CN103245800A - Detection method for grey correlation failure of speed sensor of aviation electric steering engine - Google Patents

Detection method for grey correlation failure of speed sensor of aviation electric steering engine Download PDF

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CN103245800A
CN103245800A CN2013101634974A CN201310163497A CN103245800A CN 103245800 A CN103245800 A CN 103245800A CN 2013101634974 A CN2013101634974 A CN 2013101634974A CN 201310163497 A CN201310163497 A CN 201310163497A CN 103245800 A CN103245800 A CN 103245800A
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speed
motor
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moment
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CN103245800B (en
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齐蓉
白文伟
兰根龙
范珩
张可意
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Northwestern Polytechnical University
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Abstract

The invention provides a detection method for a grey correlation failure of a speed sensor of an aviation electric steering engine, which comprises the following steps: acquiring loading moment, motor rotational speed, motor bus-bar current; then building a speed observation device; performing sampling on speed signals fed back to a controller by the speed sensor and speed signals observed by the speed observation device; building two time sequences; calculating gray detection relative correlation degree of the time sequences; and finally, designing speed sensor failure threshold to judge failure. The method improves the detection precision, and avoids detected faults of false alarm and missed alarm.

Description

The grey correlation fault detection method of aviation electric steering engine speed pickup
Technical field
The present invention relates to a kind of fault detection method of aviation electric steering engine speed pickup.
Background technology
Electric steering engine has that volume is little, reliability is high, the characteristics of function admirable, has obtained using widely in the Aircraft Steering Engine system.And speed pickup is as aviation Electrodynamic Rudder System important component part, and it breaks down and can directly influence steering gear system and normally move.At present, sensor fault detects achievement in research to be had: the article " based on induction motor speed pickup fault diagnosis and the fault-tolerant control based on state observer " that is published in " Proceedings of the CSEE " uses the Long Beige observer, but its concern is the modeling of motor, and the Error Feedback matrix that uses, calculation of complex is not easy to engineering construction; Be published in the BP Neural Network Online recursion learning algorithm of article " studying at line method of the flight control system sensor fault diagnosis " use of " computer measurement and control ", but its calculated amount is bigger, be not easy to implement, and the not fault detect of special concern aviation electric steering engine speed pickup.
Summary of the invention
In order to overcome the deficiencies in the prior art, the invention provides a kind of grey correlation fault detection method of aviation electric steering engine speed pickup, improved accuracy of detection, be convenient to engineering construction.
The technical solution adopted for the present invention to solve the technical problems may further comprise the steps:
The first step, controller are gathered loading moment T respectively L, motor speed ω rWith motor bus current i;
Second step, structure rotating speed observer
Figure BDA00003148074300011
Get motor speed observed reading differential
Figure BDA00003148074300012
Wherein, C TBe the motor torque constant, J is the motor moment of inertia, T LBe loading moment,
Figure BDA00003148074300013
Be motor speed observed reading, ω rBe motor speed, c is the observer coefficient, 0＜c＜100000;
The 3rd step, the motor speed of calculating observation Wherein t is system time;
In the 4th step, the rate signal that moment t speed pickup to the t-n+1 is constantly fed back to controller is sampled with the rate signal that speed observer is observed, and constitutes two time serieses:
Y i = ( ω ^ r ( t - n + 1 ) , ω ^ r ( t - n + 2 ) , . . . , ω ^ r ( t - 1 ) , ω ^ r ( t ) ) Y j = ( ω r ( t - n + 1 ) , ω r ( t - n + 2 ) , . . . , ω r ( t - 1 ) , ω r ( t ) )
Wherein, n is the data number, 3＜n＜10000;
The 5th step is with Y i, Y jNormalized gets Y i 0 = ( ω ^ r 0 ( t - n + 1 ) , ( ω ^ r 0 ( t - n + 2 ) , . . . , ω ^ r 0 ( t - 1 ) , ω ^ r 0 ( t ) ) ,
Y i 0 = ( ω r 0 ( t - n + 1 ) , ( ω r 0 ( t - n + 2 ) , . . . , ω r 0 ( t - 1 ) , ω r 0 ( t ) ) , Wherein ω ^ r 0 ( t + k ) = ω ^ r ( t + k ) / ω ^ r ( t - n + 1 ) ,
Figure BDA00003148074300024
Wherein
Figure BDA00003148074300025
With
Figure BDA00003148074300026
Be respectively Y i, Y jSequence after the normalization, k is sequence number, t-n+1≤k≤t;
In the 6th step, calculate norm | | s i | | 1 = Σ k = 1 t | ω ^ r 0 | , | | S j | | 1 = Σ k = 1 t | ω r 0 | , | | S i - S j | | 1 = Σ k = 1 t | ω ^ r 0 - ω r 0 | ; Wherein || s i|| 1For
Figure BDA00003148074300028
1 norm, || s j|| 1For
Figure BDA00003148074300029
1 norm, || s i-s j|| 1For
Figure BDA000031480743000210
1 norm, k is sample sequence number, t is current time;
In the 7th step, calculate ξ rel _ ij = 1 + | | S i | | 1 + | | S j | | 1 1 + | | S i | | 1 + | | S j | | 1 + | | S i - S j | | 1 , Wherein, ξ Rel_ijBe Y i, Y jGrey detect the relative degree of association;
In the 8th step, if design rate sensor fault threshold delta V=0.9 is ξ Rel_ij＞Δ V, then the speed pickup operate as normal goes to the first step; Otherwise, went to for the 9th step;
The 9th step, controller indication speed pickup fault.
The invention has the beneficial effects as follows: designed a kind of grey correlation fault detection method of speed pickup, based on gray control theory, proposed grey and detected the relative degree of association.Compare with traditional observer residual error fault detection method, improved accuracy of detection, stopped the detection failure of false-alarm, false dismissal.
Description of drawings
Fig. 1 is testing process process flow diagram of the present invention.
Embodiment
The present invention is further described below in conjunction with drawings and Examples.
This detection method process is as follows:
The first step, controller are gathered loading moment T respectively L, motor speed ω r, motor bus current i went to for second step.
Second step, structure rotating speed observer
ω ^ · r = C T J i - 1 J T L - B J ω ^ r + c ( ω r - ω ^ r )
Get motor speed observed reading differential
Figure BDA000031480743000213
Wherein, C TBe the motor torque constant, J is the motor moment of inertia, T LBe loading moment,
Figure BDA00003148074300031
Be motor speed observed reading, ω rBe motor speed, c is observer coefficient (0＜c＜100000).Went to for the 3rd step.
The 3rd step, the motor speed of calculating observation
Figure BDA00003148074300032
Wherein t is system time, goes to for the 4th step.
In the 4th step, the rate signal that moment t speed pickup to the t-n+1 is constantly fed back to controller is sampled with the rate signal that speed observer is observed, and constitutes two time serieses:
Y i = ( ω ^ r ( t - n + 1 ) , ω ^ r ( t - n + 2 ) , . . . , ω ^ r ( t - 1 ) , ω ^ r ( t ) ) Y j = ( ω r ( t - n + 1 ) , ω r ( t - n + 2 ) , . . . , ω r ( t - 1 ) , ω r ( t ) )
Wherein, n is data number (3＜n＜10000); Went to for the 5th step.
The 5th step is with Y i, Y jNormalized gets Y i 0 = ( ω ^ r 0 ( t - n + 1 ) , ( ω ^ r 0 ( t - n + 2 ) , . . . , ω ^ r 0 ( t - 1 ) , ω ^ r 0 ( t ) )
Y i 0 = ( ω r 0 ( t - n + 1 ) , ( ω r 0 ( t - n + 2 ) , . . . , ω r 0 ( t - 1 ) , ω r 0 ( t ) ) , Wherein ω ^ r 0 ( t + k ) = ω ^ r ( t + k ) / ω ^ r ( t - n + 1 ) ,
Figure BDA00003148074300037
Wherein
Figure BDA00003148074300038
With
Figure BDA00003148074300039
Be respectively Y i, Y jSequence after the normalization, k is sequence number (t-n+1≤k≤t).Went to for the 6th step.
In the 6th step, calculate norm | | s i | | 1 = Σ k = 1 t | ω ^ r 0 | , | | S j | | 1 = Σ k = 1 t | ω r 0 | , | | S i - S j | | 1 = Σ k = 1 t | ω ^ r 0 - ω r 0 | ; Wherein || s i|| 1For
Figure BDA000031480743000311
1 norm, || s j|| 1For
Figure BDA000031480743000312
1 norm, || s i-s j|| 1For 1 norm, k is sample sequence number, t is current time, goes to for the 7th step.
In the 7th step, calculate ξ rel _ ij = 1 + | | S i | | 1 + | | S j | | 1 1 + | | S i | | 1 + | | S j | | 1 + | | S i - S j | | 1 , Wherein, ξ Rel_ijBe Y i, Y jGrey detect the relative degree of association; Went to for the 8th step.
The 8th step, failure judgement, design rate sensor fault threshold delta V (Δ V=0.9) is if ξ Rel_ij＞Δ V, then the speed pickup operate as normal goes to the first step; Otherwise, went to for the 9th step;
The 9th step, controller indication speed pickup fault.
As described in Figure 1, the first step of the present invention, controller is gathered loading moment T respectively L, motor speed ω r, electric machine phase current feedback signal i went to for second step.
Second step, structure rotating speed observer
ω ^ · r = C T J i - 1 J T L - B J ω ^ r + c ( ω r - ω ^ r )
Get motor speed observed reading differential
Figure BDA000031480743000318
Wherein, C TBe the motor torque constant, J is the motor moment of inertia, T LBe loading moment,
Figure BDA00003148074300041
Be motor speed observed reading, ω rBe motor speed, c is observer coefficient (c gets 10000).Went to for the 3rd step.
The 3rd step, the motor speed of calculating observation
Figure BDA00003148074300042
Wherein system time widely went to for the 4th step.
In the 4th step, the rate signal that moment t speed pickup to the t-n+1 is constantly fed back to controller is sampled with the rate signal that speed observer is observed, and constitutes two time serieses:
Y i = ( ω ^ r ( t - n + 1 ) , ω ^ r ( t - n + 2 ) , . . . , ω ^ r ( t - 1 ) , ω ^ r ( t ) ) Y j = ( ω r ( t - n + 1 ) , ω r ( t - n + 2 ) , . . . , ω r ( t - 1 ) , ω r ( t ) )
Wherein, n is data number (n gets 5000); Went to for the 5th step.
The 5th step is with Y i, Y jNormalized gets Y i 0 = ( ω ^ r 0 ( t - n + 1 ) , ( ω ^ r 0 ( t - n + 2 ) , . . . , ω ^ r 0 ( t - 1 ) , ω ^ r 0 ( t ) ) ,
Y i 0 = ( ω ^ r 0 ( t - n + 1 ) , ( ω ^ r 0 ( t - n + 2 ) , . . . , ω ^ r 0 ( t - 1 ) , ω ^ r 0 ( t ) ) , Wherein ω ^ r 0 ( t + k ) = ω ^ r ( t + k ) / ω ^ r ( t - n + 1 ) ,
Figure BDA00003148074300047
Wherein
Figure BDA00003148074300048
With Be respectively Y i, Y jSequence after the normalization, k is sequence number (t-n+1≤k≤t).Went to for the 6th step.
In the 6th step, calculate norm | | s i | | 1 = Σ k = 1 t | ω ^ r 0 | , | | S j | | 1 = Σ k = 1 t | ω r 0 | , | | S i - S j | | 1 = Σ k = 1 t | ω ^ r 0 - ω r 0 | ; Wherein || s i|| 1For
Figure BDA000031480743000410
1 norm, || s j|| 1For
Figure BDA000031480743000411
1 norm, || s i-s j|| 1For
Figure BDA000031480743000412
1 norm, k is sample sequence number, t is current time, goes to for the 7th step.
In the 7th step, calculate ξ rel _ ij = 1 + | | S i | | 1 + | | S j | | 1 1 + | | S i | | 1 + | | S j | | 1 + | | S i - S j | | 1 , Wherein, ξ Rel_ijBe Y i, Y jGrey detect the relative degree of association; Went to for the 8th step.
The 8th step, failure judgement, design rate sensor fault threshold delta V (Δ V=0.9) is if ξ Rel_ij＞Δ V is the speed pickup operate as normal then, goes to the first step; Otherwise, went to for the 9th step;
The 9th step, controller indication speed pickup fault.

Claims (1)

1. the grey correlation fault detection method of an aviation electric steering engine speed pickup is characterized in that comprising the steps:
The first step, controller are gathered loading moment T respectively L, motor speed ω rWith motor bus current i;
Second step, structure rotating speed observer ω ^ · r = C T J i - 1 J T L - B J ω ^ r + c ( ω r - ω ^ r ) , Get motor speed observed reading differential
Figure FDA000031480742000114
Wherein, C TBe the motor torque constant, J is the motor moment of inertia, T LBe loading moment,
Figure FDA000031480742000115
Be motor speed observed reading, ω rBe motor speed, c is the observer coefficient, 0＜c＜100000;
The 3rd step, the motor speed of calculating observation System time widely wherein;
In the 4th step, the rate signal that moment t speed pickup to the t-n+1 is constantly fed back to controller is sampled with the rate signal that speed observer is observed, and constitutes two time serieses:
Y i = ( ω ^ r ( t - n + 1 ) , ω ^ r ( t - n + 2 ) , . . . , ω ^ r ( t - 1 ) , ω ^ r ( t ) ) Y j = ( ω r ( t - n + 1 ) , ω r ( t - n + 2 ) , . . . , ω r ( t - 1 ) , ω r ( t ) )
Wherein, n is the data number, 3＜n＜10000;
The 5th step is with Y i, Y jNormalized gets Y i 0 = ( ω ^ r 0 ( t - n + 1 ) , ( ω ^ r 0 ( t - n + 2 ) , . . . , ω ^ r 0 ( t - 1 ) , ω ^ r 0 ( t ) ) ,
Y i 0 = ( ω r 0 ( t - n + 1 ) , ( ω r 0 ( t - n + 2 ) , . . . , ω r 0 ( t - 1 ) , ω r 0 ( t ) ) , Wherein ω ^ r 0 ( t + k ) = ω ^ r ( t + k ) / ω ^ r ( t - n + 1 ) , Wherein
Figure FDA00003148074200018
With
Figure FDA000031480742000116
Be respectively Y i, Y jSequence after the normalization, k is sequence number, t-n+1≤k≤t;
In the 6th step, calculate norm | | s i | | 1 = Σ k = 1 t | ω ^ r 0 | , | | S j | | 1 = Σ k = 1 t | ω r 0 | , | | S i - S j | | 1 = Σ k = 1 t | ω ^ r 0 - ω r 0 | ; Wherein || s i|| 1For
Figure FDA000031480742000110
1 norm, || s j|| 1For
Figure FDA000031480742000111
1 norm, || s i-s j|| 1For
Figure FDA000031480742000112
1 norm, k is sample sequence number, t is current time;
In the 7th step, calculate ξ rel _ ij = 1 + | | S i | | 1 + | | S j | | 1 1 + | | S i | | 1 + | | S j | | 1 + | | S i - S j | | 1 , Wherein, ξ Rel_ijBe Y i, Y jGrey detect the relative degree of association;
In the 8th step, if design rate sensor fault threshold delta V=0.9 is ξ Rel_ij＞Δ V, then the speed pickup operate as normal goes to the first step; Otherwise, went to for the 9th step;
The 9th step, controller indication speed pickup fault.
CN201310163497.4A 2013-05-06 2013-05-06 Detection method for grey correlation failure of speed sensor of aviation electric steering engine Expired - Fee Related CN103245800B (en)

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Publication number Priority date Publication date Assignee Title
CN112000107A (en) * 2020-09-07 2020-11-27 中国船舶重工集团公司第七0七研究所九江分部 Steering control loop fault diagnosis method and diagnosis system based on steering engine model

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101201295A (en) * 2006-12-13 2008-06-18 上海海事大学 Method and device for predicting grey failure of rotating machinery wavelet
CN101799320A (en) * 2010-01-27 2010-08-11 北京信息科技大学 Fault prediction method and device thereof for rotation equipment

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101201295A (en) * 2006-12-13 2008-06-18 上海海事大学 Method and device for predicting grey failure of rotating machinery wavelet
CN101799320A (en) * 2010-01-27 2010-08-11 北京信息科技大学 Fault prediction method and device thereof for rotation equipment

Non-Patent Citations (1)

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Title
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112000107A (en) * 2020-09-07 2020-11-27 中国船舶重工集团公司第七0七研究所九江分部 Steering control loop fault diagnosis method and diagnosis system based on steering engine model

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