CN104503432A - Autonomous underwater robot fault identification method based on wavelet energy - Google Patents

Autonomous underwater robot fault identification method based on wavelet energy Download PDF

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CN104503432A
CN104503432A CN201410705681.1A CN201410705681A CN104503432A CN 104503432 A CN104503432 A CN 104503432A CN 201410705681 A CN201410705681 A CN 201410705681A CN 104503432 A CN104503432 A CN 104503432A
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underwater robot
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energy
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CN104503432B (en
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张铭钧
刘维新
刘星
殷宝吉
王玉甲
赵文德
姚峰
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Harbin Engineering University
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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

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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

A kind of autonomous type underwater robot fault identification method based on wavelet energy
Technical field
The present invention relates to autonomous type underwater robot fault identification and Fault Tolerance Control Technology field, be specifically related to a kind of autonomous type underwater robot fault identification method based on wavelet energy.
Background technology
Along with land resources day by day reduces, the paces of human development ocean are more and more faster.Autonomous type underwater robot (AUV:Autonomous Underwater Vehicle) is the carrier that uniquely can carry out at deep-sea at present detecting, developing in unmanned situation, is subject to the great attention of domestic and international researchist always.Thruster is the most important execution unit of AUV and load is the heaviest, once it breaks down directly affect the security of AUV, the fault tolerant control method based on thrust secondary distribution needs thruster fault degree accurately mostly.AUV thruster fault degree identification under external disturbance, for guarantee AUV self-security, improves AUV AUTONOMOUS TASK success ratio significant.
Mean filter the simplyst disturbs suppressing method outward, and its algorithm is simple, good at a lot of fields denoising effect, but general only for static or low current intelligence; Finite Impulse Response filter inherits the advantage of analog filter, and can realize with Fast Fourier Transform (FFT), substantially increases arithmetic speed.FIR filter is carried out filtering and noise reduction to signal and is completed in a frequency domain, rely on the different spectral feature of signal and noise to realize noise filtering, be adapted to quiet, Dynamic Signal denoising, but denoising effect is general, be not as good as mean filter.Above two kinds of methods are all not suitable for marine environment random external AF panel complicated and changeable
Wavelet transformation is the time frequency analyzing tool developed rapidly in recent years, overcome Fourier transform can only represent signal frequecy characteristic but can not the defect of local message on reflecting time territory, wavelet transformation has partial analysis feature and the multiresolution analysis characteristic of time and frequency simultaneously, and obtains a wide range of applications in image procossing, signal filtering and feature extraction etc.Wavelet de-noising method is based on wavelet transformation, the different qualities that the wavelet coefficient of correspondence after wavelet decomposition has according to signal and noise, external disturbance can be realized well suppress, to improve the accuracy of fault identification result, and due to the multiband characteristic of wavelet decomposition, the multiband redundancy obtained about AUV thruster fault by multilevel wavelet decomposition is described.
Tradition carries out the method for fault identification based on AUV time-domain signal, due to the impact of random external interference, and the AUV fault signature that thrust loss is corresponding is in various degree not in single variation tendency, and causing trouble identification precision is lower.For solving the lower problem of this identification precision, the energy value of wavelet coefficient after employing ENERGY METHOD extraction AUV sensor, controller signals time-domain signal and multilevel wavelet decomposition, using as fault signature, and set up fault signature matrix, by the correlation coefficient value of known fault degree fault signature matrix in fault-signal fault signature matrix to be measured and fault pattern base, reach identification AUV fault degree and improve the object of fault identification precision.
Therefore, wavelet decomposition is combined with energy feature, form a kind of novel autonomous type underwater robot fault identification method, effectively can solve autonomous type underwater robot affects by external disturbance and measurement noise, by means of only extracting the lower problem of time-domain signal nonlinear fault feature identification precision.
Summary of the invention
The object of the invention is to: overcome the deficiencies in the prior art, a kind of autonomous type underwater robot fault identification method based on wavelet decomposition and energy is provided, solve autonomous type underwater robot to affect by external disturbance and measurement noise, only extract the problem that time-domain signal nonlinear fault feature identification precision is lower.
The object of the invention is to be achieved through the following technical solutions: based on multilevel wavelet decomposition method and energy failure feature extracting method, performing step is as follows:
(1) multilevel wavelet decomposition method is adopted to decompose autonomous type underwater robot sensor and controller data:
(1.1) data cutout: when collect data length be the doppler data of L after start detection algorithm, after again collecting new data, give up former array first data and will newly gather the data of returning and be placed on the end of former array, remaining that data length is L;
(1.2) wavelet decomposition: the autonomous type underwater robot sensor intercept step (1.1) and controller signals carry out W layer wavelet decomposition, and wavelet basis function is X, obtains corresponding wavelet details coefficient and wavelets approximation coefficient;
(2) the wavelet details coefficient adopting energy failure feature extracting method to obtain original signal and step (1.2) and wavelets approximation coefficient extract fault signature E, in formula, N is data total length, and k is concrete data point position, s kfor data kth point value;
(3) related coefficient method is adopted to carry out fault identification to autonomous type underwater robot fault-signal to be measured:
(3.1) fault signature matrix is built: according to step (1) described multilevel wavelet decomposition method and the described energy failure feature extracting method of step (2), obtain the energy feature of wavelet coefficient after autonomous type underwater robot sensor and controller signals time-domain signal and multilevel wavelet decomposition respectively, adopt these energy features structure fault signature matrix
E AUV = E U L E U R E V E θ E U L Cj ( k ) E U R Cj ( k ) E V Cj ( k ) E θ Cj ( k ) E U L Dj ( K ) E U R Dj ( k ) E V Dj ( k ) E θ Dj ( k ) m × n ,
In formula: E aUVfor constructed AUV fault signature matrix, e vand E θrepresent that voltage is promoted mainly on a left side, voltage is promoted mainly on the right side respectively, longitudinal velocity and bow to angle time-domain signal energy feature, with represent the energy feature of Coefficients of Approximation after the respectively corresponding wavelet decomposition of above-mentioned time-domain signal respectively, with represent the energy feature of detail coefficients after the respectively corresponding wavelet decomposition of above-mentioned time-domain signal respectively;
(3.2) calculate coefficient R: the measured signal fault signature matrix obtained according to step (3.1), calculate the coefficient R between fault sample eigenmatrix,
R ( x , j ) = 1 / 1 m × n Σ m Σ n ( E x - E j ) 2 ,
X is an xth measured signal, and j is jth kind fault sample in fault pattern base, and the related coefficient that R (x, j) is measured signal x and jth kind fault sample, m is the line number of fault signature matrix, and n is fault signature matrix column number, E xfor the energy feature of m, n value position corresponding in measured signal fault signature matrix, E jfor the energy feature of corresponding m, n value of fault signature matrix belonging to jth kind fault sample position in fault pattern base;
(3.3) according to coefficient R identification of defective degree: the R (x, j) that step (3.2) calculates is larger, characterize measured signal x larger with the degree of correlation of corresponding jth kind fault sample, namely thrust loss degree is more close; Otherwise then characterize measured signal more to keep 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 signature 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%.
The present invention's beneficial effect is compared with prior art mainly reflected in: compared with the existing fault identification method only extracting AUV time-domain signal fault signature, the present invention proposes a kind of novel fault identification method, the method had both efficiently solved AUV sensor, controller signals is by external influences, the problem that fault identification precision is lower, utilize again the multiband characteristic of multilevel wavelet decomposition, the redundancy obtained about AUV thruster fault describes, and by extracting fault signature to multiband failure message and set up fault signature matrix simultaneously, improve AUV fault identification precision, for fault-tolerant controller provides failure message accurately.
Accompanying drawing explanation
Fig. 1 is 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 signature matrix that the inventive method builds.
Fig. 5 is the inventive method fault identification result.
Fig. 6 is the fault identification result that tradition only extracts autonomous type underwater robot measured signal doppler sensor signal time domain fault signature.
Fig. 7 is the fault identification result that tradition only extracts autonomous type underwater robot measured signal right main thruster controller signals time domain fault signature.
Embodiment
The invention provides a kind of autonomous type underwater robot thruster fault degree discrimination method based on wavelet energy, multilevel wavelet decomposition is carried out especially by autonomous type underwater robot status signal, to suppress the impact of random external interference on identification accuracy, and the failure-description obtained in the multiple frequency range of autonomous type underwater robot thruster fault, thus improve the accuracy of fault identification; Simultaneously to wavelets approximation coefficient and the wavelet details coefficient extraction energy of multilevel wavelet decomposition acquisition, composition fault signature matrix, by calculating related coefficient with the energy failure feature square in the sample characteristics matrix in earlier stage set up by tank experiments, thus obtain autonomous type underwater robot thruster fault identification result.The present invention solves autonomous type underwater robot owing to being subject to random external disturbing effect, the problem that the accuracy of thruster fault degree is not high, energy nonlinear fault feature composition fault signature matrix is extracted while suppression random external disturbing effect, the redundancy obtained about thruster fault describes, and then improve autonomous type underwater robot fault identification precision, can be used for the fields such as autonomous type underwater robot thruster fault identification, faults-tolerant control.
The object of the invention is to be achieved through the following technical solutions: based on multilevel wavelet decomposition method and energy failure feature extracting method, performing step is as follows:
(1) first, sliding window process is carried out to the data that autonomous type underwater robot collects, start detection algorithm after collecting the sensor and controller signals that data length is L=200, after again collecting new data, give up former array first data and will newly gather the data of returning and be placed on the end of former array, remaining that data length is L=200;
(2) multilevel wavelet decomposition is carried out to data in the array in sliding window.Decomposable process: selected a kind of suitable wavelet basis function " db1 ", determine that Decomposition order is 3 layers, to sensor raw data and and the controlled quentity controlled variable of sensor signal height correlation carry out multilevel wavelet decomposition, obtain corresponding detail wavelet coefficients and approach wavelet coefficient;
(3) the wavelet details coefficient adopting energy extraction method to obtain original signal and step (2) and wavelets approximation coefficient extract fault signature E, in formula, N is data total length, and k is concrete data point position, s kfor data kth point value;
(4) according to above-mentioned steps, obtain autonomous type underwater robot sensor, controller signals time domain energy feature, and detail wavelet coefficients and approach the energy feature of wavelet coefficient after multilevel wavelet decomposition, with structure fault signature matrix E AUV = E U L E U R E V E θ E U L Cj ( k ) E U R Cj ( k ) E V Cj ( k ) E θ Cj ( k ) E U L Dj ( k ) E U R Dj ( k ) E V Dj ( k ) E θ Dj ( k ) , In formula: E aUVfor constructed AUV fault signature matrix, e vand E θrepresent that voltage is promoted mainly on a left side, voltage is promoted mainly on the right side respectively, longitudinal velocity and bow to angle time-domain signal energy feature, with represent the energy feature of Coefficients of Approximation after the respectively corresponding wavelet decomposition of above-mentioned time-domain signal respectively, with represent the energy feature of detail coefficients after the respectively corresponding wavelet decomposition of above-mentioned time-domain signal respectively.
(5) according to the measured signal fault signature matrix that step (4) obtains, calculate the coefficient R between itself and fault sample eigenmatrix, sample characteristics matrix by extracting energy feature according to known thrust loss degree fault-signal respectively through step (1) ~ (4) and being formed after building fault signature matrix, by thrust loss degree be respectively 0%, 10%, 20%, 30%, 40% and 50% totally 6 kinds of fault samples form.Related coefficient in formula: x is an xth measured signal, j is jth kind fault sample in fault pattern base, and the related coefficient that R (x, j) is measured signal x and jth kind fault sample, m is the line number of fault signature matrix, and n is fault signature matrix column number, E xfor the energy feature of m, n value position corresponding in measured signal fault signature matrix, E jfor the energy feature of corresponding m, n value of fault signature matrix belonging to jth kind fault sample position in fault pattern base.
(6) according to the correlation coefficient value R (x, j) that step (5) calculates, R (x, j) is larger, and characterize measured signal x larger with the degree of correlation of corresponding jth kind fault sample, namely thrust loss degree is more close; Otherwise then characterize measured signal more to keep off with the thrust loss degree of corresponding jth kind fault sample, obtain final fault identification result and thrust loss degree.
Composition graphs 1 is set forth to Fig. 6 autonomous type underwater robot fault identification method of the present invention.The present invention carries fault identification method process flow diagram as shown in Figure 1, and its concrete implementation step is as follows:
1, first, adopt sliding window to intercept to raw data, raw data as shown in Figure 2.When collect data length be the doppler data of L=200 after start detection algorithm, after again collecting new data, give up former array first data and will newly gather the data of returning and be placed on the end of former array, remaining that data length is L.
2, then, multilevel wavelet decomposition is carried out to the signal that sliding window intercepts, specific practice is: get db1 wavelet basis function, Decomposition order is 3 layers, with db1 wavelet basis function, multilevel wavelet decomposition is carried out to data in sliding window, obtain corresponding wavelet coefficient, the third layer wavelet approximation coefficients obtained and detail coefficients result are as shown in Figure 3.
3. the wavelet details coefficient then adopting energy extraction method to obtain original signal and step (2) and wavelets approximation coefficient extract fault signature E, in formula, N is data total length, and k is concrete data point position, s kfor data kth point value.
4. the AUV fault-signal time domain energy feature, the detail wavelet coefficients energy feature that then obtain according to step (3) and approach wavelet coefficient energy feature, build measured signal energy feature matrix as shown in Figure 4.
5., according to the measured signal fault signature matrix that step (4) obtains, calculate the coefficient R between itself and fault sample eigenmatrix, in formula: x is an xth measured signal, j is jth kind fault sample in the fault pattern base set up by tank experiments data early stage, the present invention gets j=6, R (x, j) be the related coefficient of measured signal x and jth kind fault sample, m is the line number of fault signature matrix, and n is fault signature matrix column number, E xfor the energy feature of m, n value position corresponding in measured signal fault signature matrix, E jfor the energy feature of corresponding m, n value of fault signature matrix belonging to jth kind fault sample position in fault pattern base.
6. R (the x that calculates of step (5), j) larger, characterize measured signal x larger with the degree of correlation of corresponding jth kind fault sample, thrust loss degree is more close, and namely fault identification result is thrust loss degree corresponding to R (x, j) maximal value, the inventive method fault identification result as shown in Figure 5, as seen from the figure, related coefficient maximum value is positioned at thrust loss 30% place, conforms to actual.Therefore, the inventive method is the fault of 30% to the actual thrust extent of damage, and identification result is thrust loss 30%, and identification precision is higher.As carried out faults-tolerant control based on this identification result to autonomous type underwater robot, the effect of faults-tolerant control effectively can be ensured.
Fig. 6 is the fault identification result that tradition only extracts autonomous type underwater robot measured signal single-sensor time domain fault signature.As seen from the figure, due to factor impacts such as random external interference, sensor self errors, related coefficient maximum value is positioned at thrust loss 40% place, therefore, classic method is the fault of 30% to the actual thrust extent of damage, and identification result is thrust loss 40%, and identification precision is lower.As carried out faults-tolerant control based on this identification result to autonomous type underwater robot, control accuracy is difficult to ensure.
Fig. 7 is the fault identification result that tradition only extracts autonomous type underwater robot measured signal Single Controller time domain fault signature.As seen from the figure, related coefficient maximum value is positioned at thrust loss 0% i.e. non-fault place, and namely identification result is thrust loss 0%, and identification precision is lower.
In sum, first the present invention adopts sliding window method to intercept raw data, then carries out multilevel wavelet decomposition to raw data; The energy value extracting original signal, detail wavelet coefficients respectively and approach wavelet coefficient as fault signature, composition fault signature matrix; Calculate the related coefficient of known thrust loss degree sample fault signature matrix in measured signal fault signature matrix and fault sample storehouse, the thrust loss degree that related coefficient maximum point is corresponding is measured signal fault degree, realizes the fault identification of autonomous type underwater robot.Finally can improve autonomous type underwater robot faults-tolerant control precision, be a kind of novel, effective autonomous type underwater robot fault identification method.
Below be only embody rule example of the present invention, protection scope of the present invention is not constituted any limitation.Its easily extensible is applied to the application of all autonomous type underwater robot fault diagnosises, the technical scheme that all employing equivalents or equivalence are replaced and formed, and all drops within rights protection scope of the present invention.The part that the present invention does not elaborate belongs to techniques well known.

Claims (5)

1., based on an autonomous type underwater robot fault identification method for wavelet energy, it is characterized in that, performing step is as follows:
(1) multilevel wavelet decomposition method is adopted to decompose autonomous type underwater robot sensor and controller data:
(1.1) data cutout: when collect data length be the doppler data of L after start detection algorithm, after again collecting new data, give up former array first data and will newly gather the data of returning and be placed on the end of former array, remaining that data length is L;
(1.2) wavelet decomposition: the autonomous type underwater robot sensor intercept step (1.1) and controller signals carry out W layer wavelet decomposition, and wavelet basis function is X, obtains corresponding wavelet details coefficient and wavelets approximation coefficient;
(2) the wavelet details coefficient adopting energy failure feature extracting method to obtain original signal and step (1.2) and wavelets approximation coefficient extract fault signature E, in formula, N is data total length, and k is concrete data point position, s kfor data kth point value;
(3) related coefficient method is adopted to carry out fault identification to autonomous type underwater robot fault-signal to be measured:
(3.1) fault signature matrix is built: according to step (1) described multilevel wavelet decomposition method and the described energy failure feature extracting method of step (2), obtain the energy feature of wavelet coefficient after autonomous type underwater robot sensor and controller signals time-domain signal and multilevel wavelet decomposition respectively, adopt these energy features structure fault signature matrix
E AUV = E U L E U R E V E θ E U L Cj ( k ) E U R Cj ( k ) E V Cj ( k ) E θ Cj ( k ) E U L Dj ( k ) E U R Dj ( k ) E V Dj ( k ) E θ Dj ( k ) m × n ,
In formula: E aUVfor constructed AUV fault signature matrix, e vand E θrepresent that voltage is promoted mainly on a left side, voltage is promoted mainly on the right side respectively, longitudinal velocity and bow to angle time-domain signal energy feature, with represent the energy feature of Coefficients of Approximation after the respectively corresponding wavelet decomposition of above-mentioned time-domain signal respectively, with represent the energy feature of detail coefficients after the respectively corresponding wavelet decomposition of above-mentioned time-domain signal respectively;
(3.2) calculate coefficient R: the measured signal fault signature matrix obtained according to step (3.1), calculate the coefficient R between fault sample eigenmatrix,
R ( x , j ) = 1 / 1 m × n Σ m Σ n ( E x - E j ) 2 ,
X is an xth measured signal, and j is jth kind fault sample in fault pattern base, and the related coefficient that R (x, j) is measured signal x and jth kind fault sample, m is the line number of fault signature matrix, and n is fault signature matrix column number, E xfor the energy feature of m, n value position corresponding in measured signal fault signature matrix, E jfor the energy feature of corresponding m, n value of fault signature matrix belonging to jth kind fault sample position in fault pattern base;
(3.3) according to coefficient R identification of defective degree: the R (x, j) that step (3.2) calculates is larger, characterize measured signal x larger with the degree of correlation of corresponding jth kind fault sample, namely thrust loss degree is more close; Otherwise then characterize measured signal more to 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, is characterized in that: the 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 characterized in that: the 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, is characterized in that: the fault signature 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 characterized in that: the 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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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105095918A (en) * 2015-09-07 2015-11-25 上海交通大学 Multi-robot system fault diagnosis method
CN107132760A (en) * 2017-03-27 2017-09-05 哈尔滨工程大学 The underwater robot state monitoring method described based on fuzzy support vector domain
CN107860958A (en) * 2017-09-27 2018-03-30 江苏科技大学 The noise-reduction method and its real-time monitoring system of the real-time monitoring current value of underwater robot propeller
CN108036941A (en) * 2017-12-26 2018-05-15 浙江大学 A kind of steam turbine bearing abnormal vibration analysis method based on correlation visual analysis
CN109683591A (en) * 2018-12-27 2019-04-26 江苏科技大学 Underwater propeller fault degree discrimination method based on fusion signal time domain energy and time-frequency entropy
CN109696906A (en) * 2018-12-27 2019-04-30 江苏科技大学 Underwater robot propeller method for diagnosing faults based on small echo amendment Bayes's 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
CN111091159A (en) * 2019-12-27 2020-05-01 哈尔滨工程大学 Weak fault feature extraction method for autonomous underwater robot propeller

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050203643A1 (en) * 2002-12-17 2005-09-15 Xerox Corporation System and method for implementing real-time applications based on stochastic compute time algorithms
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

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050203643A1 (en) * 2002-12-17 2005-09-15 Xerox Corporation System and method for implementing real-time applications based on stochastic compute time algorithms
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)

* Cited by examiner, † Cited by third party
Title
张铭钧等: "水下机器人多传感器并发故障检测方法", 《机器人》 *

Cited By (14)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105095918B (en) * 2015-09-07 2018-06-26 上海交通大学 A kind of multi-robot system method for diagnosing faults
CN105095918A (en) * 2015-09-07 2015-11-25 上海交通大学 Multi-robot system fault diagnosis method
CN107132760A (en) * 2017-03-27 2017-09-05 哈尔滨工程大学 The underwater robot state monitoring method described based on fuzzy support vector domain
CN107860958A (en) * 2017-09-27 2018-03-30 江苏科技大学 The noise-reduction method and its real-time monitoring system of the real-time monitoring current value of underwater robot propeller
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
CN108036941A (en) * 2017-12-26 2018-05-15 浙江大学 A kind of steam turbine bearing abnormal vibration analysis method based on correlation visual analysis
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CN109683591A (en) * 2018-12-27 2019-04-26 江苏科技大学 Underwater propeller fault degree discrimination method based on fusion signal time domain energy and time-frequency entropy
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
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