of the fixed-speed rotating machine according to claim 1, wherein the method comprises the following steps: s1 specifically includes:
s11: acquiring vibration original data in normal operation, converting the original data into a series of frequency multiplication components by adopting Fourier transform, and recording vibration vectors of the frequency multiplication components of each sample;
s12: under all samples, finding out the sample central point of each frequency multiplication component by adopting an optimization algorithm;
s13: after the sample center point under the specific frequency multiplication is determined, the distances between the remaining sample points and the sample center point are calculated, and a safety region is defined according to the distances between the sample points and the sample center point.
3. The method for determining the early warning value of the vibration fault of the fixed-speed rotating machine according to claim 2, wherein the method comprises the following steps: in S12, the objective function of the optimization algorithm is as follows:
in the formula, N represents the number of samples, XiDenotes the ith sample point, XcRepresenting a sample center point; the objective function represents that a sample central point is found from all sample points, and the sum of distances from all the rest sample points to the sample central point is minimum.
4. The method for determining the early warning value of the vibration fault of the fixed-speed rotating machine according to claim 2, wherein the method comprises the following steps: in S13, the vibration vector is expressed as points on polar coordinates, and the distance between the points on polar coordinates is expressed as follows:
in the formula, X1＝[θ1,ρ1]And X2＝[θ2,ρ2]Respectively representing two points on polar coordinates, and theta and rho respectively representing an angle and a distance;
after the sample center point under a specific frequency multiplication is determined, calculating the distances from all the rest sample points to the sample center point and recording the distance as a matrix D ═ dist (X)1,Xc),…,dist(XN,Xc)](ii) a The elements in the matrix D are arranged from small to large, Q1Representing the upper quartile, Q3Denotes the lower quartile, IQR ═ Q1-Q3Represents the quarterwave, therefore, the safety region is defined in terms of the distance of the sample point from the sample center point:
dist_safe＝Q3+η*IQR (3)
where eta is a constant, when dist (X)i,Xc)>When dist _ safe, i ═ 1.. N, the sample point is considered to fall outside the safe area.
5. The method for determining the early warning value of the vibration fault of the fixed-speed rotating machine according to claim 1, wherein the method comprises the following steps: s2 specifically includes:
s21: continuously monitoring online vibration data, and defining the distance level of falling out of a safe area;
s22: and recording the times of exceeding each far and near level, and setting the triggering condition of the alarm according to the times.
6. The method for determining the early warning value of the vibration fault of the fixed-speed rotating machine according to claim 5, wherein the method comprises the following steps: in S21, in the process of monitoring the online vibration data, P times of continuous sampling are performed, and the distance level of falling out of the safety region is defined:
in the formula, the three levels of alam _1, alam _2 and alam _3 from near to far are eta1<η2<η3。
7. The method for determining the early warning value of the vibration fault of the fixed-speed rotating machine according to claim 6, wherein the method comprises the following steps: in S22, the numbers of times of exceeding alarm _1, alarm _2, and alarm _3 are respectively denoted by p1、p2、p3And if any of the following conditions is met, triggering an alarm:
wherein, 0 is less than or equal to gamma3<γ2<γ1≤1。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010919626.8A CN112082720A (en) | 2020-09-04 | 2020-09-04 | Method for determining early warning value of vibration fault of fixed-speed rotating machine |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010919626.8A CN112082720A (en) | 2020-09-04 | 2020-09-04 | Method for determining early warning value of vibration fault of fixed-speed rotating machine |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112082720A true CN112082720A (en) | 2020-12-15 |
Family
ID=73731921
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010919626.8A Pending CN112082720A (en) | 2020-09-04 | 2020-09-04 | Method for determining early warning value of vibration fault of fixed-speed rotating machine |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112082720A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113237619A (en) * | 2021-04-16 | 2021-08-10 | 江苏方天电力技术有限公司 | Fault early warning method, device, equipment and storage medium for variable-speed rotating machinery vibration |
| CN113484001A (en) * | 2021-06-30 | 2021-10-08 | 江苏方天电力技术有限公司 | Method for testing differential vibration between connecting parts |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR830005561A (en) * | 1981-03-31 | 1983-08-20 | 요시야마 히로기찌 | Diagnosis method of symptom in axial vibration monitoring of rotating machine |
| CN1370983A (en) * | 2001-02-14 | 2002-09-25 | 深圳市创为实测控技术有限公司 | Sensitive rotating machines monitoring and data storing method |
| CN104880247A (en) * | 2015-05-04 | 2015-09-02 | 华中科技大学 | Combined alarming method for on-line monitoring system for rotary machinery |
| CN105157821A (en) * | 2015-08-30 | 2015-12-16 | 华电电力科学研究院 | Rotary machinery vibration fault diagnosis and quantitative analysis method |
| CN109708907A (en) * | 2018-11-30 | 2019-05-03 | 浙江胄天科技股份有限公司 | A kind of equipment fault feature extracting method based on envelope information |
-
2020
- 2020-09-04 CN CN202010919626.8A patent/CN112082720A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR830005561A (en) * | 1981-03-31 | 1983-08-20 | 요시야마 히로기찌 | Diagnosis method of symptom in axial vibration monitoring of rotating machine |
| CN1370983A (en) * | 2001-02-14 | 2002-09-25 | 深圳市创为实测控技术有限公司 | Sensitive rotating machines monitoring and data storing method |
| CN104880247A (en) * | 2015-05-04 | 2015-09-02 | 华中科技大学 | Combined alarming method for on-line monitoring system for rotary machinery |
| CN105157821A (en) * | 2015-08-30 | 2015-12-16 | 华电电力科学研究院 | Rotary machinery vibration fault diagnosis and quantitative analysis method |
| CN109708907A (en) * | 2018-11-30 | 2019-05-03 | 浙江胄天科技股份有限公司 | A kind of equipment fault feature extracting method based on envelope information |
Non-Patent Citations (2)
| Title |
|---|
| 孔文梅 等: "基于矢量的风机振动监测与诊断", 《风机技术》 * |
| 牟少敏 等: "《模式识别与机器学习技术》", 30 June 2019, 冶金工业出版社 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113237619A (en) * | 2021-04-16 | 2021-08-10 | 江苏方天电力技术有限公司 | Fault early warning method, device, equipment and storage medium for variable-speed rotating machinery vibration |
| CN113484001A (en) * | 2021-06-30 | 2021-10-08 | 江苏方天电力技术有限公司 | Method for testing differential vibration between connecting parts |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP7199608B2 (en) | Methods and apparatus for inspecting wind turbine blades, and equipment and storage media therefor | |
| CN108087210B (en) | Wind generating set blade abnormity identification method and device | |
| EP2520794B1 (en) | Monitoring apparatus for checking a wind turbine in a wind farm for a yaw misalignment | |
| CN111400961B (en) | Wind generating set blade fault judging method and device | |
| CN110006552B (en) | Method for detecting abnormal temperature of equipment | |
| CN107192446B (en) | Method for monitoring natural frequency of tower of wind generating set | |
| CN112082720A (en) | Method for determining early warning value of vibration fault of fixed-speed rotating machine | |
| EP2877710A1 (en) | Method and system for monitoring rotating blade health | |
| WO2022048228A1 (en) | Load control method and apparatus for wind turbine generator system | |
| CN113239613B (en) | Method for judging swing degree alarm of water turbine unit | |
| CN111120388B (en) | Fan state combined monitoring method and system | |
| CN111122191B (en) | Equipment health alarm threshold setting method based on EWMA control | |
| CN112834224A (en) | Method and system for evaluating health state of nuclear power steam turbine generator | |
| US10088830B2 (en) | Apparatus and method for detecting the current damaged state of a machine | |
| EP3732370A1 (en) | Method and apparatus for monitoring a wind turbine | |
| Jami et al. | Impeller fault detection under variable flow conditions based on three feature extraction methods and artificial neural networks | |
| CN113237619B (en) | Fault early warning method, device, equipment and storage medium for variable-speed rotating machinery vibration | |
| CN116624341A (en) | Wind turbine generator power abnormality diagnosis method, system and equipment | |
| CN114645824A (en) | Blade stall identification method and device for wind generating set | |
| CN115374653B (en) | NSET model-based wind driven generator temperature early warning method and related device | |
| CN116557230A (en) | Wind power plant unit power abnormality online assessment method and system | |
| CN111400959B (en) | Blade fault diagnosis method and device for wind generating set | |
| CN113884301B (en) | Threshold determination method, system and readable storage medium | |
| TWI762101B (en) | Abnormal Diagnosis Device and Abnormal Diagnosis Program | |
| CN116664018B (en) | Power plant equipment running state evaluation platform |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201215 |
|
| RJ01 | Rejection of invention patent application after publication |