CN108709426A - It is leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature - Google Patents
It is leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature Download PDFInfo
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- CN108709426A CN108709426A CN201810556742.0A CN201810556742A CN108709426A CN 108709426 A CN108709426 A CN 108709426A CN 201810556742 A CN201810556742 A CN 201810556742A CN 108709426 A CN108709426 A CN 108709426A
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- sintering machine
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F27—FURNACES; KILNS; OVENS; RETORTS
- F27B—FURNACES, KILNS, OVENS, OR RETORTS IN GENERAL; OPEN SINTERING OR LIKE APPARATUS
- F27B21/00—Open or uncovered sintering apparatus; Other heat-treatment apparatus of like construction
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Abstract
The present invention discloses a kind of to be leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature.The method includes the following steps：The sintering machine based on frequecy characteristic is established to leak out the offline diagnostic model of failure；Fault diagnosis is carried out to sintering mill (plant) sound collection data online based on offline diagnostic model.This method utilizes the frequency spectrum and strength characteristics of sound, constructs a kind of sintering machine and leaks out the character representation method of failure, and with this feature representation method, and the foundation that can be monitored is provided for sintering machine failure of leaking out；It by calculating the intensity threshold in characteristic frequency, defines a kind of sintering machine for numerical computations and leaks out failure criterion, which is that sintering machine leaks out the basis that failure detects automatically；It is leaked out to sintering machine by way of failure criterion diagnoses decision-making mechanism, which provides intelligentized basis for estimation for sintering machine fault diagnosis of leaking out.
Description
Technical field：
It is leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature the present invention relates to a kind of.
Background technology：
Iron ore sintering is the important link in modern steel metallurgical process, and main production link is burnt in car-type
By uniformly mixed fine granularity raw material ignition on knot machine, it is made to fuse into blocky sinter.To ensure sinter junction
The intensity and chemical composition of block, need raw material to be fully burned on sintering machine.The fine granularity raw material being mixed evenly
It is equally distributed in the form of deep bed sintering on sintering machine, to ensure the abundant burning of raw material, sintering machine can be below raw material layer
Setting bellows simultaneously connect exhaust fan, and fresh air is brought into the raw material layer of burning by the negative pressure generated by exhaust fan.Due to
The sealing structure of sintering machine and the maintenance problem of long-time service will produce air leakage phenomena in sintering production process, lead to burning not
Abundant and the exhaust fan energy the waste in the case of seriously leaking out, or even will produce high-temperature particle object and trickle down, causes production thing
Therefore.The detection of air leak rate of air curtain is set to lack effective means always.
Due to sintering machine and its attached bellows, air hose, valve etc., due to complicated, and it is operated in the condition of high temperature, meeting
Cause to leak out the easy hair of failure and multiple.It says in other words, sintering machine is easy to happen failure of leaking out, but due to the failure
It is related to the air-tightness of structure, for large scale equipment, fault point and failure cause are very more and mechanism is different, so very
Difficulty carries out comprehensive monitoring to sintering machine failure of leaking out.
Invention content
The present invention provides a kind of to be leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature.
In order to achieve the above objectives, it leaks out on-line fault diagnosis side the present invention is based on the bilateral detection method sintering machine of spectrum signature
Method, the method include the following steps：
The sintering machine based on frequecy characteristic is established to leak out the offline diagnostic model of failure；
Fault diagnosis is carried out to sintering mill (plant) sound collection data online based on offline diagnostic model.
Wherein, the method includes the following steps：
Establishing the leak out method of offline diagnostic model of failure of the sintering machine based on frequecy characteristic includes：
21) sintering mill (plant) normal work spectral samples collection and the fault spectrum sample set that leaks out are made；
22) leak out fault spectrum sample set and the strength difference of normal spectral samples collection on different frequency bands are compared, according to
Descending selects the maximum k frequency range of strength difference as the fault characteristic frequency collection that leaks out and is denoted as F={ f1,…,fk, wherein fi
For ith feature frequency；
23) normal sample intensity distribution in characteristic spectra is analyzed, the method estimated using statistical parameter, according to manually setting
Fixed significance α calculates normal sample in specific frequency fiIntensity distribution confidence upper limit UCLi, by UCLiAs this feature frequency
Corresponding intensity threshold is denoted as TH={ th to obtain the corresponding intensity threshold collection of characteristic frequency1,…,thk}；
24) contribution rate of each characteristic frequency to failure of leaking out is defined, Ω={ ω is denoted as1,…,ωk, by characteristic frequency fi
The failure contributory index that leaks out at place is defined as expression formula ci=G (si,thi,ωi), defining sintering machine failure criterion of leaking out isWherein siIt is current sample in characteristic frequency fiThe intensity of sound at place.
According to training dataset, by genetic algorithm or Artificial Neural Network, optimize and calculate each characteristic frequency pair
The contribution rate Ω={ ω for failure of leaking out1,…,ωk, the optimized parameter of failure criterion C so that it is determined that sintering machine leaks out.
Wherein, include to the method for sintering mill (plant) sound collection data progress fault diagnosis online based on offline diagnostic model
Following step
31) voice data is acquired in real time in sintering mill (plant), by sampling and denoising, be prepared into online sample；
32) spectrum analysis is carried out in real time to online sample, obtains the corresponding intensity of sound of fault characteristic frequency and integrates as Sj=
{sj1,…,sjk}；
Wherein, j is the serial number of current sample, and i is characterized frequency serial number, sjiFor the corresponding sound of current sample ith feature
Loudness of a sound degree；
33) current sample is calculated in characteristic frequency fiThe failure contributory index c that leaks out at placeji=G (sji,thi,ωi), it will be every
A characteristic frequency is corresponding, and the failure contributory index that leaks out is cumulative, and the sintering machine for obtaining j-th of online sample leaks out failure criterion
34) it is leaked out failure criterion C using sintering machinejSintering machine is examined in j-th of moment failure of whether leaking out
Disconnected, the various ways such as the threshold determination that may be used or fuzzy decision leak out the decision making approach of fault diagnosis as sintering machine.
Wherein, make sintering mill (plant) normal work spectral samples collection and leak out fault spectrum sample set the step of include：
41) the sound number in the case of sintering mill (plant) voice data and various differences under normal production status leak out is acquired
According to, by sampling and denoising, be prepared into off-line modeling sample, according to sintering fault condition, sample set is divided into normal data
Collect XnormalWith the fault data collection X that leaks outfault；
42) to normal data set XnormalWith the fault data collection X that leaks outfaultTime-frequency domain conversion is carried out respectively, is obtained just
Normal frequency spectrums of operation sample set and the fault spectrum sample set that leaks out.
The method of the above-mentioned present invention has the advantage that：
1. due to sintering machine leak out failure fault point it is more, the inducement that failure occurs is complicated, so it is difficult to by traditional
Monitoring method realizes effective on-line automatic fault diagnosis, and sintering machine proposed by the present invention leaks out on-line fault diagnosis method,
It is the malfunction monitoring that leaks out of this whole large scale equipment of sintering machine by the way that the voice signal easily obtained is acquired and is analyzed
Provide a convenient easy method.
2. the present invention utilizes the frequency spectrum and strength characteristics of sound, the character representation method of construction to leak out failure for sintering machine
Provide the foundation that can be monitored；
3. the present invention by calculating the intensity threshold in characteristic frequency, define the sintering machine for numerical computations leak out therefore
Hinder criterion, computable index is provided for the sintering machine failure automatic decision that leaks out；
4. the present invention leaks out to sintering machine decision-making mechanism by way of failure criterion diagnoses, leak out for sintering machine therefore
The Intelligence Diagnosis of barrier provides effective means；
The on-line fault diagnosis method 5. sintering machine proposed by the present invention leaks out, the side being combined by sound spectrum and intensity
Formula, constitutes the novel criterion of sintering machine air leak test, and parameter optimization and statistical check of the criterion by historical data improve
Reliability.
The on-line fault diagnosis method 6. sintering machine proposed by the present invention leaks out, inline diagnosis process only need simply to solve
Analysis formula calculates, and computation complexity is low, and real-time performance is good, and the computing resource and memory source occupancy to hardware system are all very low,
Implementation cost is low.
The on-line fault diagnosis method 7. the sintering machine proposed through the invention leaks out can timely and effectively leak sintering machine
Wind failure carries out the inline diagnosis of unattended formula, is conducive to the stability for improving sintering combustion process, reduces failure rate, improves
Sinter final product quality.
The on-line fault diagnosis method 8. the sintering machine proposed through the invention leaks out, can find in sintering production in time
Air leakage phenomena, to reduce, main exhauster of sintering is energy-saving and equipment repair and maintenance provide crucial detection and differentiate means.
Description of the drawings
The detection of Fig. 1 present invention and hardware system schematic diagram
The total working flow chart of Fig. 2 present invention
The sintering machine based on frequecy characteristic of Fig. 3 present invention leaks out failure off-line modeling flow chart
The sintering machine based on frequecy characteristic of Fig. 4 present invention leaks out on-line fault diagnosis flow chart
Specific implementation mode
The characteristic for having spread speed soon the present invention is based on voice signal and being not easy to be blocked, and passed through according to long-term production
Test summary, a leak out important phenomenon of failure of sintering machine is exactly that will produce wind to pass through howling caused by narrow space, this
Leak out generation sound it is generally relatively sharp and factory's background sound has certain discrimination, so the present invention is according to sintering machine
This feature of air leakage phenomena, using the method analyzed workshop voice signal, realize sintering machine leak out failure synthesis it is online
Detection and fault diagnosis.
Now by taking domestic typical car-type sintering machine as an example, the present invention will be described：
Fig. 1 is shown in the detection and hardware system of the present invention.Be sintered it is mobile trolley used in be the mixed raw material burnt, under trolley
Portion is to extract air to meet the blower fan system that burning needs, and main includes sintering bellows, sintering flue and main exhauster etc..By
Be spliced in the independent trolley that trolley is several movable types, thus between trolley and trolley, between bellows and trolley there are gap and
Flexible connection, this results in being easy to generate air leakage phenomena in various different parts, and is difficult to through being sealed property of effective means
Detection.Sintering machine proposed by the present invention leaks out on-line fault diagnosis method, the problem of being difficult to detect for air leakage phenomena, it is proposed that
By way of the sound collection of sintering mill (plant), to leak out, failure provides basis for estimation.As shown, workshop is pacified where sintering machine
Several sound signal collecting devices are filled, sensed signal sources are provided for on-line fault diagnosis as spot sensor.Pass through offline failure
Modeling Server analyzes the historical data of workshop voice signal, structure sintering machine leak out failure diagnostic model and progress
Parameter optimization.Sintering production real time data is analyzed using the fault model that leaks out of optimization by the way that server is monitored online
And fault diagnosis.
The workflow of the present invention is shown in Fig. 2.This method is broadly divided into two phases of offline fault modeling and on-line fault diagnosis
Associated part.The implementation in the present invention offline fault modeling stage is as follows：First, to the workshop of sintering production under each operating mode
Voice data is collected and arranges, and obtains workshop sound historical sample library；Then workshop sound historical sample library is divided
Analysis and modeling obtain being sintered the fault model that leaks out.The implementation in on-line fault diagnosis stage of the present invention is as follows：First, to reality
When acquisition sintering production workshop voice data sampled and analyzed, obtain online production sample；Then it is leaked out by sintering
Fault model is monitored and calculates to online production sample；Finally, by failure decision-making mechanism, to the conclusion of fault model into
Row is final to be judged, on-line fault diagnosis conclusion is provided.
The leak out flow chart in failure off-line modeling stage of sintering machine based on frequecy characteristic is shown in Fig. 3：
The first step：Acquire the sound in the case of sintering mill (plant) voice data and various differences under normal production status leak out
Data are prepared into off-line modeling sample by sampling and denoising, and according to sintering fault condition, sample set is divided into normal number
According to collection XnormalWith the fault data collection X that leaks outfault；
Second step：To normal data set XnormalWith the fault data collection X that leaks outfaultTime-frequency domain conversion is carried out respectively, is obtained
To normal work spectral samples collection and the fault spectrum sample set that leaks out；
Third walks：Leak out fault spectrum sample set and the strength difference of normal spectral samples collection on different frequency bands are compared,
The maximum k frequency range of strength difference, which is selected, as the fault characteristic frequency collection that leaks out according to descending is denoted as F={ f1,…,fk,
In, fiFor ith feature frequency；
4th step：Normal sample intensity distribution in characteristic spectra is analyzed, the method estimated using statistical parameter, according to people
The significance α of work setting calculates normal sample in specific frequency fiIntensity distribution confidence upper limit UCLi, by UCLiAs this feature
The corresponding intensity threshold of frequency is denoted as TH={ th to obtain the corresponding intensity threshold collection of characteristic frequency1,…,thk}；
5th step：Contribution rate of each characteristic frequency to failure of leaking out is defined, Ω={ ω is denoted as1,…,ωk, by feature frequency
Rate fiThe failure contributory index that leaks out at place is defined as expression formula ci=G (si,thi,ωi), defining sintering machine failure criterion of leaking out isWherein siIt is current sample in characteristic frequency fiThe intensity of sound at place.Setting is calculated according to training dataset by heredity
The methods of method, artificial neural network, the contribution rate Ω={ ω for optimizing and calculating each characteristic frequency to failure of leaking out1,…,ωk, from
And determine that sintering machine leaks out the optimized parameter of failure criterion C；Wherein, as one embodiment：
By above 5 steps, just establishes the sintering machine based on frequecy characteristic and leak out the offline diagnostic model of failure.
The leak out flow chart in on-line fault diagnosis stage of sintering machine based on frequecy characteristic is shown in Fig. 4：
The first step：Voice data is acquired in real time in sintering mill (plant), by sampling and denoising, is prepared into online sample；
Second step：Spectrum analysis is carried out in real time to online sample, obtaining the corresponding intensity of sound collection of fault characteristic frequency is
Sj={ sj1,…,sjk, wherein j is the serial number of current sample, and i is characterized frequency serial number, sjiFor current sample ith feature
Corresponding intensity of sound；
Third walks：Current sample is calculated in characteristic frequency fiThe failure contributory index c that leaks out at placeji=G (sji,thi,ωi),
The corresponding failure contributory index that leaks out of each characteristic frequency is added up, the sintering machine failure of leaking out for obtaining j-th of online sample is sentenced
According toWherein, as one embodiment：
4th step：It is leaked out failure criterion C using sintering machinejTo sintering machine j-th of moment whether leak out failure into
Row diagnosis, the various ways such as the threshold determination that may be used or fuzzy decision leak out the decision hand of fault diagnosis as sintering machine
Section.
By above 4 steps, it is achieved that the sintering machine based on frequecy characteristic leaks out the inline diagnosis of failure.
More than, only presently preferred embodiments of the present invention, but scope of protection of the present invention is not limited thereto, and it is any to be familiar with sheet
In the technical scope disclosed by the present invention, the change or replacement that can be readily occurred in should all be covered those skilled in the art
Within protection scope of the present invention.Therefore, the scope of protection of the present invention shall be subject to the scope of protection defined by the claims.
Claims (6)
1. a kind of leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature, which is characterized in that described
Method includes the following steps：
The sintering machine based on frequecy characteristic is established to leak out the offline diagnostic model of failure；
Fault diagnosis is carried out to sintering mill (plant) sound collection data online based on offline diagnostic model.
2. it is leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature as described in claim 1, spy
Sign is, establishes the leak out method of offline diagnostic model of failure of the sintering machine based on frequecy characteristic and includes：
21) sintering mill (plant) normal work spectral samples collection and the fault spectrum sample set that leaks out are made；
22) leak out fault spectrum sample set and the strength difference of normal spectral samples collection on different frequency bands are compared, according to descending
The maximum k frequency range of strength difference is selected as the fault characteristic frequency collection that leaks out and is denoted as F={ f1,…,fk, wherein fiIt is
I characteristic frequency；
23) normal sample intensity distribution in characteristic spectra is analyzed, the method estimated using statistical parameter, according to what is manually set
Significance α calculates normal sample in specific frequency fiIntensity distribution confidence upper limit UCLi, by UCLiIt is corresponded to as this feature frequency
Intensity threshold be denoted as TH={ th to obtain the corresponding intensity threshold collection of characteristic frequency1,…,thk}；
24) contribution rate of each characteristic frequency to failure of leaking out is defined, Ω={ ω is denoted as1,…,ωk, by characteristic frequency fiPlace
Failure of leaking out contributory index is defined as expression formula ci=G (si,thi,ωi), defining sintering machine failure criterion of leaking out isWherein siIt is current sample in characteristic frequency fiThe intensity of sound at place.
3. it is leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature as described in claim 1, spy
Sign is that the method for carrying out fault diagnosis to sintering mill (plant) sound collection data online based on offline diagnostic model includes following steps
Suddenly
31) voice data is acquired in real time in sintering mill (plant), by sampling and denoising, be prepared into online sample；
32) spectrum analysis is carried out in real time to online sample, obtains the corresponding intensity of sound of fault characteristic frequency and integrates as Sj=
{sj1,…,sjk}；
Wherein, j is the serial number of current sample, and i is characterized frequency serial number, sjiIt is strong for the corresponding sound of current sample ith feature
Degree；
33) current sample is calculated in characteristic frequency fiThe failure contributory index c that leaks out at placeji=G (sji,thi,ωi), it will be each special
The corresponding failure contributory index that leaks out of sign frequency is cumulative, and the sintering machine for obtaining j-th of online sample leaks out failure criterion
34) it is leaked out failure criterion C using sintering machinejSintering machine is diagnosed in j-th of moment failure of whether leaking out.
4. it is leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature as claimed in claim 2, spy
Sign is, make sintering mill (plant) normal work spectral samples collection and leak out fault spectrum sample set the step of include：
41) voice data in the case of sintering mill (plant) voice data and various differences under normal production status leak out is acquired, is led to
Over-sampling and denoising are prepared into off-line modeling sample, and according to sintering fault condition, sample set is divided into normal data set
XnormalWith the fault data collection X that leaks outfault；
42) to normal data set XnormalWith the fault data collection X that leaks outfaultTime-frequency domain conversion is carried out respectively, obtains normal work
Make spectral samples collection and the fault spectrum sample set that leaks out.
5. it is leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature as claimed in claim 2, spy
Sign is, further includes following step：
25) according to training dataset, by genetic algorithm or Artificial Neural Network, optimize and calculate each characteristic frequency to leakage
Contribution rate Ω={ ω of wind failure1,…,ωk, the optimized parameter of failure criterion C so that it is determined that sintering machine leaks out.
6. it is leaked out on-line fault diagnosis method based on the bilateral detection method sintering machine of spectrum signature as claimed in claim 2, spy
Sign is, in step 34), be using threshold determination or fuzzy decision by the way of leak out fault diagnosis as sintering machine
Decision making approach.
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| CN111815201A (en) * | 2020-07-31 | 2020-10-23 | 中国汽车工程研究院股份有限公司 | Establishment of new energy automobile continuous sampling system and universal characteristic determination method |
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