CN110188143A - A kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method - Google Patents
A kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method Download PDFInfo
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- CN110188143A CN110188143A CN201910270676.5A CN201910270676A CN110188143A CN 110188143 A CN110188143 A CN 110188143A CN 201910270676 A CN201910270676 A CN 201910270676A CN 110188143 A CN110188143 A CN 110188143A
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Abstract
The present invention provides a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic methods, and the state of air-introduced machine is characterized by different characteristic values, and characteristic value measuring point is arranged on air-introduced machine, and the detection data of each characteristic value is obtained by characteristic value measuring point.Present invention combination historical data and online data calculate Vibration Trouble of Induced Draft Fan encoded radio in real time, obtain the vibration fault of blower, carry out maintenance guidance using artificial intelligence technology according to online characteristic value data.The method of Vibration Trouble of Induced Draft Fan diagnosis in power plant provided by the invention, it is contemplated that the correlation between vibration performance Value Data realizes the on-line fault diagnosis of air-introduced machine state, provides foundation for the repair based on condition of component of air-introduced machine.
Description
Technical field
The present invention relates to a kind of methods of power plant Vibration Trouble of Induced Draft Fan diagnosis, belong to blower technical field.
Background technique
In power plant, air-introduced machine is one of seven big subsidiary engines, and operation conditions is directly related to the safety of power plant, stabilization
Property.According to the classifying importance to power plant, power station equipment is segmented into key equipment, necessaries and ancillary equipment, blower
It is listed in key equipment.Therefore, state evaluation is carried out to air-introduced machine to be extremely important.
Air-introduced machine mainly have it is uneven, misalign, loosen and the different vibration fault modes such as bearing fault.How to judge
Which kind of vibration fault occurs for air-introduced machine, and then carries out relevant overhaul plan, is always the problem for perplexing power plant staff.
Existing Vibration Trouble of Induced Draft Fan diagnostic techniques gives the Vibration Analysis method of air-introduced machine, but these technologies
It is most of to use frequency-domain analysis, orbit of shaft center method, it has the disadvantage in that
(1) analysis result accuracy rate tends to rely on the experience of professional technician and the familiarity to equipment, different
The staff of technical capability may obtain same result not consistent analysis result；
(2) frequency spectrum analysis method sample frequency is higher, and mass data is generated in the same time, be not readily accomplished calculate in real time and
On-line analysis judges, among the analysis after being often applied to failure, is unable to satisfy the requirement of inline diagnosis Vibration Trouble of Induced Draft Fan.
Summary of the invention
The object of the present invention is to provide a kind of method for capableing of real-time online completion Vibration Trouble of Induced Draft Fan diagnosis, guidance inspections
It repairs personnel and accurately carries out troubleshooting.
In order to achieve the above object, the technical solution of the present invention is to provide a kind of diagnosis of power plant Vibration Trouble of Induced Draft Fan
Method characterizes the state of air-introduced machine by different characteristic values, and characteristic value measuring point is arranged on air-introduced machine, passes through characteristic value
Measuring point obtains the detection data of each characteristic value, which is characterized in that the vibrating failure diagnosis method includes the following steps:
Step 1, in the operation of power plant pressure fan and during maintenance down, characteristic value measuring point data saves in real time, is formed
The characteristic value measuring point data of the history of each characteristic value；
Step 2 filters out abnormal data in previous step characteristic value measuring point data obtained, and abnormal data is carried out
Interpolation replacement processing, in which: the characteristic value measuring point data for the characteristic value X that previous step obtains is defined as { x1, x2..., xn, it is special
Value indicative measuring point data { x1, x2..., xnIn abnormal data be defined as xa, then by xaValue useValue is calculated
It replaces, in formula, xiIndicate ith feature value measuring point data；
The characteristic value measuring point data of each characteristic value obtained in the previous step is normalized in step 3, in which: will
The characteristic value measuring point data for the characteristic value X that previous step obtains is defined as { x1, x2..., xn, characteristic value measuring point data { x1,
x2..., xnNormalization data be { x1z, x2z..., xnz, normalization data { x1z, x2z..., xnzIn i-th normalization
Data definition is xiz, then have:In formula, xiIndicate ith feature value measuring point data；μ indicates characteristic value measuring point data
{x1, x2..., xnMean value；σ indicates characteristic value measuring point data { x1, x2..., xnStandard deviation；
Step 4, the online characteristic value measuring point number calculated during air-introduced machine is on active service by history obtained in the previous step in real time
According to variation tendency, obtain characteristic value correlation arrange, to extremely relevant variable carry out simplify processing, reduce characteristic value measuring point
The input quantity of data, in which: the correlation of characteristic value X and characteristic value Y are defined as ρX, Y, then have:
In formula,Indicate the mean value of characteristic value X,The mean value for indicating characteristic value Y, if ρX, Y∈ [0.8,1.0), then characteristic value X and feature
The extremely strong correlation of value Y chooses any feature value as input in characteristic value X and characteristic value Y；If ρX, Y∈ [0.6,0.8), feature
Value X and characteristic value Y strong correlation, required precision it is not high, in the demanding situation of calculated performance, in characteristic value X and characteristic value Y
Middle selection any feature value is as input；If ρX, Y∈ [0.4,0.6), characteristic value X is moderate related to characteristic value Y；If ρX, Y
∈ [0.2,0.4), characteristic value X is weak related to characteristic value Y；If ρX, Y∈ [0,0.2), characteristic value X and characteristic value Y it is extremely weak related or
Without correlation；
Step 5, the air-introduced machine malfunction history data provided using power plant, encode each vibration fault, calculate
The characteristic value measuring point data of the history obtained in the previous step change curve with corresponding vibration fault at any time, establish each vibration therefore
The disaggregated model of barrier and the characteristic value measuring point data of history；
Step 6, the real-time characteristic value measuring point data that each characteristic value is obtained by characteristic value measuring point, utilize step 2
Method carries out interpolation replacement processing to the abnormal data in characteristic value measuring point data, followed by the method for step 3 to real-time
Characteristic value measuring point data is normalized；
Step 7, power plant air-introduced machine be on active service during, using step 5 establish disaggregated model, obtained according to step 6
Characteristic value measuring point data, in real time calculate vibration fault encoded radio, to obtain corresponding vibration fault.
Preferably, it in step 2, is filtered out using La Yida method different in previous step characteristic value measuring point data obtained
Regular data.
Preferably, the characteristic value includes ball bearing temperature, drive end bearing temperature, non-driven-end bearing temperature, electricity
Machine stator A phase temperature, motor stator B phase temperature, motor stator C phase temperature, electric current.
Preferably, the characteristic value further includes bearing X-direction ISO10816 value, bearing Y-direction ISO10816 value and bearing Z
Direction ISO10816 value.
Preferably, the characteristic value further includes that bearing X-direction accelerated speed effective value, bearing X-direction envelope linear velocity are effective
Value, bearing X-direction high frequency peaks, bearing X-direction high frequency virtual value, bearing X-direction envelope accelerated speed effective value, the bearing side X
To peak factor, bearing X-direction high frequency peaks factor, bearing X-direction deflection angle value, bearing X-direction kurtosis value, bearing Y-direction
Accelerated speed effective value, bearing Y-direction envelope linear velocity virtual value, bearing Y-direction high frequency peaks, bearing Y-direction high frequency virtual value,
Bearing Y-direction envelope accelerated speed effective value, bearing Y-direction peak factor, bearing Y-direction high frequency peaks factor, bearing Y-direction
Deflection angle value, bearing Y-direction kurtosis value, bearing Z-direction accelerated speed effective value, bearing Z-direction envelope linear velocity virtual value, bearing
Z-direction high frequency peaks, bearing Z-direction high frequency virtual value, bearing Z-direction envelope accelerated speed effective value, bearing Z-direction peak value because
Number, bearing Z-direction high frequency peaks factor, bearing Z-direction deflection angle value, bearing Z-direction kurtosis value.
Preferably, it in step 5, when establishing the disaggregated model of the characteristic value measuring point data of each vibration fault and history, uses
Disaggregated model is stored in artificial intelligence calculation server by artificial intelligence technology.
Preferably, the artificial intelligence technology is artificial neural network.
Preferably, the artificial neural network is Recognition with Recurrent Neural Network.
Preferably, the Recognition with Recurrent Neural Network contains input layer, 2 hidden layers and output layer using 4 layers of structure.
Present invention combination historical data and online data, using artificial intelligence technology, according to online characteristic value data, in real time
Vibration Trouble of Induced Draft Fan encoded radio is calculated, the vibration fault of blower is obtained, carries out maintenance guidance.Draw in power plant provided by the invention
The method of Trouble Dagnosis for Fan Vibration, it is contemplated that the correlation between vibration performance Value Data, realize air-introduced machine state
Line fault diagnosis provides foundation for the repair based on condition of component of air-introduced machine.
Compared with prior art, advantages of the present invention are as follows:
(1) artificial intelligence technology is used, is calculated from air-introduced machine historical failure and the mapping relations of history feature Value Data
Disaggregated model between the two out realizes the high-precision of Vibration Trouble of Induced Draft Fan coding in line computation；
(2) real-time and accurately judge the vibration fault of air-introduced machine, automate guided maintenance trouble unit, reduce service personnel
It is required that the repair based on condition of component for air-introduced machine provides foundation；
(3) system sampling frequency is settable, input of the day frequently with low frequency signal as disaggregated model, ensures and calculates quickly
Property, when needing to further confirm that result, real-time calling high-frequency signal ensures as input and calculates accuracy, flexibly adjustable.
Detailed description of the invention
Fig. 1 is the block diagram of the system of power plant Vibration Trouble of Induced Draft Fan of the present invention diagnosis；
Fig. 2 is the flow chart of the method for power plant Vibration Trouble of Induced Draft Fan of the present invention diagnosis；
Fig. 3 is the computer software block diagram that the present invention uses；
Fig. 4 is the partial content of certain model pressure fan malfunction coding list.
Specific embodiment
In order to make the present invention more obvious and understandable, hereby with preferred embodiment, and attached drawing is cooperated to be described in detail below.
Fig. 1 is the block diagram of the system of power plant Vibration Trouble of Induced Draft Fan of the present invention diagnosis, the power plant air-introduced machine
The system of vibrating failure diagnosis is taken by database server 3, characteristic value data preprocessing server 4 and artificial intelligence failure modes
Business device 5 forms, and the data source of database server 3 is history feature Value Data 1 and online characteristic value data 2.
It is illustrated in figure 2 the flow chart of the method for Vibration Trouble of Induced Draft Fan diagnosis in power plant of the present invention, is illustrated in figure 3 this
Software block diagram used by inventing, the software installation is in database server 3, characteristic value data preprocessing server 4 and artificial
Inline diagnosis on intelligent trouble classified service device 5, applied to power plant Vibration Trouble of Induced Draft Fan.
Embodiment 1
To Mr. Yu's model air-introduced machine, this air-introduced machine during one's term of military service, using device shown in FIG. 1, stream shown in Fig. 2
Cheng Tu, computer software shown in Fig. 3 and malfunction coding table shown in Fig. 4, in the process of running, discovery air-introduced machine have vibration simultaneously
There is noise.
Step 1: online characteristic value data typing characteristic value data preprocessing server, completes data outliers interpolation；
Step 2: standardization carries out the online characteristic value data after exceptional value interpolation；
Step 3: the online characteristic value data after standardization is inputted artificial intelligence failure modes server, vibration is utilized
The disaggregated model of failure and history feature Value Data calculates the characteristic value data input disaggregated model of above-mentioned steps.
Step 4: vibration fault encoded radio H is calculated0=1.26, it is obtained according to calculation formula H=Round (1.26)=1
To vibration fault H, air-introduced machine malfunction coding table is inquired, it is known that air-introduced machine is calculated for disaggregated model happens is that revolving speed near critical
Resonance failure caused by revolving speed, vibration fault belong to blade and wheel rotation component.
Embodiment 2
To Mr. Yu's model air-introduced machine, this air-introduced machine during one's term of military service, using device shown in FIG. 1, stream shown in Fig. 2
Cheng Tu, computer software shown in Fig. 3 and malfunction coding table shown in Fig. 4 find air-introduced machine bearing temperature in the process of running
Rise.
Step 1: online characteristic value data typing characteristic value data preprocessing server, completes data outliers interpolation；
Step 2: standardization carries out the online characteristic value data after exceptional value interpolation；
Step 3: the online characteristic value data after standardization is inputted artificial intelligence failure modes server, vibration is utilized
The disaggregated model of failure and history feature Value Data calculates the characteristic value data input disaggregated model of above-mentioned steps.
Step 4: vibration fault encoded radio H is calculated0=1.91, it is obtained according to calculation formula H=Round (1.91)=2
To vibration fault H, air-introduced machine malfunction coding table is inquired, it is known that air-introduced machine is calculated for disaggregated model happens is that rolling bearing rolls
Axis insufficient lubrication failure, vibration fault belong to parts of bearings.
Embodiment 3
To Mr. Yu's model air-introduced machine, this air-introduced machine during one's term of military service, using device shown in FIG. 1, stream shown in Fig. 2
Cheng Tu, computer software shown in Fig. 3 and malfunction coding table shown in Fig. 4 find energy at air-introduced machine casing in the process of running
Hear slight metal friction sound.
Step 1: online characteristic value data typing characteristic value data preprocessing server, completes data outliers interpolation；
Step 2: standardization carries out the online characteristic value data after exceptional value interpolation；
Step 3: the online characteristic value data after standardization is inputted artificial intelligence failure modes server, vibration is utilized
The disaggregated model of failure and history feature Value Data calculates the characteristic value data input disaggregated model of above-mentioned steps.
Step 4: vibration fault encoded radio H is calculated0=3.17, it is obtained according to calculation formula H=Round (3.17)=3
To vibration fault H, air-introduced machine malfunction coding table is inquired, it is known that air-introduced machine is calculated for disaggregated model happens is that blade and casing
Rubbing faults, vibration fault belong to blade and wheel rotation component.
Embodiment 4
To Mr. Yu's model air-introduced machine, this air-introduced machine during one's term of military service, using device shown in FIG. 1, stream shown in Fig. 2
Cheng Tu, computer software shown in Fig. 3 and malfunction coding table shown in Fig. 4 find energy at air-introduced machine casing in the process of running
Hear slight metal friction sound.
Step 1: online characteristic value data typing characteristic value data preprocessing server, completes data outliers interpolation；
Step 2: standardization carries out the online characteristic value data after exceptional value interpolation；
Step 3: the online characteristic value data after standardization is inputted artificial intelligence failure modes server, vibration is utilized
The disaggregated model of failure and history feature Value Data calculates the characteristic value data input disaggregated model of above-mentioned steps.
Step 4: vibration fault encoded radio H is calculated0=4.38, it is obtained according to calculation formula H=Round (4.38)=4
To vibration fault H, air-introduced machine malfunction coding table is inquired, it is known that air-introduced machine is calculated for disaggregated model happens is that bearing inner race is loose
Dynamic failure, vibration fault belong to parts of bearings.
The system and method diagnosed using power plant Vibration Trouble of Induced Draft Fan provided by the invention, quantitatively calculates vibration online
Dynamic malfunction coding value, and then judge the vibration fault that air-introduced machine occurs, foundation is provided for the safe operation of power plant air-introduced machine.
The above, only presently preferred embodiments of the present invention, not to the present invention in any form with substantial limitation,
It should be pointed out that under the premise of not departing from the method for the present invention, can also be made for those skilled in the art
Several improvement and supplement, these are improved and supplement also should be regarded as protection scope of the present invention.All those skilled in the art,
Without departing from the spirit and scope of the present invention, when made using disclosed above technology contents it is a little more
Dynamic, modification and the equivalent variations developed, are equivalent embodiment of the invention；Meanwhile all substantial technologicals pair according to the present invention
The variation, modification and evolution of any equivalent variations made by above-described embodiment, still fall within the range of technical solution of the present invention
It is interior.
Claims (9)
1. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method characterizes the state of air-introduced machine by different characteristic values, and
Characteristic value measuring point is set on air-introduced machine, the detection data of each characteristic value is obtained by characteristic value measuring point, which is characterized in that institute
Vibrating failure diagnosis method is stated to include the following steps:
Step 1, in the operation of power plant pressure fan and during maintenance down, characteristic value measuring point data saves in real time, is formed each
The characteristic value measuring point data of the history of characteristic value；
Step 2, brush select the abnormal data in previous step characteristic value measuring point data obtained, carry out interpolation for abnormal data
Replacement processing, in which: the characteristic value measuring point data for the characteristic value X that previous step obtains is defined as { x1, x2..., xn, characteristic value
Measuring point data { x1, x2..., xnIn abnormal data be defined as xa, then by xaValue useValue replacement is calculated,
In formula, xiIndicate ith feature value measuring point data；
The characteristic value measuring point data of each characteristic value obtained in the previous step is normalized in step 3, in which: by upper one
The characteristic value measuring point data for walking the characteristic value X obtained is defined as { x1, x2..., xn, characteristic value measuring point data { x1, x2...,
xnNormalization data be { x1z, x2z..., xnz, normalization data { x1z, x2z..., xnzIn i-th normalization data it is fixed
Justice is xiz, then have:In formula, xiIndicate ith feature value measuring point data；μ indicates characteristic value measuring point data { x1,
x2..., xnMean value；σ indicates characteristic value measuring point data { x1, x2..., xnStandard deviation；
Step 4, the online air-introduced machine that calculates in real time are on active service in the process through the characteristic value measuring point data of history obtained in the previous step
Variation tendency show that characteristic value correlation arranges, and carries out simplifying processing to extremely relevant variable, reduces characteristic value measuring point data
Input quantity, in which: the correlation of characteristic value X and characteristic value Y are defined as ρX, Y, then have:Formula
In,Indicate the mean value of characteristic value X,The mean value for indicating characteristic value Y, if ρX, Y∈ [0.8,1.0), then characteristic value X and characteristic value Y
Extremely strong correlation chooses any feature value as input in characteristic value X and characteristic value Y；If ρX, Y∈ [0.6,0.8), characteristic value X
With characteristic value Y strong correlation, required precision it is not high, in the demanding situation of calculated performance, in characteristic value X and characteristic value Y
Any feature value is chosen as input；If ρX, Y∈ [0.4,0.6), characteristic value X is moderate related to characteristic value Y；If ρX, Y∈
[02,0.4), characteristic value X is weak related to characteristic value Y；If ρX, Y∈ [0,0.2), characteristic value X and characteristic value Y be extremely weak related or nothing
It is related；
Step 5, the air-introduced machine malfunction history data provided using power plant, encode each vibration fault, calculate upper one
Walk the obtained characteristic value measuring point data of the history change curve with corresponding vibration fault at any time, establish each vibration fault with
The disaggregated model of the characteristic value measuring point data of history；
Step 6, the real-time characteristic value measuring point data that each characteristic value is obtained by characteristic value measuring point, using the method for step 2,
Interpolation replacement processing is carried out to the abnormal data in characteristic value measuring point data, followed by the method for step 3 to real-time feature
Value measuring point data is normalized；
Step 7, power plant air-introduced machine be on active service during, using step 5 establish disaggregated model, the spy obtained according to step 6
Value indicative measuring point data calculates vibration fault encoded radio, to obtain corresponding vibration fault in real time.
2. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method as described in claim 1, which is characterized in that in step 2, benefit
The abnormal data in previous step characteristic value measuring point data obtained is selected with La Yida method brush.
3. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method as described in claim 1, which is characterized in that the characteristic value
Including ball bearing temperature, drive end bearing temperature, non-driven-end bearing temperature, motor stator A phase temperature, motor stator B phase
Temperature, motor stator C phase temperature, electric current.
4. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method as claimed in claim 3, which is characterized in that the characteristic value
It further include bearing X-direction ISO10816 value, bearing Y-direction ISO10816 value and bearing Z-direction ISO10816 value.
5. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method as described in claim 1, which is characterized in that the characteristic value
It further include bearing X-direction accelerated speed effective value, bearing X-direction envelope linear velocity virtual value, bearing X-direction high frequency peaks, bearing X
Direction high frequency virtual value, bearing X-direction envelope accelerated speed effective value, bearing X-direction peak factor, bearing X-direction high frequency peak
Q factor, bearing X-direction deflection angle value, bearing X-direction kurtosis value, bearing Y-direction accelerated speed effective value, bearing Y-direction envelope
Linear velocity virtual value, bearing Y-direction high frequency peaks, bearing Y-direction high frequency virtual value, bearing Y-direction envelope linear acceleration are effective
Value, bearing Y-direction peak factor, bearing Y-direction high frequency peaks factor, bearing Y-direction deflection angle value, bearing Y-direction kurtosis value,
Bearing Z-direction accelerated speed effective value, bearing Z-direction envelope linear velocity virtual value, bearing Z-direction high frequency peaks, bearing Z-direction are high
Frequency virtual value, bearing Z-direction envelope accelerated speed effective value, bearing Z-direction peak factor, bearing Z-direction high frequency peaks factor,
Bearing Z-direction deflection angle value, bearing Z-direction kurtosis value.
6. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method as described in claim 1, which is characterized in that in step 5, build
When founding the disaggregated model of the characteristic value measuring point data of each vibration fault and history, using artificial intelligence technology, disaggregated model is deposited
Enter artificial intelligence calculation server.
7. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method as claimed in claim 6, which is characterized in that the artificial intelligence
Energy technology is artificial neural network.
8. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method as claimed in claim 7, which is characterized in that the artificial mind
It is Recognition with Recurrent Neural Network through network.
9. a kind of power plant Vibration Trouble of Induced Draft Fan diagnostic method as claimed in claim 8, which is characterized in that the circulation mind
Input layer, 2 hidden layers and output layer are contained using 4 layers of structure through network.
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| CN112529036A (en) * | 2020-11-06 | 2021-03-19 | 上海发电设备成套设计研究院有限责任公司 | Fault early warning method, device, equipment and storage medium |
| CN112525334A (en) * | 2020-11-18 | 2021-03-19 | 西安因联信息科技有限公司 | Dynamic equipment vibration multistable detection method |
| CN113554085A (en) * | 2021-07-20 | 2021-10-26 | 云南电力试验研究院(集团)有限公司 | System and method for sensing vibration safety situation of induced draft fan |
| CN113554085B (en) * | 2021-07-20 | 2023-04-18 | 云南电力试验研究院(集团)有限公司 | System and method for sensing vibration safety situation of induced draft fan |
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