CN104614178A - Method for extracting fault symptoms based on vector spectrum - Google Patents
Method for extracting fault symptoms based on vector spectrum Download PDFInfo
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- CN104614178A CN104614178A CN201510103524.8A CN201510103524A CN104614178A CN 104614178 A CN104614178 A CN 104614178A CN 201510103524 A CN201510103524 A CN 201510103524A CN 104614178 A CN104614178 A CN 104614178A
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Abstract
The invention relates to a method for extracting fault symptoms based on a vector spectrum, and belongs to the technical field of mechanical fault diagnosis. The method comprises the following steps: firstly measuring vibration original signals of two channels vertical to each other on a same section, then performing vector spectrum filtering of the collected vibration data of the two channels and extracting the main vibration vector values of the vector spectrum under different doubled frequencies; determining the membership of each doubled frequency based on fuzzy processing of the extracted main vibration vector values under doubled frequencies; extracting symptom information according to the determined membership and determining the types of the vibration defaults. The method disclosed by the invention adopts the analysis method of two-channel vector spectrum, and can objectively, truly reflect the frequency spectrum of vibration signals, and the reliability of default diagnosis can be remarkably improved by combining the reasonable fuzzy distribution function.
Description
Technical field
The present invention relates to a kind of failure symptom extracting method based on vector spectrum, belong to technology for mechanical fault diagnosis field.
Background technology
Mechanical fault expert system diagnosis accurately prerequisite is the accuracy of failure symptom extraction and rational sign fuzzy method.At present, mechanical fault symptoms abstraction is based on single pass, according to patent CN101929917A, when single channel signal gathers, signals collecting is not comprehensive, and same position shows different without the signal spectrum figure of angle detecting, occurs that the possibility of erroneous judgement is very large, based on the vector spectrum technology that homologous information merges, effectively can evade the inconsistent problem of different measuring points collection of illustrative plates, two passages are carried out vectorized process, the accuracy that failure symptom extracts can be greatly improved.The Fuzzy processing of sign is the important channel of improving expert system diagnosis, and the process of obfuscation determines the process of subordinate function, and the confirmation of subordinate function is conventional at present has three kinds, based on expert graded with based on dualistic contrast compositor with based on Fuzzy Distribution.First two is according to expertise, and subjectivity is larger.Fuzzy distribution function is experimentally analyzed, and can effectively avoid subjectivity difference, suitable ambiguity function must be determined in conjunction with actual conditions.Vow spectrum prognostic information than single channel frequency spectrum prognostic information more comprehensively, but based on vowing that the ambiguity function of spectrum mainly relies on expert estimation at present, the reliability of expert system diagnosis effectively can not ensure.
Summary of the invention
The object of this invention is to provide a kind of failure symptom extracting method based on vector spectrum, with solve existing employing single channel signal carry out acquisition process cause False Rate large problem.
The present invention is for solving the problems of the technologies described above and providing a kind of failure symptom extracting method based on vector spectrum, and this extracting method comprises the following steps:
1) vibration data of orthogonal two passages in same cross section is gathered;
2) carry out vector spectrum filtering to the vibration data of gathered two passages, the master extracted on vector spectrogram under different frequency multiplication shakes vector value;
3) Fuzzy processing is carried out to the vector value that shakes of the master under extracted each frequency multiplication, determine the degree of membership of each frequency multiplication;
4) failure symptom information is extracted, to determine type belonging to vibration fault according to determined degree of membership.
Described step 3) be adopt to rise the determination that ridge shape distribution function carries out degree of membership.
The described ridge shape distribution function that rises is:
Wherein x is vibration amplitude, a and b is the threshold value of different stage.
Described step 4) in the failure symptom that extracts be that degree of membership is greater than fault corresponding to setting degree of membership threshold value.
Described setting degree of membership threshold value is the half of maximum membership degree.
Described step 1) be realize measuring by arranging two mutually perpendicular sensors in same cross section.
The invention has the beneficial effects as follows: first the present invention measures the vibration original signal of orthogonal two passages in same cross section, then carry out vector spectrum filtering to the vibration data of gathered two passages, the master extracted on vector spectrogram under different frequency multiplication shakes vector value; And Fuzzy processing is carried out to the vector value that shakes of the master under extracted each frequency multiplication, determine the degree of membership of each frequency multiplication; Extract prognostic information according to determined degree of membership, determine type belonging to vibration fault.The present invention adopts twin-channel vector spectrum analytical approach can reflect the spread spectrum scenarios of vibration signal objective reality, in conjunction with rational Fuzzy distribution function, can significantly improve the reliability of fault diagnosis.
Accompanying drawing explanation
Fig. 1 is the sensor mounting location schematic diagram measuring two-channel vibrating functional signal in the embodiment of the present invention;
Fig. 2-a be in the embodiment of the present invention X to spectrum analysis figure;
Fig. 2-b is Y-direction spectrum analysis figure in the embodiment of the present invention;
Fig. 2-c is binary channels vector spectrum analysis chart in the embodiment of the present invention;
Fig. 3 is the process flow diagram based on the failure symptom extracting method of vector spectrum in the embodiment of the present invention.
Embodiment
Below in conjunction with accompanying drawing, the specific embodiment of the present invention is further described.
The present invention is by measuring the vibration original signal of orthogonal two passages in same cross section, the spread spectrum scenarios of rotor is reflected by vector spectrum analytical approach objective reality, in conjunction with rational Fuzzy distribution function, the reliability of fault diagnosis can be significantly improved, as shown in Figure 3, detailed process is as follows for its flow process:
1. gather the vibration original signal of orthogonal two passages in same cross section
This step realizes by sensor installation on orthogonal two passages in same cross section.The present embodiment is described for certain power plant steam turbine rotor oscillation signal, the layout of sensor is according to the feature of rotor, pass the two ends adopting and be arranged symmetrically in rotor, install sketch as shown in Figure 1, the vibration data of this power plant steam turbine rotor measuring point 2 position can adopt orthogonal electric vortex sensor measuring to go out.
2. the vibration data of pair two passages gathered carries out vector spectrum filtering, and the master extracted on vector spectrogram under different frequency multiplication shakes vector value.
According to the binary channels current vortex data that measuring point 2 collects, carry out vector spectrum analysis, wherein for X to for frequency spectrum, as shown in Fig. 2-a, 1 frequency multiplication value is maximum, and be attended by 2 frequencys multiplication, other frequency multiplication values are very little, draw it may is rotor bow fault according to expertise; Analyze from Y-direction frequency spectrum separately, as shown in Fig. 2-b, 2 frequencys multiplication values are maximum, and other frequencys multiplication smaller, draws it may is misalign fault according to expertise.Vector spectrum analysis is carried out to x, y two passages, as shown in fig. 2-c, can find out, 2 frequency multiplication values are maximum, be attended by 1 frequency multiplication, draw comprehensively to be inferred as according to expertise and misalign fault, analyze x separately, y channel frequency spectrum figure, likely obtain different inferred results, therefore, the approach that vector spectrum Filtering Analysis is highly effective raising fault diagnosis accuracy is carried out to x, y passage.
3. the vector value that shakes of the master under pair each frequency multiplication extracted carries out Fuzzy processing, determines the degree of membership of each frequency multiplication.
When vibration amplitude reaches a certain setting danger warning value b, degree of membership is defined as 1, and when vibration amplitude is lower than a certain setting alarming value a, degree of membership is defined as 0.According to this, adopt ambiguity function to process the main vector value that shakes, the Fuzzy distribution function that this enforcement adopts is for rise ridge shape distribution function, and concrete form is as follows:
Amplitude under main frequency multiplication (1/4X, 1/2X, 1X, 2X, 3X, 4X, 5X) on spectrogram is carried out Fuzzy processing, determines the degree of membership of each frequency multiplication, form sign fuzzy vector.According to vector spectrum characteristic, a, b value that vector Pu Sheng ridge shape distribution function is corresponding should be 0.3 ~ 0.5 times during single channel.
(4) fuzzy matrix
Fuzzy matrix R is the important step embodying expert system fault diagnosis level, and be the important component part of expert reasoning machine, blurring mapping formula b=aR is sign fuzzy vector a (a
1, a
2..., a
m) and fault fuzzy vector b (b
1, b
2..., b
n) bridge.
(5) setting of threshold value
For effectively ensureing the reliability that fault is inferred, avoiding the generation of judging by accident, needing setting degree of membership threshold value, when degree of membership exceedes given threshold value, prove to infer effectively.General setting threshold value is the half of maximum membership degree value.
(6) checking and conclusion
In order to verify the validity that vector spectrum sign is fuzzy, at the scene steam turbine has carried out multiple authentication, experimental procedure is carried out according to above step.Single channel frequency spectrum sign Fuzzy Processing result and binary channels sign Fuzzy Processing result are analyzed by experiment.Comparing result is as shown in table 1.
Table 1
Infer and experimental analysis according to expert, determine a fuzzy relationship matrix r (remarks: through exchanging with expert, fuzzy matrix R renewal is as follows :)
According to blurring mapping formula b=aR, derive fault fuzzy vector, be subordinate to angle value accordingly as shown in table 2:
Table 2 (remarks: the data of table 2 upgrade as follows according to fuzzy matrix R)
| Uneven | Misalign | Bending | Oil whirl | Oil whip | |
| X is to frequency spectrum | 0.8534 | 0.5648 | 0.9769 | 0.8534 | 0.9460 |
| Y-direction frequency spectrum | 0 | 0.9156 | 0.3662 | 0 | 0.2747 |
| Vector spectrum | 0.5212 | 0.9175 | 0.8256 | 0.5212 | 0.7495 |
According to threshold value setting rule, X, to threshold value δ=0.9769/2=0.4885 corresponding to frequency spectrum, finally determines that X is flexural failure to fault most probable; In like manner, threshold value δ=0.4578 that Y-direction frequency spectrum is corresponding, finally determines that Y-direction fault most probable is for misaligning fault; Threshold value δ=0.4588 corresponding according to vector spectrum, final confirmation fault is for misaligning fault.
Known by comparative analysis, compared to single channel frequency spectrum sign blur method, extract prognostic information more comprehensively based on the vector spectrum sign blur method rising ridge shape distribution function, expert system fault diagnosis result reliability is higher.
Claims (6)
1. based on a failure symptom extracting method for vector spectrum, it is characterized in that, this extracting method comprises the following steps:
1) vibration data of orthogonal two passages in same cross section is gathered;
2) carry out vector spectrum filtering to the vibration data of gathered two passages, the master extracted on vector spectrogram under different frequency multiplication shakes vector value;
3) Fuzzy processing is carried out to the vector value that shakes of the master under extracted each frequency multiplication, determine the degree of membership of each frequency multiplication;
4) failure symptom information is extracted, to determine type belonging to vibration fault according to determined degree of membership.
2. the failure symptom extracting method based on vector spectrum according to claim 1, is characterized in that, described step 3) be adopt to rise the determination that ridge shape distribution function carries out degree of membership.
3. the failure symptom extracting method based on vector spectrum according to claim 2, is characterized in that, the described ridge shape distribution function that rises is:
Wherein x is vibration amplitude, a and b is the threshold value of different stage.
4. the failure symptom extracting method based on vector spectrum according to claim 3, is characterized in that, described step 4) in the failure symptom that extracts be that degree of membership is greater than fault corresponding to setting degree of membership threshold value.
5. the failure symptom extracting method based on vector spectrum according to claim 4, is characterized in that, described setting degree of membership threshold value is the half of maximum membership degree.
6. the failure symptom extracting method based on vector spectrum according to any one of claim 1-5, is characterized in that, described step 1) be realize measuring by arranging two mutually perpendicular sensors in same cross section.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105004363A (en) * | 2015-07-01 | 2015-10-28 | 长安大学 | Sensor performance on-line test device and method based on multi-threshold wavelet under strong interference |
| CN108051189A (en) * | 2017-11-20 | 2018-05-18 | 郑州工程技术学院 | A kind of rotary machine fault characteristic extraction method and device |
| CN108197756A (en) * | 2018-01-26 | 2018-06-22 | 河北工业大学 | AgSnO is determined based on fuzzy comprehensive evoluation2The method of the second mutually optimal granularity of contact material |
| CN110879586A (en) * | 2019-12-04 | 2020-03-13 | 江苏方天电力技术有限公司 | Phase modulator fault diagnosis and state monitoring method and system |
| CN112345248A (en) * | 2019-08-09 | 2021-02-09 | 郑州工程技术学院 | Fault diagnosis method and device for rolling bearing |
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105004363A (en) * | 2015-07-01 | 2015-10-28 | 长安大学 | Sensor performance on-line test device and method based on multi-threshold wavelet under strong interference |
| CN108051189A (en) * | 2017-11-20 | 2018-05-18 | 郑州工程技术学院 | A kind of rotary machine fault characteristic extraction method and device |
| CN108197756A (en) * | 2018-01-26 | 2018-06-22 | 河北工业大学 | AgSnO is determined based on fuzzy comprehensive evoluation2The method of the second mutually optimal granularity of contact material |
| CN112345248A (en) * | 2019-08-09 | 2021-02-09 | 郑州工程技术学院 | Fault diagnosis method and device for rolling bearing |
| CN112345248B (en) * | 2019-08-09 | 2022-11-25 | 郑州工程技术学院 | Fault diagnosis method and device for rolling bearing |
| CN110879586A (en) * | 2019-12-04 | 2020-03-13 | 江苏方天电力技术有限公司 | Phase modulator fault diagnosis and state monitoring method and system |
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Application publication date: 20150513 |