CN104503432B - Autonomous underwater robot fault identification method based on wavelet energy - Google Patents
Autonomous underwater robot fault identification method based on wavelet energy Download PDFInfo
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- CN104503432B CN104503432B CN201410705681.1A CN201410705681A CN104503432B CN 104503432 B CN104503432 B CN 104503432B CN 201410705681 A CN201410705681 A CN 201410705681A CN 104503432 B CN104503432 B CN 104503432B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0208—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the configuration of the monitoring system
Abstract
The invention relates to the technical field of autonomous underwater robot fault identification and fault-tolerant control, and specifically relates to an autonomous underwater robot fault identification method based on wavelet energy. The method comprises the following steps: employing a multi-layer wavelet decomposition method to achieve the data decomposition of the sensor and controller of an autonomous underwater robot; employing a method of fault feature extraction to achieve the extraction of fault features of an original signal, a wavelet detail coefficient and a wavelet approximation coefficient; and employing a correlation coefficient method to achieve the fault identification of a to-be-detected fault signal of the autonomous underwater robot. The method provided by the invention effectively solves the problems that an AUV sensor and the controller are affected by external interference and the precision of fault identification is lower. The redundancy description of a fault of an AUV propeller is obtained through the multi-band characteristics of multi-layer wavelet decomposition. Through multi-band fault information, the fault features is extracted at the same time, and a fault feature matrix is built up, thereby improving the identification precision of an AUV fault, and providing accurate fault information for a fault-tolerant controller.
Description
Technical field
The present invention relates to autonomous type underwater robot fault identification and Fault Tolerance Control Technology field, and in particular to one kind is based on
The autonomous type underwater robot fault identification method of wavelet energy.
Background technology
As land resources is day by day reduced, the paces of human development ocean are more and more faster.Autonomous type underwater robot (AUV：
Autonomous Underwater Vehicle) currently the only can be detected, be developed at deep-sea in the case of nobody
Carrier, is constantly subjected to the great attention of domestic and international research worker.Propeller is most important execution units of AUV and load is most heavy,
Once it breaks down directly affects the safety of AUV, it is accurate that the fault tolerant control method based on thrust secondary distribution needs mostly
Propeller fault degree.AUV propellers fault degree identification under external disturbance is improved for AUV self-securities are ensured
AUV AUTONOMOUS TASK success rates are significant.
Mean filter is to disturb suppressing method outside simplest, and its algorithm is simple, good in many field denoising effects, but one
As be served only for static or low current intelligence；Finite Impulse Response filter inherits the advantage of analog filter, and can use quick Fu
In leaf transformation realizing, substantially increase arithmetic speed.It is to complete in a frequency domain that FIR filter is filtered denoising to signal
, noise filtering is realized by the different spectral feature of signal and noise, it is adapted to quiet, Dynamic Signal denoising, but denoising is imitated
Really typically, be not as good as mean filter.Both the above method is not suitable for marine environment random external AF panel complicated and changeable
Wavelet transformation is the time frequency analyzing tool for developing rapidly in recent years, and overcoming Fourier transformation can only represent letter
Number frequecy characteristic but be unable to the defect of local message on reflecting time domain, wavelet transformation has the local of time and frequency simultaneously
Analysis feature and multiresolution analysis characteristic, and obtained extensively at aspects such as image procossing, signal filtering and feature extractions
Application.Wavelet de-noising method based on wavelet transformation, according to signal and noise after wavelet decomposition corresponding wavelet systems
Several had different qualities, can well realize that external disturbance suppresses, to improve the accuracy of fault identification result, and by
In the multiband characteristic of wavelet decomposition, the multiband redundancy obtained with regard to AUV propeller failures by multilevel wavelet decomposition is retouched
State.
The method that tradition carries out fault identification based on AUV time-domain signals, due to the impact of random external interference, and AUV
In various degree the corresponding fault signature of thrust loss is in not single variation tendency, and causing trouble identification precision is relatively low.To solve
The relatively low problem of this identification precision, extracts AUV sensors, controller signals time-domain signal and multilamellar little using ENERGY METHOD
The energy value of wavelet coefficient after Wave Decomposition, using it as fault signature, and sets up fault energy matrix, by fault-signal to be measured
Fault energy matrix and the correlation coefficient value of known fault degree fault energy matrix in fault pattern base, reach identification AUV events
Barrier degree simultaneously improves the purpose of fault identification precision.
Therefore, by wavelet decomposition in combination with energy feature, constitute a kind of new autonomous type underwater robot failure and distinguish
Knowledge method, can effectively solving autonomous type underwater robot affected by external disturbance and measurement noise, only by extract time-domain signal
The relatively low problem of nonlinear fault feature identification precision.
The content of the invention
It is an object of the invention to：Overcome the deficiencies in the prior art, there is provided a kind of based on the autonomous of wavelet decomposition and energy
Formula underwater robot fault identification method, solve autonomous type underwater robot is affected by external disturbance and measurement noise, is only extracted
The relatively low problem of time-domain signal nonlinear fault feature identification precision.
The purpose of the present invention is achieved through the following technical solutions：Based on multilevel wavelet decomposition method and energy failure
Feature extracting method, realizes that step is as follows：
(1) autonomous type underwater robot sensor and controller data are decomposed using multilevel wavelet decomposition method：
(1.1) data cutout：Start detection algorithm after the doppler data that data length is L is collected, when adopting again
After collecting new data, give up first data of former array and the data that new collection is returned are placed on into the end of former array, all the time
Holding data length is L；
(1.2) wavelet decomposition：The autonomous type underwater robot sensor and controller signals intercepted to step (1.1) is carried out
W layer wavelet decomposition, wavelet basis function is X, obtains corresponding wavelet details coefficient and wavelets approximation coefficient；
(2) primary signal and step (1.2) are obtained wavelet details coefficient using energy failure feature extracting method
Fault signature E is extracted with wavelets approximation coefficient,N is total length of data in formula, and k is concrete data point position,
skFor data kth point value；
(3) fault identification is carried out to autonomous type underwater robot fault-signal to be measured using correlation coefficient method：
(3.1) fault energy matrix is built：According to step (1) the multilevel wavelet decomposition method and steps (2) energy
Amount fault signature extracting method, obtains autonomous type underwater robot sensor and controller signals time-domain signal is little with multilamellar respectively
The energy feature of wavelet coefficient after Wave Decomposition, using these energy features fault energy matrix is built
In formula：EAUVFor constructed AUV fault energy matrixes,EVAnd EθRepresent respectively a left side promote mainly voltage,
Voltage, longitudinal velocity and bow are promoted mainly to angle time-domain signal energy feature in the right side,WithRespectively
The energy feature that above-mentioned time-domain signal distinguishes Coefficients of Approximation after corresponding wavelet decomposition is represented,WithThe energy feature that above-mentioned time-domain signal distinguishes detail coefficients after corresponding wavelet decomposition is represented respectively；
(3.2) coefficient R is calculated：According to the measured signal fault energy matrix that step (3.1) is obtained, calculate and failure
Coefficient R between sample energy matrix,
X is x-th measured signal, and j is jth kind fault sample in fault pattern base, and R (x, j) is measured signal x and jth
The correlation coefficient of kind of fault sample, m for fault energy matrix line number, n is fault energy matrix column number, ExFor measured signal
The energy feature of correspondence m, n value position, E in fault energy matrixjFor the affiliated failure energy of jth kind fault sample in fault pattern base
The energy feature of moment matrix correspondence m, n value position；
(3.3) according to coefficient R identification of defective degree：The R (x, j) that step (3.2) is calculated is bigger, characterizes to be measured
Signal x is bigger with the degree of correlation of corresponding jth kind fault sample, i.e., thrust loss degree closer to；Otherwise then characterize to be measured
Signal is more kept off with the thrust loss degree of corresponding jth kind fault sample.
Data length L=200 described in step (1.1).
Wavelet decomposition number of plies W=3 described in step (1.2), wavelet basis function " X " is " db1 ".
Fault energy matrix line number m=7 described in step (3.1), columns n=4.
Fault sample number j=6 described in step (3.2), corresponding fault degree is respectively 0%, 10%, 20%, 30%,
40% and 50%.
Present invention beneficial effect compared with prior art is mainly reflected in:AUV time-domain signal failures are only extracted with existing
The fault identification method of feature is compared, and the present invention proposes a kind of new fault identification method, and the method was both efficiently solved
AUV sensors, controller signals receive external influences, the relatively low problem of fault identification precision to utilize multilevel wavelet decomposition again
Multiband characteristic, obtain the redundancy with regard to AUV propeller failures and describe, and by extracting to multiband fault message simultaneously therefore
Barrier feature simultaneously sets up fault energy matrix, improves AUV fault identification precision, and for fault-tolerant controller accurate fault message is provided.
Description of the drawings
Fig. 1 is the fault identification method structured flowchart of the present invention.
Fig. 2 is fault degree autonomous type underwater robot sensor to be measured and controller time-domain signal.
Fig. 3 is autonomous type underwater robot sensor and controller signals third layer wavelet coefficient after multilevel wavelet decomposition.
Fig. 4 is the autonomous type underwater robot measured signal fault energy matrix that the inventive method builds.
Fig. 5 is the inventive method fault identification result.
Fig. 6 is that tradition only extracts autonomous type underwater robot measured signal doppler sensor signal time domain fault signature
Fault identification result.
It is special that Fig. 7 only extracts the right main thruster controller signals time domain failure of autonomous type underwater robot measured signal for tradition
The fault identification result levied.
Specific embodiment
The invention provides a kind of autonomous type underwater robot propeller fault degree discrimination method based on wavelet energy,
Multilevel wavelet decomposition is carried out especially by autonomous type underwater robot status signal, to suppress random external interference accurate to identification
The really impact of property, and the failure-description in the multiple frequency ranges of autonomous type underwater robot propeller failure is obtained, so as to improve failure
The accuracy of identification；The wavelets approximation coefficient for obtaining to multilevel wavelet decomposition simultaneously and wavelet details coefficient extract energy, composition
Fault energy matrix, by the energy failure feature square in the sample energy matrix set up by tank experiments with early stage phase is calculated
Relation number, so as to obtain autonomous type underwater robot propeller fault identification result.The present invention solves autonomous type underwater robot
Due to by random external interference effect, the not high problem of propeller fault degree accuracy, suppressing random external interference effect
While extract energy nonlinear fault feature composition fault energy matrix, obtain being described with regard to the redundancy of propeller failure, enter
And autonomous type underwater robot fault identification precision is improved, can be used for autonomous type underwater robot propeller fault identification, fault-tolerant
The fields such as control.
The purpose of the present invention is achieved through the following technical solutions：Based on multilevel wavelet decomposition method and energy failure
Feature extracting method, realizes that step is as follows：
(1) first, the data for collecting to autonomous type underwater robot carry out sliding window process, when collecting data length
To start detection algorithm after the sensor and controller signals of L=200, after new data are collected again, give up former array
The data that new collection is returned simultaneously are placed on the end of former array by first data, remain that data length is L=200；
(2) multilevel wavelet decomposition is carried out to data in the array in sliding window.Catabolic process：Select a kind of suitable small echo
Basic function " db1 ", determines Decomposition order for 3 layers, to sensor raw data and the controlled quentity controlled variable with sensor signal height correlation
Multilevel wavelet decomposition is carried out, corresponding detail wavelet coefficients is obtained and is approached wavelet coefficient；
(3) wavelet details coefficient primary signal and step (2) obtained using energy extraction method and wavelets approximation
Coefficient extracts fault signature E,In formula N be total length of data, k be concrete data point position, skFor data
K point values；
(4) according to above-mentioned steps, autonomous type underwater robot sensor, controller signals time domain energy feature are obtained, with
And detail wavelet coefficients and approach the energy feature of wavelet coefficient after multilevel wavelet decomposition, with structure fault energy matrixIn formula：EAUVFor constructed AUV fault energy matrixes,
EVAnd EθA left side represented respectively promote mainly voltage, the right side promote mainly voltage, longitudinal velocity and bow to angle time-domain signal energy feature,WithAbove-mentioned time-domain signal is represented respectively distinguishes Coefficients of Approximation after corresponding wavelet decomposition
Energy feature,WithAbove-mentioned time-domain signal is represented respectively distinguishes thin after corresponding wavelet decomposition
The energy feature of section coefficient.
(5) the measured signal fault energy matrix obtained according to step (4), calculates it between fault sample energy matrix
Coefficient R, sample energy matrix is to be extracted respectively according to known thrust loss degree fault-signal Jing steps (1)～(4)
Energy feature and building constituted after fault energy matrix, be respectively 0% by thrust loss degree, 10%, 20%, 30%,
40% and 50% totally 6 kinds of fault samples composition.Correlation coefficientIn formula：X is xth
Individual measured signal, j is jth kind fault sample in fault pattern base, and R (x, j) is the phase of measured signal x and jth kind fault sample
Relation number, m for fault energy matrix line number, n be fault energy matrix column number, ExFor in measured signal fault energy matrix
The energy feature of correspondence m, n value position, EjFor jth kind fault sample in fault pattern base affiliated fault energy matrix correspondence m, n
The energy feature of value position.
(6) correlation coefficient value R (x, j) calculated according to step (5), R (x, j) is bigger, characterize measured signal x with it is right
The degree of correlation of the jth kind fault sample answered is bigger, i.e., thrust loss degree closer to；Otherwise then characterize measured signal with it is corresponding
The thrust loss degree of jth kind fault sample more keep off, obtain final fault identification result i.e. thrust loss degree.
The autonomous type underwater robot fault identification method of the present invention is illustrated with reference to Fig. 1 to Fig. 6.The present invention is carried
Fault identification method flow chart is as shown in figure 1, its specific implementation step is as follows：
1st, first, initial data is intercepted using sliding window, initial data is as shown in Figure 2.It is long when data are collected
Spend after the doppler data for L=200 and start detection algorithm, after new data are collected again, give up former array first
The data that new collection is returned simultaneously are placed on the end of former array by data, remain that data length is L.
2 and then, to sliding window intercept signal carry out multilevel wavelet decomposition, specific practice is：Db1 wavelet basis functions are taken,
Decomposition order is 3 layers, and multilevel wavelet decomposition is carried out to data in sliding window with db1 wavelet basis functions, obtains corresponding wavelet systems
Number, the third layer wavelet approximation coefficients and detail coefficients result for obtaining are as shown in Figure 3.
3. the wavelet details coefficient and small echo for then being obtained to primary signal and step (2) using energy extraction method is forced
Nearly coefficient extracts fault signature E,In formula N be total length of data, k be concrete data point position, skFor data
Kth point value.
4. the AUV fault-signal time domain energy features that and then according to step (3) obtain, detail wavelet coefficients energy feature with
And wavelet coefficient energy feature is approached, build measured signal energy energy matrix as shown in Figure 4.
5. the measured signal fault energy matrix for being obtained according to step (4), calculates it between fault sample energy matrix
Coefficient R,In formula：X is x-th measured signal, and j is that early stage passes through water
Jth kind fault sample in the fault pattern base that pond experimental data is set up, the present invention takes j=6, and R (x, j) is measured signal x and jth
The correlation coefficient of kind of fault sample, m for fault energy matrix line number, n is fault energy matrix column number, ExFor measured signal
The energy feature of correspondence m, n value position, E in fault energy matrixjFor the affiliated failure energy of jth kind fault sample in fault pattern base
The energy feature of moment matrix correspondence m, n value position.
6. the R (x, j) that step (5) is calculated is bigger, characterizes the phase of measured signal x and corresponding jth kind fault sample
Pass degree is bigger, thrust loss degree closer to, i.e., fault identification result be the corresponding thrust loss degree of R (x, j) maximum,
As shown in figure 5, as seen from the figure, correlation coefficient maximum is located at thrust loss 30% to the inventive method fault identification result
Place, and is actually consistent.Therefore, to the failure that the actual thrust extent of damage is 30%, identification result is thrust damage to the inventive method
30% is lost, identification precision is higher.As carried out faults-tolerant control to autonomous type underwater robot based on the identification result, can effectively ensure that
The effect of faults-tolerant control.
Fig. 6 is that the failure of tradition only extraction autonomous type underwater robot measured signal single-sensor time domain fault signature is distinguished
Know result.As seen from the figure, because the factors such as random external interference, sensor itself error affect, correlation coefficient maximum
At thrust loss 40%, therefore, traditional method is thrust to the failure that the actual thrust extent of damage is 30%, identification result
Loss 40%, identification precision is relatively low.As carried out faults-tolerant control, control accuracy to autonomous type underwater robot based on the identification result
It is difficult to ensure that.
Fig. 7 is that the failure of tradition only extraction autonomous type underwater robot measured signal Single Controller time domain fault signature is distinguished
Know result.As seen from the figure, it is that at fault-free, i.e., identification result is thrust that correlation coefficient maximum is located at thrust loss 0%
Loss 0%, identification precision is relatively low.
In sum, the present invention is intercepted initially with sliding window method to initial data, and then initial data is entered
Row multilevel wavelet decomposition；The energy value for extracting primary signal, detail wavelet coefficients respectively and approaching wavelet coefficient is special as failure
Levy, constitute fault energy matrix；Measured signal fault energy matrix is calculated with known thrust loss degree sample in fault sample storehouse
The correlation coefficient of this fault energy matrix, the corresponding thrust loss degree of correlation coefficient maximum point is measured signal failure journey
Degree, realizes the fault identification of autonomous type underwater robot.Autonomous type underwater robot faults-tolerant control precision can be finally improved, is one
Plant new, effective autonomous type underwater robot fault identification method.
The above is only the concrete application example of the present invention, protection scope of the present invention is not limited in any way.It can expand
Exhibition is applied to the application of all autonomous type underwater robot fault diagnosises, all employing equivalents or equivalence replacement and shape
Into technical scheme, all fall within this obviously within rights protection scope.It is public that what the present invention was not elaborated partly belongs to this area
Know technology.
Claims (5)
1. a kind of autonomous type underwater robot fault identification method based on wavelet energy, it is characterised in that realize that step is as follows：
(1) autonomous type underwater robot sensor and controller data are decomposed using multilevel wavelet decomposition method：
(1.1) data cutout：Start detection algorithm after the controller data that data length is L is collected, when collecting again
After new data, give up first data of former array and the data that new collection is returned are placed on into the end of former array, remain
Data length is L；
(1.2) wavelet decomposition：The autonomous type underwater robot sensor and controller data intercepted to step (1.1) carries out W layers
Wavelet decomposition, wavelet basis function is X, obtains corresponding wavelet details coefficient and wavelets approximation coefficient；
(2) the wavelet details coefficient and small echo for being obtained to primary signal and step (1.2) using energy feature extraction method is forced
Nearly coefficient extracts energy feature E,In formula N be total length of data, k be concrete data point position, skFor data
Kth point value；
(3) fault identification is carried out to autonomous type underwater robot fault-signal to be measured using correlation coefficient method：
(3.1) energy feature matrix is built：It is special according to step (1) the multilevel wavelet decomposition method and steps (2) energy
Extracting method is levied, is obtained respectively after autonomous type underwater robot sensor and controller signals time-domain signal and multilevel wavelet decomposition
The energy feature of wavelet coefficient, using these energy features energy feature matrix is built
In formula：EAUVFor constructed AUV energy feature matrixes,EVAnd EθA left side is represented respectively promote mainly voltage, the right side promote mainly
Voltage, longitudinal velocity and bow to angle time-domain signal energy feature,WithOn representing respectively
The energy feature that time-domain signal distinguishes Coefficients of Approximation after corresponding wavelet decomposition is stated,WithRespectively
Represent the energy feature that above-mentioned time-domain signal distinguishes detail coefficients after corresponding wavelet decomposition；
(3.2) coefficient R is calculated：The energy feature matrix of the measured signal obtained according to step (3.1), calculates and failure sample
Coefficient R between eigen matrix,
X is x-th measured signal, and j is jth kind fault sample in fault pattern base, and R (x, j) is measured signal x and the event of jth kind
Barrier sample correlation coefficient, m for energy feature matrix line number, n be energy feature matrix column number, ExFor the energy of measured signal
The energy feature of correspondence m, n value position, E in measure feature matrixjIt is special for the affiliated energy of jth kind fault sample in fault pattern base
Levy the energy feature of matrix correspondence m, n value position；
(3.3) according to coefficient R identification of defective degree：The R (x, j) that step (3.2) is calculated is bigger, characterizes measured signal
X is bigger with the degree of correlation of corresponding jth kind fault sample, i.e., thrust loss degree closer to；Otherwise then characterize measured signal
More keep off with the thrust loss degree of corresponding jth kind fault sample.
2. a kind of autonomous type underwater robot fault identification method based on wavelet energy according to claim 1, it is special
Levy and be：Data length L=200 described in step (1.1).
3. a kind of autonomous type underwater robot fault identification method based on wavelet energy according to claim 1, it is special
Levy and be：Wavelet decomposition number of plies W=3 described in step (1.2), wavelet basis function " X " is " db1 ".
4. a kind of autonomous type underwater robot fault identification method based on wavelet energy according to claim 1, it is special
Levy and be：Energy feature matrix line number m=7 described in step (3.1), columns n=4.
5. a kind of autonomous type underwater robot fault identification method based on wavelet energy according to claim 1, it is special
Levy and be：Fault sample number j=6 described in step (3.2), corresponding fault degree is respectively 0%, 10%, 20%, 30%,
40% and 50%.
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| CN105095918B (en) * | 2015-09-07 | 2018-06-26 | 上海交通大学 | A kind of multi-robot system method for diagnosing faults |
| CN107132760B (en) * | 2017-03-27 | 2020-02-11 | 哈尔滨工程大学 | Underwater robot state monitoring method based on fuzzy support vector field description |
| CN107860958B (en) * | 2017-09-27 | 2020-05-26 | 江苏科技大学 | Noise reduction method for real-time monitoring current value of underwater robot propeller and real-time monitoring system thereof |
| CN108036941B (en) * | 2017-12-26 | 2019-10-22 | 浙江大学 | A kind of steam turbine bearing abnormal vibration analysis method based on correlation visual analysis |
| CN109683591B (en) * | 2018-12-27 | 2021-03-19 | 江苏科技大学 | Underwater propeller fault degree identification method based on fusion signal time domain energy and time-frequency entropy |
| CN109696906B (en) * | 2018-12-27 | 2021-06-11 | 江苏科技大学 | Underwater robot propeller fault diagnosis method based on wavelet correction Bayes convolution energy |
| CN110716532A (en) * | 2019-10-17 | 2020-01-21 | 哈尔滨工程大学 | Underwater robot propeller weak fault identification method based on wavelet packet energy and FFT |
| CN111091159B (en) * | 2019-12-27 | 2023-03-17 | 哈尔滨工程大学 | Weak fault feature extraction method for autonomous underwater robot propeller |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101344460A (en) * | 2007-08-10 | 2009-01-14 | 上海海事大学 | Underwater robot sensor fault diagnosis method and system |
| CN101464935A (en) * | 2009-01-09 | 2009-06-24 | 哈尔滨工程大学 | AUV intelligent fault-tolerance combined navigation simulation system based on network |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6941177B2 (en) * | 2002-12-17 | 2005-09-06 | Xerox Corporation | System and method for implementing real-time applications based on stochastic compute time algorithms |
-
2014
- 2014-11-27 CN CN201410705681.1A patent/CN104503432B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101344460A (en) * | 2007-08-10 | 2009-01-14 | 上海海事大学 | Underwater robot sensor fault diagnosis method and system |
| CN101464935A (en) * | 2009-01-09 | 2009-06-24 | 哈尔滨工程大学 | AUV intelligent fault-tolerance combined navigation simulation system based on network |
Non-Patent Citations (1)
| Title |
|---|
| 水下机器人多传感器并发故障检测方法;张铭钧等;《机器人》;20100531;第32卷(第3期);第298-305页 * |
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