A method, system and program product for identifying the fault degree of underwater robot propellers in cases where the fault degree is relatively weak

Abstract

This invention discloses a method, system, and program product for identifying the fault severity of underwater robot thrusters under conditions of relatively mild faults, belonging to the field of underwater robot fault diagnosis technology. The invention first obtains a thrust deviation curve based on the bow angle and lateral thruster control voltage. Then, based on different parts of the curve, it uses difference and multi-time-window sliding Fourier transform methods in the time and frequency domains respectively to obtain multiple thrust losses. Next, it introduces a kernel density estimation method to obtain the time-domain and frequency-domain fault severity probability density curves based on the thrust losses obtained in the time and frequency domains. Finally, it uses a method of averaging at the same location to fuse the curves, and identifies the fault severity based on the fused fault severity probability density curves. This invention achieves high identification accuracy and is particularly suitable for fault severity identification in ocean current environments and under conditions of relatively mild faults.

Description

Technical Field

[0001] This invention relates to the field of underwater robot fault diagnosis, specifically to a method, system, and program product for identifying the degree of fault in underwater robot thrusters when the fault is relatively minor. Background Technology

[0002] Underwater robots have a wide range of applications due to their ability to navigate freely in complex marine environments, such as underwater exploration, underwater search, and autonomous operation. Reliable fault diagnosis is a crucial step in ensuring the successful completion of tasks by underwater robots, and fault feature extraction is an important component of fault diagnosis. Existing fault feature extraction methods employ neural networks and support vector machines to classify fault severity based on multiple signals, or use weighted grey relational analysis to quantitatively identify fault severity.

[0003] Existing fault severity identification methods, such as neural networks and support vector machines, can only classify fault severity and cannot perform quantitative analysis. While weighted grey relational analysis can quantitatively identify fault severity, its accuracy is low in weak fault conditions due to the extraction of fault features such as standard deviation, which are significantly affected by ocean current interference. To address these issues, the applicant's research first calculates the thrust loss caused by multiple faults in the time and frequency domains, then obtains the probability density distribution of thrust loss, resulting in time-domain and frequency-domain fault severity probability density curves. Finally, the two probability density curves are merged to obtain the final probability density curve, which is used to identify fault severity, thus solving the problems of typical methods. Therefore, this research is significant. Summary of the Invention

[0004] The purpose of this invention is to provide a method, system, and program product for identifying the fault level of underwater robot thrusters under conditions of relatively minor fault severity. It can identify the fault level of underwater robot thrusters in ocean current environments with relatively low fault severity, and has high identification accuracy.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] This invention provides a method for identifying the degree of failure in underwater robot thrusters, comprising the following steps:

[0007] Step 1: Based on the known fault severity data, the thrust deviation curve is obtained according to the underwater robot dynamics model based on the bow angle and the lateral thruster control voltage. The difference method is used to obtain multiple time-domain thrust losses based on the thrust deviation curve. Based on the thrust loss, the kernel density estimation method is used to obtain the thrust loss probability density curve, and the time-domain thrust loss benchmark is obtained.

[0008] Step 2: For the fault severity data to be identified, the thrust deviation curve is obtained based on the bow angle and lateral thruster control voltage according to the underwater robot dynamics model. The difference method is used to obtain multiple time-domain thrust losses based on the thrust deviation curve. The kernel density estimation method is used to obtain the thrust loss probability density curve based on the thrust loss. Finally, the time-domain fault severity probability density curve is obtained based on the known fault severity thrust loss benchmark in the time domain.

[0009] Step 3: Based on the known fault severity data, the multi-time-window sliding Fourier transform method is used to obtain multiple frequency domain thrust losses based on the thrust deviation curve, thus obtaining the frequency domain thrust loss benchmark.

[0010] Step 4: For the fault severity data to be identified, the difference method is used to obtain multiple frequency domain thrust losses based on the thrust deviation curve, and the frequency domain fault severity probability density curve is obtained based on the thrust loss benchmark in the frequency domain.

[0011] Step 5: Combining the results obtained in Steps 2 and 4, the time-domain and frequency-domain fault probability density curves are fused using the same position averaging method to obtain the final fault probability density curve, and the fault degree is identified based on this curve.

[0012] A computer device / equipment / system includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of a method for identifying the degree of failure of an underwater robot thruster under conditions of relatively weak failure.

[0013] A computer program product includes a computer program / instructions that, when executed by a processor, implement steps of a method for identifying the degree of failure of an underwater robot thruster under conditions of relatively minor failure.

[0014] The beneficial effects of this invention are as follows:

[0015] The advantages of this invention compared to existing technologies are mainly reflected in the following: existing fault severity identification methods do not consider the influence of random interference such as ocean currents. Therefore, in the case of weak faults, the fault signal is significantly affected by interference from ocean currents, resulting in low accuracy in fault severity identification. To address this research background, this invention proposes a fault severity identification method based on probability density distribution. To reduce the influence of random interference such as ocean currents, multiple thrust losses are first obtained in the time and frequency domains using difference and multi-time-window sliding Fourier transform methods, respectively. Then, based on the probability density distribution of thrust losses, the probability density curves of fault severity in the time and frequency domains are obtained. Finally, the method of averaging at the same location is used to fuse the curves, obtaining the final probability density curve of fault severity and identifying the fault severity. This reduces the uncertainty caused by random interference and improves the accuracy of fault severity identification. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the fault severity identification process of the present invention.

[0017] Figure 2 The probability density curve of the failure degree obtained by the method of this invention under a 5% failure condition. Detailed Implementation

[0018] The present invention will now be further described with reference to the accompanying drawings.

[0019] according to Figure 1 This invention provides a method for identifying the fault severity of underwater robot thrusters when the fault severity is relatively low. The specific steps are as follows:

[0020] A method for identifying the degree of failure in underwater robot thrusters includes the following steps:

[0021] Step 1: Based on the known fault severity data, the thrust deviation curve is obtained according to the underwater robot dynamics model based on the bow angle and the lateral thruster control voltage. The difference method is used to obtain multiple time-domain thrust losses based on the thrust deviation curve. Based on the thrust loss, the kernel density estimation method is used to obtain the thrust loss probability density curve, and the time-domain thrust loss benchmark is obtained.

[0022] Based on the bow angle and lateral thruster control voltage, the thrust deviation curve, consisting of the difference between theoretical and actual thrust, is obtained. According to the time of the fault occurrence, data before and after that time are extracted to obtain normal data and fault data, as shown in formulas (1) and (2):

[0023] T n (i)=T(b-150+i) (1)

[0024] T f (i)=T(b-150+i) (2)

[0025] In the formula, T is the thrust deviation, T n This is normal data, T f This is fault data, where b is the number of cycles corresponding to the time the fault occurred;

[0026] The difference between each cycle of normal data and each cycle of fault data is calculated to obtain multiple thrust losses. The calculation method is shown in formula (3):

[0027] T d =T f (p)-T n (q) (3)

[0028] In the formula, T d It is thrust loss;

[0029] The probability density curves of thrust loss are obtained using the kernel density estimation method, as shown in equations (4) and (5):

[0030]

[0031] In the formula, It is the estimated density function, x i These are univariate independent and identically distributed samples drawn from a distribution with unknown density f. In this paper, they are the inference difference T. d K is the kernel function, and this paper uses the Gaussian kernel function. h is the bandwidth of the smoothing function, and σ is the standard deviation of x.

[0032] Based on the obtained probability density estimation curve The horizontal axis of the curve represents thrust loss, and the vertical axis represents the probability of that thrust loss. The thrust loss corresponding to the peak position of the curve is calculated and denoted as T. dm The calculation method is shown in formula (6):

[0033]

[0034] Based on formulas (1-6), the thrust loss corresponding to the peak positions of the probability density curves for 20% and 10% of the fault data is calculated, and denoted as T. dmk20 and T dmk10 , serving as a benchmark for thrust loss at different levels of failure;

[0035] Step 2: For the fault severity data to be identified, the thrust deviation curve is obtained based on the bow angle and lateral thruster control voltage according to the underwater robot dynamics model. The difference method is used to obtain multiple time-domain thrust losses based on the thrust deviation curve. The kernel density estimation method is used to obtain the thrust loss probability density curve based on the thrust loss. Finally, the time-domain fault severity probability density curve is obtained based on the known fault severity thrust loss benchmark in the time domain.

[0036] According to formula (1-5), the thrust loss probability density curve is obtained. The magnitude of thrust loss T corresponding to the peak position of the curve is obtained according to formula (6). dmi ;

[0037] The thrust loss probability density curve of the data to be identified The method to convert the thrust loss probability density curve into a probability percentage is to convert the horizontal axis of the thrust loss probability density curve into a probability percentage; the thrust loss T data to be identified is then converted into a probability percentage. dmi Thrust loss benchmark T with known fault severity dk20 and T dk10 Compare when 0 ≤ T dmi ≤T dmk10When the fault severity is determined to be within 10%, the probability density curve is... The horizontal axis is converted to a similar fault degree using formula (7), when T dmk10 ≤T dmi ≤T dmk20 When the fault severity is determined to be between 10% and 20%, formula (8) is used for calculation, and the fault severity probability density curve based on the kernel density estimation method is obtained using the above method.

[0038]

[0039] Where D k T represents the percentage of failure severity, and is used as the abscissa of the failure severity probability density curve. dk ′ represents the x-coordinate of a point on the thrust loss probability density curve of the data to be identified, and represents the magnitude of the thrust loss;

[0040] Step 3: Based on the known fault severity data, the multi-time-window sliding Fourier transform method is used to obtain multiple frequency domain thrust losses based on the thrust deviation curve, thus obtaining the frequency domain thrust loss benchmark.

[0041] The lowest frequency amplitude curve is obtained by performing a sliding Fourier transform on the thrust deviation curve, as shown in formula (9):

[0042]

[0043] In the formula, f is the lowest frequency amplitude, and M is the number of signals within the time window;

[0044] According to formula (9), multiple time windows are used to obtain multiple minimum frequency amplitude curves;

[0045] Based on multiple lowest frequency amplitude curves, the thrust loss benchmarks for different frequencies are calculated as shown in formula (10):

[0046]

[0047] In the formula, T df As a reference for thrust loss, f max The peak value of the lowest frequency amplitude curve is represented by u, where u is the frequency.

[0048] Based on the data with known fault severity, multiple thrust losses corresponding to fault severity levels of 20% and 10% are calculated using the steps described above, serving as the baseline T for thrust losses at different fault severity levels. df20 (u) and T df10 (u); For fault-free data, the thrust loss baseline T is calculated by taking the peak size of the lowest frequency amplitude curve. df0 (u);

[0049] Step 4: For the fault severity data to be identified, the difference method is used to obtain multiple frequency domain thrust losses based on the thrust deviation curve, and the frequency domain fault severity probability density curve is obtained based on the thrust loss benchmark in the frequency domain.

[0050] Based on the thrust deviation curve of the data to be identified, multiple thrust losses T are obtained according to formulas (9) and (10). df ′(u);

[0051] Based on thrust loss benchmark T df20 (u), T df10 (u) and T df0 (u), thrust loss T for the fault data to be identified df ′(u), the fault degree corresponding to different frequencies is obtained, as shown in formula (11):

[0052]

[0053] Where D f The degree of failure;

[0054] The fault degree D is calculated based on formulas (4) and (5). f (u) Perform kernel density estimation to obtain its probability density curve. As the probability density curve of the degree of failure in the frequency domain;

[0055] Step 5: Combining the results obtained in Steps 2 and 4, the time-domain and frequency-domain fault probability density curves are fused using the same position averaging method to obtain the final fault probability density curve, and the fault degree is identified based on this curve.

[0056] The probability density curves obtained in the time domain and frequency domain are normalized respectively, so that the sum of the probability densities of each curve after processing is 1, as shown in formula (12):

[0057]

[0058] In the formula, This represents the normalized probability density of the degree of failure. The probability density of the degree of failure;

[0059] Based on formula (12), the probability density curves based on kernel density estimation and sliding Fourier transform methods are respectively analyzed. and Calculations were performed to obtain the probability density curves after normalization using the two methods. and

[0060] Two probability density curves are fused using the method of averaging at the same location. and As shown in formulas (13) and (14):

[0061]

[0062] In the formula, D represents the fault degree, which is the abscissa of the fused fault degree probability density curve. The fused probability density curve of the fault severity reflects the probability of occurrence of different fault severity levels. The fault severity corresponding to the peak position is used as the fault severity identification result.

[0063] To verify the effectiveness of the underwater robot thruster fault severity identification method based on probability density distribution of this invention, the following comparative experiment was designed:

[0064] In a simulated underwater robot operating under ocean current interference, where the left thruster experiences a 5% thrust loss failure after 40 seconds, the typical method of weighted grey relational analysis is used to identify the degree of thruster failure. This method is then compared and verified with the failure degree identification method based on probability density distribution designed in this invention.

[0065] In the simulation experiment verification process, the same original data were used in the typical method and the method of the present invention, and the verification carrier was an underwater robot.

[0066] like Figure 2 As shown, the fault severity identification result obtained by this invention is a probability density curve, with the fault severity corresponding to the peak position being 4.84% and the relative error being 3.20%. The fault severity identification result obtained using the weighted grey relational method is 4.43%, with a relative error of 11.40%. Compared with the weighted grey relational method, this invention improves the relative error by 8.2%. This verifies that this invention has a significant improvement in fault severity identification.

[0067] In summary, this invention studies a method for identifying the degree of fault in underwater robot thrusters. It enables the identification of the degree of fault in underwater robot thrusters under ocean current conditions and when the fault is relatively weak. Compared with the weighted grey relational method, it has significantly improved the accuracy of fault degree identification, which is of great significance for underwater robot fault diagnosis.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying the degree of failure in an underwater robot thruster, characterized in that: Includes the following steps: Step 1: Based on the known fault severity data, the thrust deviation curve is obtained according to the underwater robot dynamics model based on the bow angle and the lateral thruster control voltage. The difference method is used to obtain multiple time-domain thrust losses based on the thrust deviation curve. Based on the thrust loss, the kernel density estimation method is used to obtain the thrust loss probability density curve, and the time-domain thrust loss benchmark is obtained. Step 2: For the fault severity data to be identified, the thrust deviation curve is obtained based on the bow angle and lateral thruster control voltage according to the underwater robot dynamics model. The difference method is used to obtain multiple time-domain thrust losses based on the thrust deviation curve. The kernel density estimation method is used to obtain the thrust loss probability density curve based on the thrust loss. Finally, the time-domain fault severity probability density curve is obtained based on the known fault severity thrust loss benchmark in the time domain. Step 3: Based on the known fault severity data, the multi-time-window sliding Fourier transform method is used to obtain multiple frequency domain thrust losses based on the thrust deviation curve, thus obtaining the frequency domain thrust loss benchmark. Step 4: For the fault severity data to be identified, the difference method is used to obtain multiple frequency domain thrust losses based on the thrust deviation curve, and the frequency domain fault severity probability density curve is obtained based on the thrust loss benchmark in the frequency domain. Step 5: Combining the results obtained in Steps 2 and 4, the time-domain and frequency-domain fault probability density curves are fused using the same position averaging method to obtain the final fault probability density curve, and the fault degree is identified based on this curve.

2. The method for identifying the degree of failure of an underwater robot thruster according to claim 1, characterized in that: Step 1 specifically involves: Based on the bow angle and lateral thruster control voltage, the thrust deviation curve, consisting of the difference between theoretical and actual thrust, is obtained. According to the time of the fault occurrence, data before and after that time are extracted to obtain normal data and fault data, as shown in formulas (1) and (2): T n (i)=T(b-150+i) (1) T f (i)=T(b-150+i) (2) In the formula, T is the thrust deviation, T n This is normal data, T f This is fault data, where b is the number of cycles corresponding to the time the fault occurred; The difference between each cycle of normal data and each cycle of fault data is calculated to obtain multiple thrust losses. The calculation method is shown in formula (3): T d =T f (p)-T n (q) (3) In the formula, T d It is thrust loss; The probability density curves of thrust loss are obtained using the kernel density estimation method, as shown in equations (4) and (5): In the formula, It is the estimated density function, x i These are univariate independent and identically distributed samples drawn from a distribution with unknown density f. In this paper, they are the inference difference T. d K is the kernel function, and this paper uses the Gaussian kernel function. h is the bandwidth of the smoothing function, and σ is the standard deviation of x. Based on the obtained probability density estimation curve The horizontal axis of the curve represents thrust loss, and the vertical axis represents the probability of that thrust loss. The thrust loss corresponding to the peak position of the curve is calculated and denoted as T. dm The calculation method is shown in formula (6): Based on formulas (1-6), the thrust loss corresponding to the peak positions of the probability density curves for 20% and 10% of the fault data is calculated, and denoted as T. dmk20 and T dmk10 This serves as a benchmark for thrust loss at different levels of failure.

3. The method for identifying the degree of failure of an underwater robot thruster according to claim 2, characterized in that: Step 2, according to formula (1-5), yields the thrust loss probability density curve. The magnitude of thrust loss T corresponding to the peak position of the curve is obtained according to formula (6). dmi ; The thrust loss probability density curve of the data to be identified The method to convert the thrust loss probability density curve into a probability percentage is to convert the horizontal axis of the thrust loss probability density curve into a probability percentage; the thrust loss T data to be identified is then converted into a probability percentage. dmi Thrust loss benchmark T with known fault severity dk20 and T dk10 Compare when 0 ≤ T dmi ≤T dmk10 When the fault severity is determined to be within 10%, the probability density curve is... The horizontal axis is converted to a similar fault degree using formula (7), when T dmk10 ≤T dmi ≤T dmk20 When the fault severity is determined to be between 10% and 20%, formula (8) is used for calculation, and the fault severity probability density curve based on the kernel density estimation method is obtained using the above method. In the formula, D k T represents the percentage of failure severity, and is used as the abscissa of the failure severity probability density curve. dk ′ represents the x-coordinate of a point on the thrust loss probability density curve of the data to be identified, indicating the magnitude of the thrust loss.

4. The method for identifying the degree of failure of an underwater robot thruster according to claim 1, characterized in that: Step 3 specifically involves: The lowest frequency amplitude curve is obtained by performing a sliding Fourier transform on the thrust deviation curve, as shown in formula (9): In the formula, f is the lowest frequency amplitude, and M is the number of signals within the time window; According to formula (9), multiple time windows are used to obtain multiple minimum frequency amplitude curves; Based on multiple lowest frequency amplitude curves, the thrust loss benchmarks for different frequencies are calculated as shown in formula (10): In the formula, T df As a reference for thrust loss, f max The peak value of the lowest frequency amplitude curve is represented by u, where u is the frequency. Based on the data with known fault severity, multiple thrust losses corresponding to fault severity levels of 20% and 10% are calculated using the steps described above, serving as the baseline T for thrust losses at different fault severity levels. df20 (u) and T df10 (u); For fault-free data, the thrust loss baseline T is calculated by taking the peak size of the lowest frequency amplitude curve. df0 (u).

5. The method for identifying the degree of failure of an underwater robot thruster according to claim 4, characterized in that: Step 4, based on the thrust deviation curve of the data to be identified, calculates multiple thrust losses T according to formulas (9) and (10). df ′(u); Based on thrust loss benchmark T df20 (u), T df10 (u) and T df0 (u), thrust loss T for the fault data to be identified df ′(u), the fault degree corresponding to different frequencies is obtained, as shown in formula (11): In the formula, D f The degree of failure; The fault degree D is calculated based on formulas (4) and (5). f (u) Perform kernel density estimation to obtain its probability density curve. As a probability density curve of the degree of failure in the frequency domain.

6. The method for identifying the degree of failure of an underwater robot thruster according to claim 1, characterized in that: Step 5 normalizes the probability density curves obtained in the time domain and frequency domain respectively, so that the sum of the probability densities of each curve after processing is 1, as shown in formula (12): In the formula, This represents the normalized probability density of the degree of failure. The probability density of the degree of failure; Based on formula (12), the probability density curves based on kernel density estimation and sliding Fourier transform methods are respectively analyzed. and Calculations were performed to obtain the probability density curves after normalization using the two methods. and Two probability density curves are fused using the method of averaging at the same location. and As shown in formulas (13) and (14): In the formula, D represents the fault degree, which is the abscissa of the fused fault degree probability density curve. The fused probability density curve of the fault severity reflects the probability of occurrence of different fault severity levels. The fault severity corresponding to the peak position is used as the fault severity identification result.

7. A computer device / equipment / system, comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 6.

8. A computer program product comprising a computer program / instructions, characterized in that: When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1 to 6.

Images