CN104343709A - Draught fan failure detection apparatus and method - Google Patents

Draught fan failure detection apparatus and method Download PDF

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Publication number
CN104343709A
CN104343709A CN201310312114.5A CN201310312114A CN104343709A CN 104343709 A CN104343709 A CN 104343709A CN 201310312114 A CN201310312114 A CN 201310312114A CN 104343709 A CN104343709 A CN 104343709A
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China
Prior art keywords
model
signal
draught fan
running shaft
trouble
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CN201310312114.5A
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CN104343709B (en
Inventor
许小刚
吴正人
孙玮
刘锦廉
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North China Electric Power University
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North China Electric Power University
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
    • F04D—NON-POSITIVE-DISPLACEMENT PUMPS
    • F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
    • F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2260/00—Function
    • F05D2260/80—Diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
    • F05D2260/00—Function
    • F05D2260/83—Testing, e.g. methods, components or tools therefor

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  • Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Control Of Positive-Displacement Air Blowers (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)

Abstract

The invention discloses a draught fan failure detection apparatus and method. The draught fan failure detection apparatus structurally comprises a position adjusting unit and a vibration acquisition frame arranged on the position adjusting unit. A displacement sensor connected to a processing host is arranged on the vibration acquisition frame. The draught fan failure detection method with the draught fan failure detection apparatus includes the steps of A adjusting the position of the displacement sensor; B, simulating draught fan failure and collecting a failure signal; C, obtaining a time series of the failure signal; D, converting the time series into a symbol sequence; E, calculating information entropy of the symbol sequence; F, extracting a feature vector; G, performing a first model training; H, performing a second model training; I, obtaining a final training model; J, formally running a draught fan and comparing the signal with the final training model to obtain failure information. According to the draught fan failure detection apparatus and method, a vibration acquisition device is arranged reasonably and symbolization processing is performed on the acquired vibration signal to obtain a comparative database and reduce the impact of noise, so that the computation speed is greatly improved.

Description

A kind of device and method detecting fan trouble
Technical field
The present invention relates to fan trouble early warning field, especially a kind of device and method detecting fan trouble.
Background technique
In power station, the operation conditions of blower fan is directly connected to safety, the economical operation of power plant, and the reliability of blower fan, Security and Economy depend on its Effec-tive Function, real-time status tracking evaluation, accurately fault diagnosis and maintenance, the fault diagnosis therefore studying blower fan is significant.Common blower fan mechanical failure has rotor unbalance, rotor misalignment, bearing's looseness and impact and rub etc., although method for diagnosing faults has a lot, is substantially all divided into 3 steps: the acquisition of diagnostic message; Fault signature extracts; State recognition and fault diagnosis.
Traditional signal characteristic extracting methods, premised on the stationarity of signal, cannot carry out analysing and processing effectively to non-stationary signal.Non-linear due to the non-linear of the driving force in fan operation, damping force and elastic force and mechanical system, detected oscillating signal is non-stationary signal, and traditional signal characteristic extracting methods has larger narrow limitation.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of device and method detecting fan trouble, by appropriate design vibration acquisition device, and symbolization process is carried out to the oscillating signal gathered, obtain comparison database, greatly reduce the impact of noise, drastically increase computational speed.
For solving the problems of the technologies described above, the technical solution used in the present invention is as follows.
A kind of device detecting fan trouble, structure comprises fan body, in fan body, running shaft is installed, running shaft is connected with motor, the two ends of described running shaft are separately installed with the vibration acquisition frame that two are parallel to described rotating shaft axis direction, mutually vertical at two vibration acquisition framves of same one end, the one end connecting described motor at described running shaft is provided with a vibration acquisition frame perpendicular to rotating shaft axis direction, each vibration acquisition frame is provided with displacement transducer, vibration collecting frame is arranged on position regulating device, the structure of position regulating device comprises slide rail, slide rail is provided with slide block, vibration acquisition frame is fixed on slide block, slide block is provided with leading screw, one end of leading screw is provided with micrometer, screw thread on leading screw is provided with teflon protective layer, also comprise a processing host in structure, displacement transducer and processing host are carried out communication and are connected, and processing host is also connected with a personal-machine interactive module, and the structure of processing host comprises computing module and DBM.
As a preferred technical solution of the present invention, between described slide rail and slide block, be provided with spring.
As a preferred technical solution of the present invention, the integrated current vortex sensor of institute's displacement sensors.
As a preferred technical solution of the present invention, described DBM adopts SQL server 2008 management system.
Utilize the device of above-mentioned detection fan trouble to carry out the method for faut detection, comprise the following steps:
A, installation position displacement sensor, use position controlling device regulates the measuring distance between displacement transducer and running shaft to be 30mm ~ 70mm;
B, simulation fan trouble, gather trouble signal
Simulate known fan trouble item by item, obtain trouble signal by displacement transducer, by trouble signal input processing main frame, form error.dat signal file;
C, the error.dat signal file read in above-mentioned steps A, obtain its time sequence X, in matlab software, input following statement:
Load error.dat；
X＝error；
D, the time series X obtained in step C is converted into symbol sebolic addressing
s i ( x i ) = 0 &mu; < x i &le; ( 1 + &alpha; ) &mu; 1 ( 1 + &alpha; ) &mu; < x i < &infin; 2 ( 1 - &alpha; ) &mu; < x i &le; &mu; 3 x i &le; ( 1 - &alpha; ) &mu; ( 1 &le; i &le; N )
In formula, μ represents the average of time series X, namely α is defined as weight, is set as that 0.05, S is the symbol sebolic addressing transforming and obtain;
The entropy of information of E, compute sign sequence
Symbol sebolic addressing S is divided into length be 3 substring, because S has 4 kinds of different values, so substring has form in 64, calculate the probability of occurrence of each substring
p ( l ) = C ( l ) N - 3 + 1 1≤l≤64
In formula, C (l) is the number of times that a substring occurs;
To its computing information entropy,
H k = - &Sigma; p ( l ) > 0 p ( l ) lgp ( l ) ;
F, extraction characteristic vector
The data that 5 displacement sensor measurements are obtained after above-mentioned steps process, composition characteristic vector F k=[H k1, H k2, H k3, H k4, H k5];
G, for the first time model training
Use the characteristic vector F obtained in step F by matlab software training detection model,
Model 1=svmtrain (L, F), wherein L is fault category, model 1for the monitoring model obtained;
H, second time model training
The function relation between fault category L and characteristic vector F is made to be L=model 2(AF+B), A is the transmission weights of function, and the initial value of A is 10, is carried out the correction of A by following formula:
ΔA(n)＝ηζF+εΔA(n-1)
Wherein η is learning rate, is set as that 0.01, ε is factor of momentum, is set as that 0.9, ζ is error rate, is set as that the scope of 1%, n is 2 ~ 2001, carries out 2000 circulations and revises;
I, ask model 1and model 2the common factor of two training patterns, and the testing result of conflict is manually revised, obtain final training pattern model, stored in DBM;
J, blower fan commencement of commercial operation, the signal gathered by signal emitter, again through the process of above-mentioned steps B ~ step F, obtains characteristic vector F, uses matlab software to be compared by the final training pattern model obtained in contrast characteristic's vector and above-mentioned steps I,
L＝svmpredict(F，model)，
The fault category L that comparison goes out carries out fault message output by human-computer interaction module.
The beneficial effect adopting technique scheme to bring is: the mode using displacement transducer indirect inspection, does not affect the normal operation of blower fan.Rotating threaded shaft can make slide block move on slide rail; teflon protective layer can avoid the screw thread on leading screw to get rusty when long-time outdoor exposure; spring is applied with a fastening force between slide block and slide rail, prevents the loosening position that causes owing to being engaged between screw thread from offseting.By carrying out symbolization process to the signal gathered, and utilize and obtain two training patterns by different modes and carry out comprehensively, the impact of reduction noise, improves computational speed and judging nicety rate.Through carrying out identical experiment comparison on same blower fan, the fault diagnosis accuracy rate obtained by traditional signal characteristic extracting methods is 77.4%, and the fault diagnosis accuracy rate obtained by signal characteristic extracting methods provided by the invention is 99.23%.
Accompanying drawing explanation
Fig. 1 is the schematic diagram of fault pre-alarming device in the present invention's embodiment.
Fig. 2 is the schematic diagram of position regulating device in the present invention's embodiment.
Fig. 3 is the schematic diagram of trouble signal process in the present invention's embodiment.
Fig. 4 is the comparison diagram of the failure prediction categories drawn after fault concrete class and use the present invention diagnose.
In figure: 1, fan body; 2, running shaft; 3, vibration acquisition frame; 4, motor; 5, displacement transducer; 6, position regulating device; 7, processing host; 8, human-computer interaction module; 9, spring; 61, slide rail; 62, slide block; 63, leading screw; 64, micrometer; 65, teflon protective layer; 71, computing module; 72, DBM.
Embodiment
Referring to accompanying drawing 1-3, a kind of device detecting fan trouble, structure comprises fan body 1, running shaft 2 is installed in fan body 1, running shaft 2 is connected with motor 4, the two ends of described running shaft 2 are separately installed with the vibration acquisition frame 3 that two are parallel to described running shaft 2 axial direction, two vibration acquisition framves 3 in same one end are mutually vertical, the one end connecting described motor 4 at described running shaft 2 is provided with a vibration acquisition frame 3 perpendicular to running shaft 2 axial direction, each vibration acquisition frame 3 is provided with displacement transducer 5, vibration collecting frame 3 is arranged on position regulating device 6, the structure of position regulating device 6 comprises slide rail 61, slide rail 61 is provided with slide block 62, vibration acquisition frame 3 is fixed on slide block 62, slide block 62 is provided with leading screw 63, one end of leading screw 63 is provided with micrometer 64, screw thread on leading screw 63 is provided with teflon protective layer 65, also comprise a processing host 7 in structure, displacement transducer 5 carries out communication with processing host 7 and is connected, and processing host 7 is also connected with a personal-machine interactive module 8, and the structure of processing host 7 comprises computing module 71 and DBM 72.Spring 9 is provided with between described slide rail 61 and slide block 62.The integrated current vortex sensor of institute's displacement sensors 5.Described DBM 72 adopts SQL server2008 management system.
Utilize the device of above-mentioned detection fan trouble to carry out the method for faut detection, comprise the following steps:
A, installation position displacement sensor (5), use position controlling device (6) regulates the measuring distance between displacement transducer (5) and running shaft (2) to be 45mm;
B, simulation fan trouble, gather trouble signal
Simulate known fan trouble item by item, obtain trouble signal by displacement transducer (5), by trouble signal input processing main frame (7), form error.dat signal file;
The part signal that one of them displacement transducer 6 collects is as follows, and unit is V:
[3.8374，1.2832，-0.9849，-1.4434，2.1835]
C, the error.dat signal file read in above-mentioned steps A, obtain its time sequence X, in matlab software, input following statement:
Load error.dat；
X＝error；
D, the time series X obtained in step C is converted into symbol sebolic addressing
s i ( x i ) = 0 &mu; < x i &le; ( 1 + &alpha; ) &mu; 1 ( 1 + &alpha; ) &mu; < x i < &infin; 2 ( 1 - &alpha; ) &mu; < x i &le; &mu; 3 x i &le; ( 1 - &alpha; ) &mu; ( 1 &le; i &le; N )
In formula, μ represents the average of time series X, namely α is defined as weight, is set as that 0.05, S is the symbol sebolic addressing transforming and obtain;
The entropy of information of E, compute sign sequence
Symbol sebolic addressing S is divided into length be 3 substring, because S has 4 kinds of different values, so substring has form in 64, calculate the probability of occurrence of each substring
p ( l ) = C ( l ) N - 3 + 1 1≤l≤64
In formula, C (l) is the number of times that a substring occurs;
To its computing information entropy,
H k = - &Sigma; p ( l ) > 0 p ( l ) lgp ( l ) ;
F, extraction characteristic vector
The data that 5 displacement sensor (5) measurements are obtained after above-mentioned steps process, composition characteristic vector F k=[H k1, H k2, H k3, H k4, H k5]; The characteristic vector of part signal is as shown in the table:
G, for the first time model training
Use the characteristic vector F obtained in step F by matlab software training detection model,
Model 1=svmtrain (L, F), wherein L is fault category, model 1for the monitoring model obtained;
H, second time model training
The function relation between fault category L and characteristic vector F is made to be L=model 2(AF+B), A is the transmission weights of function, and the initial value of A is 10, is carried out the correction of A by following formula:
ΔA(n)＝ηζF+εΔA(n-1)
Wherein η is learning rate, is set as that 0.01, ε is factor of momentum, is set as that 0.9, ζ is error rate, is set as that the scope of 1%, n is 2 ~ 2001, carries out 2000 circulations and revises;
I, ask model 1and model 2the common factor of two training patterns, and the testing result of conflict is manually revised, obtain final training pattern model, stored in DBM (72);
J, blower fan commencement of commercial operation, the signal gathered by signal emitter, again through the process of above-mentioned steps B ~ step F, obtains characteristic vector F, uses matlab software to be compared by the final training pattern model obtained in contrast characteristic's vector and above-mentioned steps I,
L＝svmpredict(F，model)，
The fault category L that comparison goes out carries out fault message output by human-computer interaction module (8).
Referring to accompanying drawing 4, the Percent of contact area of fault concrete class and failure prediction categories reaches 99.23%.
Working principle of the present invention is: the mode using displacement transducer indirect inspection, does not affect the normal operation of blower fan.Rotating threaded shaft can make slide block move on slide rail; teflon protective layer can avoid the screw thread on leading screw to get rusty when long-time outdoor exposure; spring is applied with a fastening force between slide block and slide rail, prevents the loosening position that causes owing to being engaged between screw thread from offseting.By carrying out symbolization process to the signal gathered, and utilize and obtain two training patterns by different modes and carry out comprehensively, the impact of reduction noise, improves computational speed and judging nicety rate.
Foregoing description only proposes, not as the single restrictive condition to its technological scheme itself as the enforceable technological scheme of the present invention.

Claims (5)

1. one kind is detected the device of fan trouble, structure comprises fan body (1), running shaft (2) is installed in fan body (1), running shaft (2) is connected with motor (4), it is characterized in that: the two ends of described running shaft (2) are separately installed with the vibration acquisition frame (3) that two are parallel to described running shaft (2) axial direction, mutually vertical two vibration acquisition framves (3) of same one end, the one end connecting described motor (4) at described running shaft (2) is provided with a vibration acquisition frame (3) perpendicular to running shaft (2) axial direction, each vibration acquisition frame (3) is provided with displacement transducer (5), vibration collecting frame (3) is arranged on position regulating device (6), the structure of position regulating device (6) comprises slide rail (61), slide rail (61) is provided with slide block (62), vibration acquisition frame (3) is fixed on slide block (62), slide block (62) is provided with leading screw (63), one end of leading screw (63) is provided with micrometer (64), screw thread on leading screw (63) is provided with teflon protective layer (65), a processing host (7) is also comprised in structure, displacement transducer (5) carries out communication with processing host (7) and is connected, processing host (7) is also connected with a personal-machine interactive module (8), and the structure of processing host (7) comprises computing module (71) and DBM (72).
2. the device of detection fan trouble according to claim 1, is characterized in that: be provided with spring (9) between described slide rail (61) and slide block (62).
3. the device of detection fan trouble according to claim 1, is characterized in that: institute's displacement sensors (5) integrated current vortex sensor.
4. the device of detection fan trouble according to claim 1, is characterized in that: described DBM (72) adopts SQL server2008 management system.
5. utilize the device of the detection fan trouble described in claim 1 to carry out the method for faut detection, it is characterized in that, comprise the following steps:
A, installation position displacement sensor (5), use position controlling device (6) regulates the measuring distance between displacement transducer (5) and running shaft (2) to be 30mm ~ 70mm;
B, simulation fan trouble, gather trouble signal
Simulate known fan trouble item by item, obtain trouble signal by displacement transducer (5), by trouble signal input processing main frame (7), form error.dat signal file;
C, the error.dat signal file read in above-mentioned steps A, obtain its time sequence X, in matlab software, input following statement:
Load error.dat；
X＝error；
D, the time series X obtained in step C is converted into symbol sebolic addressing
s i ( x i ) = 0 &mu; < x i &le; ( 1 + &alpha; ) &mu; 1 ( 1 + &alpha; ) &mu; < x i < &infin; 2 ( 1 - &alpha; ) &mu; < x i &le; &mu; 3 x i &le; ( 1 - &alpha; ) &mu; ( 1 &le; i &le; N )
In formula, μ represents the average of time series X, namely α is defined as weight, is set as that 0.05, S is the symbol sebolic addressing transforming and obtain;
The entropy of information of E, compute sign sequence
Symbol sebolic addressing S is divided into length be 3 substring, because S has 4 kinds of different values, so substring has form in 64, calculate the probability of occurrence of each substring
p ( l ) = C ( l ) N - 3 + 1 1≤l≤64
In formula, C (l) is the number of times that a substring occurs;
To its computing information entropy,
H k = - &Sigma; p ( l ) > 0 p ( l ) lgp ( l ) ;
F, extraction characteristic vector
The data that 5 displacement sensor (5) measurements are obtained after above-mentioned steps process, composition characteristic vector F k=[H k1, H k2, H k3, H k4, H k5];
G, for the first time model training
Use the characteristic vector F obtained in step e by matlab software training detection model,
Model 1=svmtrain (L, F), wherein L is fault category, model 1for the monitoring model obtained;
H, second time model training
The function relation between fault category L and characteristic vector F is made to be L=model 2(AF+B), A is the transmission weights of function, and the initial value of A is 10, is carried out the correction of A by following formula:
ΔA(n)＝ηζF+εΔA(n-1)
Wherein η is learning rate, is set as that 0.01, ε is factor of momentum, is set as that 0.9, ζ is error rate, is set as that the scope of 1%, n is 2 ~ 2001, carries out 2000 circulations and revises;
I, ask model 1and model 2the common factor of two training patterns, and the testing result of conflict is manually revised, obtain final training pattern model, stored in DBM (72);
J, blower fan commencement of commercial operation, the signal gathered by signal emitter, again through the process of above-mentioned steps B ~ step F, obtains characteristic vector F, uses matlab software to be compared by the final training pattern model obtained in contrast characteristic's vector and above-mentioned steps I,
L＝svmpredict(F，model)，
The fault category L that comparison goes out carries out fault message output by human-computer interaction module (8).
CN201310312114.5A 2013-07-24 2013-07-24 A kind of device and method detecting fan trouble Expired - Fee Related CN104343709B (en)

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104343711A (en) * 2013-07-27 2015-02-11 华北电力大学（保定） Device and method for pre-warning non-steady-state failure of fan
CN106706028A (en) * 2015-11-13 2017-05-24 Abb技术有限公司 Method and system used for detecting state of motor
CN108131321A (en) * 2018-02-12 2018-06-08 山东理工大学 A kind of axial fan stall fault monitoring system and fault monitoring method
CN110594184A (en) * 2019-10-14 2019-12-20 中铁第四勘察设计院集团有限公司 Safety monitoring device and method for tunnel hoisting fan
CN110657116A (en) * 2019-10-28 2020-01-07 浙江上风高科专风实业有限公司 Fault detection device of axial flow fan
CN111043946A (en) * 2020-01-09 2020-04-21 合肥工业大学 Magnetic field interference noise test system for eddy current displacement sensor
CN111624931A (en) * 2020-06-18 2020-09-04 山东山大世纪科技有限公司 Industrial park electricity utilization internet intelligent operation and maintenance management and control system and method
CN114017378A (en) * 2021-11-04 2022-02-08 中广核全椒风力发电有限公司 Curve deviation comparative analysis method based on fan position characteristic formation and application

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CN202417987U (en) * 2011-12-30 2012-09-05 刘亚娟 Diagnosis and test device for fan faults
CN202614367U (en) * 2012-04-28 2012-12-19 长安大学 Fault diagnosis apparatus for induced draught fan
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KR20080107597A (en) * 2007-06-07 2008-12-11 주식회사 포스코 Blower seal, device and method for monitoring the wear quantity of blower seal
CN201402209Y (en) * 2009-03-30 2010-02-10 唐德尧 Intelligent failure monitoring and diagnosis system for wind generating set
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104343711A (en) * 2013-07-27 2015-02-11 华北电力大学（保定） Device and method for pre-warning non-steady-state failure of fan
CN104343711B (en) * 2013-07-27 2016-06-01 华北电力大学（保定） A kind of device and method for blower fan unstable condition fault pre-alarming
CN106706028A (en) * 2015-11-13 2017-05-24 Abb技术有限公司 Method and system used for detecting state of motor
CN108131321A (en) * 2018-02-12 2018-06-08 山东理工大学 A kind of axial fan stall fault monitoring system and fault monitoring method
CN108131321B (en) * 2018-02-12 2019-06-28 山东理工大学 A kind of axial fan stall fault monitoring method
CN110594184A (en) * 2019-10-14 2019-12-20 中铁第四勘察设计院集团有限公司 Safety monitoring device and method for tunnel hoisting fan
CN110657116A (en) * 2019-10-28 2020-01-07 浙江上风高科专风实业有限公司 Fault detection device of axial flow fan
CN111043946A (en) * 2020-01-09 2020-04-21 合肥工业大学 Magnetic field interference noise test system for eddy current displacement sensor
CN111043946B (en) * 2020-01-09 2021-05-28 合肥工业大学 Magnetic field interference noise test system for eddy current displacement sensor
CN111624931A (en) * 2020-06-18 2020-09-04 山东山大世纪科技有限公司 Industrial park electricity utilization internet intelligent operation and maintenance management and control system and method
CN114017378A (en) * 2021-11-04 2022-02-08 中广核全椒风力发电有限公司 Curve deviation comparative analysis method based on fan position characteristic formation and application

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