CN113310503A - Fault diagnosis method for sensor of incomplete modeling mobile robot - Google Patents

Fault diagnosis method for sensor of incomplete modeling mobile robot Download PDF

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Publication number
CN113310503A
CN113310503A CN202110606326.9A CN202110606326A CN113310503A CN 113310503 A CN113310503 A CN 113310503A CN 202110606326 A CN202110606326 A CN 202110606326A CN 113310503 A CN113310503 A CN 113310503A
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fault
support vector
wheel encoder
mobile robot
gyroscope
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段琢华
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University of Electronic Science and Technology of China Zhongshan Institute
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University of Electronic Science and Technology of China Zhongshan Institute
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
    • G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D18/00—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/497—Means for monitoring or calibrating

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Manufacturing & Machinery (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

A fault diagnosis method for an incomplete modeling mobile robot sensor is characterized in that a mobile robot is a differential steering system and is provided with two encoders, a gyroscope and a two-dimensional laser radar; the known fault model comprises four types of faults, namely normal, left wheel encoder fault, right wheel encoder fault, gyroscope fault and the like, and the known fault is diagnosed by constructing residual error characteristics and threshold logic; registering the laser radar data of the previous and subsequent times by using an iterative closest point method, and detecting unknown faults by using registration errors as characteristics; the continuous multiple occurrence of unknown faults is considered a new fault pattern, which is modeled using the support vector data description.

Description

Fault diagnosis method for sensor of incomplete modeling mobile robot
Technical Field
The invention relates to a fault diagnosis method for a sensor of an incomplete modeling mobile robot, and belongs to the technical field of fault diagnosis of mobile robots.
Background
The problem of fault diagnosis under the condition of model imperfection is a challenging difficult problem. Most of the existing fault diagnosis methods require the establishment of a complete fault model. However, the system model is often incomplete for several reasons: (1) people do not master all rules of a complex system, so that part of dynamic states are not modeled; (2) since the complexity of the system is very high, some high order dynamics are usually ignored in order to simplify the computation; (3) due to the dynamic changes of the system and its environment, it is not possible to model the system completely.
In the prior art, Chishuhua et al proposed a particle filtering method for fault diagnosis of an incomplete multi-model hybrid system (see Chishu, Chua' Seiki, in Jinxia; "particle filtering algorithm for fault diagnosis of incomplete multi-model hybrid system", automated declaration, 2008, 5 st 581-587). The method extracts two statistics based on particle sets: normalization factor W for the set of particles and confidence B for the maximum a posteriori probability estimate state. Threshold logic is designed to detect unknown failure modes on this basis, i.e. discrete states are unknown failure modes when W is almost 0 and B is small. The main drawbacks of this technique are: this technique performs unknown fault detection based on the normalizer W of the particle set and the confidence B of the maximum a posteriori probability estimated state, whereas the normalization factor of the particle set depends on the number of particles and the continuous state transition equation, and is difficult to quantitatively characterize.
Disclosure of Invention
A fault diagnosis method for an incomplete modeling mobile robot sensor is characterized in that a mobile robot is a differential steering system and is provided with two encoders, a gyroscope and a two-dimensional laser radar; the known fault model comprises four types of faults, namely normal, left wheel encoder fault, right wheel encoder fault, gyroscope fault and the like, and the known fault is diagnosed by constructing residual error characteristics and threshold logic; registering the laser radar data of the previous and subsequent times by using an iterative closest point method, and detecting unknown faults by using registration errors as characteristics; the continuous multiple occurrence of unknown faults is considered a new fault pattern, which is modeled using the support vector data description.
The method comprises the following concrete steps: inputting: from time 1 toTLeft wheel encoder measurement of time of day
Figure 740494DEST_PATH_IMAGE001
Right wheel encoder measurement
Figure 332012DEST_PATH_IMAGE002
Gyro measurement
Figure 150932DEST_PATH_IMAGE003
Lidar scanning
Figure 802494DEST_PATH_IMAGE004
；
And (3) outputting: 1 from time toTFault status of time of day
Figure 582231DEST_PATH_IMAGE005
Learning the obtained fault model space
Figure 243019DEST_PATH_IMAGE006
；
Step 1: constructing a known fault model space
Figure 57392DEST_PATH_IMAGE006
={1，...，
Figure 738909DEST_PATH_IMAGE007
}，
Figure 5942DEST_PATH_IMAGE007
=4 represents the number of known fault models, fault 1 represents normal, fault 2 represents left wheel encoder fault, fault 3 represents right wheel encoder fault, and fault 4 represents gyroscope fault;
setting parameters
Figure 204842DEST_PATH_IMAGE008
，
Figure 936038DEST_PATH_IMAGE009
，
Figure 929402DEST_PATH_IMAGE010
，
Figure 946380DEST_PATH_IMAGE011
，
Figure 948972DEST_PATH_IMAGE012
，
Figure 331411DEST_PATH_IMAGE006
，k，W, ,
Figure 495676DEST_PATH_IMAGE013
Wherein
Figure 799619DEST_PATH_IMAGE008
Indicating the number of consecutive occurrences of an unknown fault,
Figure 340322DEST_PATH_IMAGE009
representing the minimum number of samples required to construct a new fault model,
Figure 780530DEST_PATH_IMAGE010
which is indicative of an encoder failure threshold value,
Figure 381276DEST_PATH_IMAGE011
a threshold value indicative of a failure of the gyroscope,
Figure 844618DEST_PATH_IMAGE012
indicating the unknown fault decision threshold value and,
Figure 251329DEST_PATH_IMAGE014
initialized to an empty set, representing a fault model space obtained by learning,kinitialized to 0, representing the number of fault models obtained by learning,Win order to connect the axial lengths of the left and right wheels of the mobile robot,
Figure 483727DEST_PATH_IMAGE013
is composed oft-1 totThe time interval of the moment of time,t=1,
Figure 520953DEST_PATH_IMAGE015
；
step 2: for thei=1,...,
Figure 471592DEST_PATH_IMAGE007
Are respectively provided with
Figure 213151DEST_PATH_IMAGE016
=i，i=1,...,
Figure 565635DEST_PATH_IMAGE007
，
Figure 570500DEST_PATH_IMAGE017
Wherein, in the step (A),
Figure 8435DEST_PATH_IMAGE018
，
Figure 759878DEST_PATH_IMAGE019
，
Figure 966868DEST_PATH_IMAGE020
，
Figure 142635DEST_PATH_IMAGE021
，
Figure 67865DEST_PATH_IMAGE022
，
Figure 354490DEST_PATH_IMAGE023
，
Figure 681566DEST_PATH_IMAGE024
，
Figure 700338DEST_PATH_IMAGE025
，
Figure 175182DEST_PATH_IMAGE026
，
Figure 203181DEST_PATH_IMAGE027
，
Figure 384763DEST_PATH_IMAGE028
；
and step 3: for thei=1,...,
Figure 902332DEST_PATH_IMAGE007
Are respectively aligned with
Figure 536576DEST_PATH_IMAGE029
Through which is passed
Figure 165003DEST_PATH_IMAGE030
Coordinate transformation is carried out on the expressed translation vector and the rotation angle to obtain a point set pair
Figure 466672DEST_PATH_IMAGE031
Using iterative closest point method pairs
Figure 92825DEST_PATH_IMAGE032
And
Figure 276682DEST_PATH_IMAGE031
carrying out registration, the registration results are respectively
Figure 646483DEST_PATH_IMAGE033
The registration accuracy is respectively
Figure 864975DEST_PATH_IMAGE034
Let us order
Figure 662030DEST_PATH_IMAGE035
；
And 4, step 4: if it is not
Figure 270865DEST_PATH_IMAGE036
Turning to the step 6, otherwise, turning to the step 5;
and 5:
Figure 238166DEST_PATH_IMAGE037
，
Figure 248847DEST_PATH_IMAGE038
，
Figure 216803DEST_PATH_IMAGE039
，
Figure 375252DEST_PATH_IMAGE040
，
Figure 86856DEST_PATH_IMAGE041
，
Figure 14361DEST_PATH_IMAGE042
，
Figure 153218DEST_PATH_IMAGE043
，
Figure 798963DEST_PATH_IMAGE044
firstly, set up
Figure 314258DEST_PATH_IMAGE045
=0, if
Figure 33952DEST_PATH_IMAGE046
And is
Figure 406028DEST_PATH_IMAGE047
And is
Figure 476752DEST_PATH_IMAGE048
Then set up
Figure 592476DEST_PATH_IMAGE045
=1, if
Figure 432256DEST_PATH_IMAGE046
And is
Figure 912916DEST_PATH_IMAGE049
And is
Figure 533253DEST_PATH_IMAGE048
Then set up
Figure 376968DEST_PATH_IMAGE045
=2, if
Figure 71255DEST_PATH_IMAGE046
And is
Figure 50712DEST_PATH_IMAGE047
And is
Figure 830449DEST_PATH_IMAGE050
Then set up
Figure 287976DEST_PATH_IMAGE045
=3, if
Figure 102348DEST_PATH_IMAGE051
And is
Figure 924810DEST_PATH_IMAGE047
And is
Figure 254160DEST_PATH_IMAGE048
Then set up
Figure 453061DEST_PATH_IMAGE045
= 4; if it is not
Figure 184256DEST_PATH_IMAGE045
Go to step 6 if 0, otherwise order
Figure 177620DEST_PATH_IMAGE008
=0, go to step 11;
step 6:m=1；
and 7: if it is notm>kTurning to step 9;
and 8: is provided with
Figure 994266DEST_PATH_IMAGE052
Judgment of
Figure 731278DEST_PATH_IMAGE053
Whether or not it belongs tomA support vector data descriptor if
Figure 337488DEST_PATH_IMAGE054
Then order
Figure 767332DEST_PATH_IMAGE055
，
Figure 71275DEST_PATH_IMAGE008
=0, go to step 11; otherwise makem=m+1, go to step 7; wherein
Figure 346398DEST_PATH_IMAGE056
，
Figure 52186DEST_PATH_IMAGE057
Is as followsmThe number of support vectors of a support vector descriptor,
Figure 387353DEST_PATH_IMAGE058
is as followsmA support vector descriptorlA number of support vectors that are,
Figure 178591DEST_PATH_IMAGE059
is as followsmIn a support vector descriptorlLagrange multipliers of the support vectors,
Figure 257406DEST_PATH_IMAGE060
，
Figure 817700DEST_PATH_IMAGE061
+
Figure 589347DEST_PATH_IMAGE062
is as followsmA radius of each support vector descriptor;
and step 9:
Figure 539985DEST_PATH_IMAGE008
=
Figure 484807DEST_PATH_IMAGE008
+1；
step 10: if it is not
Figure 837291DEST_PATH_IMAGE063
Then give an orderk=k+1, using sample sets
Figure 842157DEST_PATH_IMAGE064
Exercise the firstkA descriptor of a support vector is provided,
Figure 280091DEST_PATH_IMAGE065
，
Figure 763025DEST_PATH_IMAGE066
，
Figure 235595DEST_PATH_IMAGE067
，
Figure 414291DEST_PATH_IMAGE008
=0；
step 11: judging whether new sensor data exist or not, and if not, exiting; otherwise, it orderst=t+1, go to step 2.
Detailed Description
In order to illustrate the effectiveness of the method, the method is applied to a specific mobile robot system,
inputting: from time 1 toTLeft wheel encoder measurement of time of day
Figure 339521DEST_PATH_IMAGE001
Right wheel encoder measurement
Figure 626146DEST_PATH_IMAGE002
Gyro measurement
Figure 687643DEST_PATH_IMAGE003
Lidar scanning
Figure 971994DEST_PATH_IMAGE004
(ii) a The laser radar adopts RPLidar A1;
and (3) outputting: 1 from time toTFault status of time of day
Figure 446838DEST_PATH_IMAGE005
Learning the obtained fault model space
Figure 474837DEST_PATH_IMAGE006
；
Step 1: constructing a known fault model space
Figure 718736DEST_PATH_IMAGE006
={1，...，
Figure 908409DEST_PATH_IMAGE007
}，
Figure 808232DEST_PATH_IMAGE007
=4 represents the number of known fault models, fault 1 represents normal, fault 2 represents left wheel encoder fault, fault 3 represents right wheel encoder fault, and fault 4 represents gyroscope fault;
setting parameters
Figure 436659DEST_PATH_IMAGE008
，
Figure 738328DEST_PATH_IMAGE009
，
Figure 161219DEST_PATH_IMAGE010
，
Figure 548338DEST_PATH_IMAGE011
，
Figure 918139DEST_PATH_IMAGE012
，
Figure 871052DEST_PATH_IMAGE006
，k，W, ,
Figure 668106DEST_PATH_IMAGE013
Wherein
Figure 339259DEST_PATH_IMAGE008
Representing the number of times of continuous unknown faults and the minimum number of samples required for constructing a new fault model
Figure 512752DEST_PATH_IMAGE009
Set to 10, encoder failure threshold
Figure 523433DEST_PATH_IMAGE010
Set to 0.01m/s, gyroscope failure threshold
Figure 550776DEST_PATH_IMAGE011
Set to 0.001 rad/sec, unknown fault decision threshold
Figure 646908DEST_PATH_IMAGE012
Which is expressed as a value of 0.02,
Figure 358512DEST_PATH_IMAGE014
initialized to an empty set, representing a fault model space obtained by learning,kthe initialization is 0, the number of fault models obtained by learning is represented, and the axial length of the left wheel and the right wheel connecting the mobile robot is longW=0.4m， t-1 totTime interval of time of day
Figure 286017DEST_PATH_IMAGE013
Is 0.1 s; ,t=1,
Figure 424874DEST_PATH_IMAGE015
；
step 2: for thei=1,...,
Figure 70619DEST_PATH_IMAGE007
Are respectively provided with
Figure 320335DEST_PATH_IMAGE016
=i，i=1,...,
Figure 305608DEST_PATH_IMAGE007
，
Figure 677684DEST_PATH_IMAGE017
Wherein, in the step (A),
Figure 748408DEST_PATH_IMAGE018
，
Figure 864132DEST_PATH_IMAGE019
，
Figure 703912DEST_PATH_IMAGE020
，
Figure 184572DEST_PATH_IMAGE021
，
Figure 539330DEST_PATH_IMAGE022
，
Figure 396427DEST_PATH_IMAGE023
，
Figure 90714DEST_PATH_IMAGE024
，
Figure 804592DEST_PATH_IMAGE025
，
Figure 849908DEST_PATH_IMAGE026
，
Figure 307434DEST_PATH_IMAGE027
，
Figure 856227DEST_PATH_IMAGE028
；
and step 3: for thei=1,...,
Figure 9516DEST_PATH_IMAGE007
Are respectively aligned with
Figure 276549DEST_PATH_IMAGE029
Through which is passed
Figure 537766DEST_PATH_IMAGE030
Coordinate transformation is carried out on the expressed translation vector and the rotation angle to obtain a point set pair
Figure 3382DEST_PATH_IMAGE031
Using iterative closest point method pairs
Figure 262325DEST_PATH_IMAGE032
And
Figure 78972DEST_PATH_IMAGE031
carrying out registration, the registration results are respectively
Figure 878300DEST_PATH_IMAGE033
The registration accuracy is respectively
Figure 401686DEST_PATH_IMAGE034
Let us order
Figure 893847DEST_PATH_IMAGE035
；
And 4, step 4: if it is not
Figure 869893DEST_PATH_IMAGE036
Turning to the step 6, otherwise, turning to the step 5;
and 5:
Figure 472913DEST_PATH_IMAGE037
，
Figure 116384DEST_PATH_IMAGE038
，
Figure 513867DEST_PATH_IMAGE039
，
Figure 242789DEST_PATH_IMAGE040
，
Figure 380990DEST_PATH_IMAGE041
，
Figure 878968DEST_PATH_IMAGE042
，
Figure 385035DEST_PATH_IMAGE043
，
Figure 663570DEST_PATH_IMAGE044
firstly, set up
Figure 546075DEST_PATH_IMAGE045
=0, if
Figure 960876DEST_PATH_IMAGE046
And is
Figure 903424DEST_PATH_IMAGE047
And is
Figure 341359DEST_PATH_IMAGE048
Then set up
Figure 824293DEST_PATH_IMAGE045
=1, if
Figure 296863DEST_PATH_IMAGE046
And is
Figure 207050DEST_PATH_IMAGE049
And is
Figure 194597DEST_PATH_IMAGE048
Then set up
Figure 418905DEST_PATH_IMAGE045
=2, if
Figure 808298DEST_PATH_IMAGE046
And is
Figure 92649DEST_PATH_IMAGE047
And is
Figure 505176DEST_PATH_IMAGE050
Then set up
Figure 329912DEST_PATH_IMAGE045
=3, if
Figure 511495DEST_PATH_IMAGE051
And is
Figure 31994DEST_PATH_IMAGE047
And is
Figure 931817DEST_PATH_IMAGE048
Then set up
Figure 497927DEST_PATH_IMAGE045
= 4; if it is not
Figure 596333DEST_PATH_IMAGE045
Go to step 6 if 0, otherwise order
Figure 222487DEST_PATH_IMAGE008
=0, go to step 11;
step 6:m=1；
and 7: if it is notm>kTurning to step 9;
and 8: is provided with
Figure 671923DEST_PATH_IMAGE052
Judgment of
Figure 776145DEST_PATH_IMAGE053
Whether or not it belongs tomA support vector data descriptor if
Figure 932320DEST_PATH_IMAGE054
Then order
Figure 791691DEST_PATH_IMAGE055
，
Figure 400527DEST_PATH_IMAGE008
=0, go to step 11; otherwise makem=m+1, go to step 7; wherein
Figure 636336DEST_PATH_IMAGE056
，
Figure 647018DEST_PATH_IMAGE057
Is as followsmThe number of support vectors of a support vector descriptor,
Figure 614974DEST_PATH_IMAGE058
is as followsmA support vector descriptorlA number of support vectors that are,
Figure 773423DEST_PATH_IMAGE059
is as followsmIn a support vector descriptorlLagrange multipliers of the support vectors,
Figure 219447DEST_PATH_IMAGE060
，
Figure 412531DEST_PATH_IMAGE061
+
Figure 551389DEST_PATH_IMAGE062
is as followsmA radius of each support vector descriptor;
and step 9:
Figure 134817DEST_PATH_IMAGE008
=
Figure 446849DEST_PATH_IMAGE008
+1；
step 10: if it is not
Figure 432123DEST_PATH_IMAGE063
Then give an orderk=k+1, using sample sets
Figure 801269DEST_PATH_IMAGE064
Exercise the firstkA descriptor of a support vector is provided,
Figure 871993DEST_PATH_IMAGE065
，
Figure 925400DEST_PATH_IMAGE066
，
Figure 827496DEST_PATH_IMAGE067
，
Figure 370473DEST_PATH_IMAGE008
=0；
step 11: judging whether new sensor data exist or not, and if not, exiting; otherwise, it orderst=t+1，And (6) turning to the step 2.

Claims (1)

1. A fault diagnosis method for an incomplete modeling mobile robot sensor is characterized in that a mobile robot is a differential steering system and is provided with two encoders, a gyroscope and a two-dimensional laser radar; the known fault model comprises four types of faults, namely normal, left wheel encoder fault, right wheel encoder fault, gyroscope fault and the like, and the known fault is diagnosed by constructing residual error characteristics and threshold logic; registering the laser radar data of the previous and subsequent times by using an iterative closest point method, and detecting unknown faults by using registration errors as characteristics; the continuous and repeated occurrence of unknown faults is regarded as a new fault mode, and the new fault mode is modeled by using the description of the support vector data;
the method comprises the following concrete steps: inputting: from time 1 toTLeft wheel encoder measurement of time of day
Figure 275469DEST_PATH_IMAGE001
Right wheel encoder measurement
Figure 296515DEST_PATH_IMAGE002
Gyro measurement
Figure 954416DEST_PATH_IMAGE003
Lidar scanning
Figure 238767DEST_PATH_IMAGE004
；
And (3) outputting: 1 from time toTFault status of time of day
Figure 713611DEST_PATH_IMAGE005
Learning the obtained fault model space
Figure 538347DEST_PATH_IMAGE006
；
Step 1: constructing a known fault model space
Figure 719930DEST_PATH_IMAGE006
={1，...，
Figure 175182DEST_PATH_IMAGE007
}，
Figure 137322DEST_PATH_IMAGE007
=4 represents the number of known fault models, fault 1 represents normal, fault 2 represents left wheel encoder fault, fault 3 represents right wheel encoder fault, and fault 4 represents gyroscope fault;
setting parameters
Figure 765749DEST_PATH_IMAGE008
，
Figure 801839DEST_PATH_IMAGE009
，
Figure 490309DEST_PATH_IMAGE010
，
Figure 939745DEST_PATH_IMAGE011
，
Figure 106284DEST_PATH_IMAGE012
，
Figure 525108DEST_PATH_IMAGE006
，k，W, ,
Figure 322163DEST_PATH_IMAGE013
Wherein
Figure 930999DEST_PATH_IMAGE008
Indicating the number of consecutive occurrences of an unknown fault,
Figure 166808DEST_PATH_IMAGE009
representing samples required for building a new fault modelThe minimum value of the number of the present numbers,
Figure 177489DEST_PATH_IMAGE010
which is indicative of an encoder failure threshold value,
Figure 207762DEST_PATH_IMAGE011
a threshold value indicative of a failure of the gyroscope,
Figure 303894DEST_PATH_IMAGE012
indicating the unknown fault decision threshold value and,
Figure 749919DEST_PATH_IMAGE014
initialized to an empty set, representing a fault model space obtained by learning,kinitialized to 0, representing the number of fault models obtained by learning,Win order to connect the axial lengths of the left and right wheels of the mobile robot,
Figure 943003DEST_PATH_IMAGE013
is composed oft-1 totThe time interval of the moment of time,t=1,
Figure 81860DEST_PATH_IMAGE015
；
step 2: for thei=1,...,
Figure 727605DEST_PATH_IMAGE007
Are respectively provided with
Figure 977321DEST_PATH_IMAGE016
=i，i=1,...,
Figure 24912DEST_PATH_IMAGE007
，
Figure 334670DEST_PATH_IMAGE017
Wherein, in the step (A),
Figure 467711DEST_PATH_IMAGE018
，
Figure 521118DEST_PATH_IMAGE019
，
Figure 423215DEST_PATH_IMAGE020
，
Figure 903875DEST_PATH_IMAGE021
，
Figure 261562DEST_PATH_IMAGE022
，
Figure 118660DEST_PATH_IMAGE023
，
Figure 812946DEST_PATH_IMAGE024
，
Figure 526825DEST_PATH_IMAGE025
，
Figure 572141DEST_PATH_IMAGE026
，
Figure 29667DEST_PATH_IMAGE027
，
Figure 578460DEST_PATH_IMAGE028
；
and step 3: for thei=1,...,
Figure 666502DEST_PATH_IMAGE007
Are respectively aligned with
Figure 995852DEST_PATH_IMAGE029
Through which is passed
Figure 929173DEST_PATH_IMAGE030
Coordinate transformation is carried out on the expressed translation vector and the rotation angle to obtain a point set pair
Figure 660369DEST_PATH_IMAGE031
Using iterative closest point method pairs
Figure 919312DEST_PATH_IMAGE032
And
Figure 470379DEST_PATH_IMAGE031
carrying out registration, the registration results are respectively
Figure 472970DEST_PATH_IMAGE033
The registration accuracy is respectively
Figure 996355DEST_PATH_IMAGE034
Let us order
Figure 222937DEST_PATH_IMAGE035
；
And 4, step 4: if it is not
Figure 464562DEST_PATH_IMAGE036
Turning to the step 6, otherwise, turning to the step 5;
and 5:
Figure 67582DEST_PATH_IMAGE037
，
Figure 445474DEST_PATH_IMAGE038
，
Figure 105607DEST_PATH_IMAGE039
，
Figure 568949DEST_PATH_IMAGE040
，
Figure 975660DEST_PATH_IMAGE041
，
Figure 473637DEST_PATH_IMAGE042
，
Figure 228972DEST_PATH_IMAGE043
，
Figure 445190DEST_PATH_IMAGE044
firstly, set up
Figure 390012DEST_PATH_IMAGE045
=0, if
Figure 804813DEST_PATH_IMAGE046
And is
Figure 547029DEST_PATH_IMAGE047
And is
Figure 984963DEST_PATH_IMAGE048
Then set up
Figure 733476DEST_PATH_IMAGE045
=1, if
Figure 940467DEST_PATH_IMAGE046
And is
Figure 53916DEST_PATH_IMAGE049
And is
Figure 41464DEST_PATH_IMAGE048
Then set up
Figure 265772DEST_PATH_IMAGE045
=2, if
Figure 655165DEST_PATH_IMAGE046
And is
Figure 939516DEST_PATH_IMAGE047
And is
Figure 86463DEST_PATH_IMAGE050
Then set up
Figure 176779DEST_PATH_IMAGE045
=3, if
Figure 358362DEST_PATH_IMAGE051
And is
Figure 875931DEST_PATH_IMAGE047
And is
Figure 775754DEST_PATH_IMAGE048
Then set up
Figure 404181DEST_PATH_IMAGE045
= 4; if it is not
Figure 440270DEST_PATH_IMAGE045
Go to step 6 if 0, otherwise order
Figure 128740DEST_PATH_IMAGE008
=0, go to step 11;
step 6:m=1；
and 7: if it is notm>kTurning to step 9;
and 8: is provided with
Figure 250280DEST_PATH_IMAGE052
Judgment of
Figure 620082DEST_PATH_IMAGE053
Whether or not it belongs tomA support vector data descriptor if
Figure 859081DEST_PATH_IMAGE054
Then order
Figure 656136DEST_PATH_IMAGE055
，
Figure 327289DEST_PATH_IMAGE008
=0, go to step 11; otherwise makem=m+1, go to step 7; wherein
Figure 235202DEST_PATH_IMAGE056
，
Figure 308200DEST_PATH_IMAGE057
Is as followsmThe number of support vectors of a support vector descriptor,
Figure 276156DEST_PATH_IMAGE058
is as followsmA support vector descriptorlA number of support vectors that are,
Figure 372288DEST_PATH_IMAGE059
is as followsmIn a support vector descriptorlLagrange multipliers of the support vectors,
Figure 146209DEST_PATH_IMAGE060
，
Figure 276976DEST_PATH_IMAGE061
+
Figure 478150DEST_PATH_IMAGE062
is as followsmA radius of each support vector descriptor;
and step 9:
Figure 795999DEST_PATH_IMAGE008
=
Figure 311294DEST_PATH_IMAGE008
+1；
step 10: if it is not
Figure 358885DEST_PATH_IMAGE063
Then give an orderk=k+1, using sample sets
Figure 668643DEST_PATH_IMAGE064
Exercise the firstkA descriptor of a support vector is provided,
Figure 536105DEST_PATH_IMAGE065
，
Figure 589512DEST_PATH_IMAGE066
，
Figure 491609DEST_PATH_IMAGE067
，
Figure 972268DEST_PATH_IMAGE008
=0；
step 11: judging whether new sensor data exist or not, and if not, exiting; otherwise, it orderst=t+1, go to step 2.
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Publication number Priority date Publication date Assignee Title
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115201865A (en) * 2022-07-18 2022-10-18 广东汇天航空航天科技有限公司 Fault detection and equipment selection method, device, equipment and storage medium

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