CN116644281A - Yacht hull deviation detection method - Google Patents
Yacht hull deviation detection method Download PDFInfo
- Publication number
- CN116644281A CN116644281A CN202310926558.1A CN202310926558A CN116644281A CN 116644281 A CN116644281 A CN 116644281A CN 202310926558 A CN202310926558 A CN 202310926558A CN 116644281 A CN116644281 A CN 116644281A
- Authority
- CN
- China
- Prior art keywords
- signal
- component
- degree
- reference signal
- signals
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 19
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 21
- 238000006073 displacement reaction Methods 0.000 claims description 15
- 238000000034 method Methods 0.000 abstract description 10
- 238000012545 processing Methods 0.000 abstract description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 9
- 238000012880 independent component analysis Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 7
- 239000011159 matrix material Substances 0.000 description 6
- 238000012937 correction Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 239000006185 dispersion Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 238000000926 separation method Methods 0.000 description 2
- 230000035939 shock Effects 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 238000007476 Maximum Likelihood Methods 0.000 description 1
- 206010034719 Personality change Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000000354 decomposition reaction Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
- B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING
- B63B39/00—Equipment to decrease pitch, roll, or like unwanted vessel movements; Apparatus for indicating vessel attitude
- B63B39/14—Equipment to decrease pitch, roll, or like unwanted vessel movements; Apparatus for indicating vessel attitude for indicating inclination or duration of roll
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/10—Pre-processing; Data cleansing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2131—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on a transform domain processing, e.g. wavelet transform
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2134—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on separation criteria, e.g. independent component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
- G06F2218/04—Denoising
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Mechanical Engineering (AREA)
- Ocean & Marine Engineering (AREA)
- Combustion & Propulsion (AREA)
- Navigation (AREA)
Abstract
The application relates to the field of data processing, and provides a yacht hull deviation detection method, which comprises the following steps: decomposing the acquired ship body attitude signals by using an EMD algorithm to obtain component signals corresponding to the ship body attitude signals, wherein each component signal represents a vibration mode of the signal in different frequency ranges; determining a reference signal from the component signals; determining an estimated signal source of each component signal according to the reference signal, and denoising the ship body attitude signal by using the estimated signal source so as to obtain attitude data to be detected; and detecting the deviation condition of the yacht hull by using the attitude data to be detected. The method can solve the problem that the ICA algorithm can not distinguish noise signals with similar frequencies, so that the obtained denoising signals are more accurate.
Description
Technical Field
The application relates to the field of data processing, in particular to a yacht hull deviation detection method.
Background
Yacht hull misalignment refers to the phenomenon of a ship rolling or tilting relative to the horizontal when the yacht is sailing or berthing. Hull misalignment can have an impact on the stability, safety and comfort of the yacht, and therefore requires monitoring and control. Hull offset may be caused by a number of factors: load distribution: for example, the distribution of cargo, equipment and passengers on a yacht may cause an imbalance of the hull, which in turn causes an offset phenomenon; external environment: changes in external environmental conditions such as wind, tides and sea waves may also cause misalignment of the yacht hull; manipulation operation: such as a crew maneuver or a failure of the steering engine system may cause an unintended displacement of the hull.
In order to detect and control the deflection of the yacht hull, the attitude angle of the hull is monitored in real time by installing a tilt sensor so as to detect whether the hull deflects. However, since the hull floats on the water surface, when attitude data of the hull is acquired by the inclination sensor, the acquired attitude data is noisy due to fluctuation of the water surface, and also noisy due to mechanical shock. Therefore, denoising is required to be carried out on the acquired attitude data, and then the deflection condition of the ship body is analyzed according to the denoised attitude data.
In the prior art, a plurality of algorithms for denoising data are available, wherein an independent component analysis algorithm (ICA) has a good effect on denoising the data, and the algorithm utilizes the statistical characteristics of signals, particularly non-Gaussian characteristics, to separate mutually independent source signals, so that noise and signals can be separated, and a denoising effect is realized. However, when the yacht ship attitude data is denoised, due to the fluctuation of the water surface and the like, corresponding low-frequency signals are generated, so that aliasing of the low-frequency signals is caused, and when the ICA algorithm is used for denoising, an accurate estimated mixing matrix cannot be obtained, and therefore an independent noise signal source cannot be estimated, and the denoising effect on the signals is poor.
Disclosure of Invention
The application provides a yacht hull deviation detection method, which can solve the problem that ICA algorithm can not distinguish noise signals with similar frequencies, so that the obtained denoising signals are more accurate.
In a first aspect, the present application provides a yacht hull displacement detection method, comprising:
decomposing the acquired ship body attitude signals by using an EMD algorithm to obtain component signals corresponding to the ship body attitude signals, wherein each component signal represents a vibration mode of the signal in different frequency ranges;
determining a reference signal from the component signals;
determining an estimated signal source of each component signal according to the reference signal, and denoising the ship body attitude signal by using the estimated signal source so as to obtain attitude data to be detected;
and detecting the deviation condition of the yacht hull by using the attitude data to be detected.
In an embodiment, determining a reference signal from the component signals comprises:
determining a possible period of each component signal based on an average time width between maximum points of each component signal;
if the difference value of the possible periods of two adjacent component signals is smaller than a preset value, taking the component signals as candidate component signals;
and calculating the difference between the possible period of each candidate component signal and the average possible period of all the component signals, and taking the candidate component signal corresponding to the difference as the reference signal.
In an embodiment, determining an estimated signal source for each component signal from the reference signal comprises:
calculating the influence degree of each component signal on the reference signal;
an estimated signal source for each component signal is determined based on the extent to which the component signal affects the reference signal.
In an embodiment, calculating the degree of influence of each of the component signals on the reference signal includes:
calculating a difference in possible periods between each of the component signals and the reference signal;
calculating the difference in amplitude between each of the component signals and the reference signal;
the degree of influence of each of the component signals on the reference signal is calculated based on the difference in possible period between each of the component signals and the reference signal, the difference in amplitude, and the variance of each of the component signals.
In an embodiment, determining an estimated signal source for each component signal based on a degree of influence of the component signal on a reference signal comprises:
constructing a Gaussian function based on the influence degree of each component signal on the reference signal;
calculating the offset degree of the influence degree of each component signal on the reference signal based on the difference between the actual value of each influence degree and the fitting value of the Gaussian function and the maximum amplitude of the Gaussian function;
an estimated signal source of the component signal is determined based on a degree of offset of the degree of influence of the component signal on the reference signal.
In an embodiment, determining an estimated signal source for each component signal based on a degree of offset of the degree of influence of the component signal on the reference signal comprises:
and calculating the amplitude of the jth data point in the estimated signal source corresponding to the component signal based on the offset degree of the influence degree of the component signal on the reference signal, the influence degree of the component signal on the reference signal and the amplitude of the jth data point in the reference signal, so as to obtain the estimated signal source of the component signal.
In one embodiment, the magnitude of the jth data point in the estimated signal source corresponding to the component signal is calculated by:
wherein ,indicate->The estimated signal source corresponding to the strip component signal is +.>Amplitude of data points, +.>Represent the firstThe degree of deviation of the degree of influence of the strip component signal on the reference signal,/->Representing the%>The magnitude of the data points is such that,indicate->The degree of influence of the strip component signal on the reference signal.
In one embodiment, constructing a gaussian function based on the degree of influence of each of the component signals on the reference signal comprises:
calculating the mean value and standard deviation of the influence degree of each component signal on the reference signal;
constructing a Gaussian function based on the influence degree of each component signal on the reference signal and the mean value and standard deviation of the influence degree of each component signal on the reference signal;
wherein the gaussian function is:
wherein ,indicate->The degree of influence of the strip component signal on the reference signal, < >>Indicate->The mean value of the degree of influence of the strip component signal on said reference signal,/->Indicate->Standard deviation of the extent of influence of the bar component signal on the reference signal,indicate->The degree of influence of the strip component signal on the reference signal +.>Values in a gaussian function.
In one embodiment, detecting yacht hull misalignment using attitude data to be detected comprises:
calculating the average amplitude of attitude data to be detected, thereby obtaining the hull deviation degree;
if the degree of deflection is greater than 0, the hull is deflected to the right;
if the degree of deflection is less than 0, it means that the hull deflects to the left.
The yacht hull deflection detection method has the beneficial effects that the yacht hull deflection detection method is different from the prior art, and comprises the following steps: decomposing the acquired ship body attitude signals by using an EMD algorithm to obtain component signals corresponding to the ship body attitude signals, wherein each component signal represents a vibration mode of the signal in different frequency ranges; determining a reference signal from the component signals; determining an estimated signal source of each component signal according to the reference signal, and denoising the ship body attitude signal by using the estimated signal source so as to obtain attitude data to be detected; and detecting the deviation condition of the yacht hull by using the attitude data to be detected. The method can solve the problem that the ICA algorithm can not distinguish noise signals with similar frequencies, so that the obtained denoising signals are more accurate.
Drawings
FIG. 1 is a flow chart of an embodiment of a yacht hull displacement detection method according to the present application;
fig. 2 is a flowchart illustrating an embodiment of determining the estimated signal source of each component signal in step S13 in fig. 1.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Yacht hull misalignment refers to the phenomenon of a ship rolling or tilting relative to the horizontal when the yacht is sailing or berthing. Hull misalignment can have an impact on the stability, safety and comfort of the yacht, and therefore requires monitoring and control. In order to detect and control the deflection of the yacht hull, the attitude angle of the hull is monitored in real time by installing a tilt sensor so as to detect whether the hull deflects. However, since the hull floats on the water surface, when attitude data of the hull is acquired by the inclination sensor, the acquired attitude data is noisy due to fluctuation of the water surface, and also noisy due to mechanical shock. Therefore, denoising is required to be carried out on the acquired attitude data, and then the deflection condition of the ship body is analyzed according to the denoised attitude data. The present application will be described in detail with reference to the accompanying drawings and examples.
Referring to fig. 1, fig. 1 is a flow chart of an embodiment of a yacht hull displacement detection method according to the present application, which specifically includes:
step S11: and decomposing the acquired ship body attitude signals by using an EMD algorithm to obtain component signals corresponding to the ship body attitude signals, wherein each component signal represents a vibration mode of the signal in different frequency ranges.
The application mainly aims to denoise the attitude data of the yacht hull, so that the hull attitude data of the yacht needs to be obtained first. When the attitude data of the yacht is collected, the attitude change of the yacht is obtained by installing the accelerometer on the yacht body, the accelerometer is installed at a position far away from an engine cabin of the yacht body, and the influence of large noise in the collected and obtained data caused by high-frequency vibration of the engine is avoided. Because little data is missing due to equipment when the data is acquired, interpolation processing is performed on the acquired data, and a linear interpolation method is used here, so that a ship attitude signal is finally acquired.
Because the attitude signals of the ship are affected by various factors including self-inclination of the ship, shaking of the ship, and vibration of the ship. The data generated by these factors are superimposed in the hull attitude signal, i.e., the noise signal generated. The inclination and the shaking of the ship body generate low-frequency signals, and the shaking of the ship body is high-frequency signals, so that noise signals are mainly eliminated according to the change of the signals when different frequency signals are identified, and the wanted signals are reserved. If the hull is on an absolute stationary water surface, the acquired hull attitude signal is a low frequency signal. The reason for the inclination and shaking of the ship body is that the ship body shakes due to the fluctuation of the water surface, so that the accelerometer generates low-frequency signals with different frequencies when collecting data. When a plurality of low-frequency signals are aliased, the signals with different frequencies need to be distinguished, and noise signals with low frequencies and high frequencies are eliminated.
In analyzing the signals by the independent component analysis algorithm, a mixing matrix is estimated by a maximum likelihood estimation or gradient descent method, etc., and the matrix is used to represent the linear relationship between each observed signal and the source signal. However, because the application contains many low-frequency signals and has great influence on analysis of the ship attitude signals, the obtained estimated mixing matrix cannot accurately describe the relationship between the observed signals and the source signals. In order to describe the relationship between ship attitude signals, the ship attitude signals are decomposed, then the proper reference signals are determined according to the characteristics of the component signals, and then the reference signals are verified to judge whether the reference signals accord with the change characteristics of the ship attitude signals, so that the ship attitude signals are subjected to denoising processing.
When the change characteristics of each component are obtained, the application firstly decomposes the ship body attitude signal through an EMD algorithm, when the EMD algorithm decomposes the ship body attitude signal, the application calculates extreme points of the ship body attitude signal, then carries out curve fitting to obtain upper and lower envelope curves, then averages the extreme curves to obtain an average envelope curve, subtracts the average envelope curve from the ship body attitude signal to obtain an intermediate signal, and carries out state judgment to obtain the IMF component signal. And then repeating the above operation to obtain each component signal. Each component signal represents a vibration mode of the signal over a different frequency range, with higher frequency vibration modes corresponding to shorter scale changes and lower frequency vibration modes corresponding to longer scale changes.
Step S12: a reference signal is determined from the component signals.
When the ship body is hit by wind waves, the ship body is in a back and forth shaking posture, so that when the ship body posture signals are collected through the accelerometer, the regular shaking is recorded, the influence on the ship body posture signals is large, after the ship body posture signals are decomposed through the EMD algorithm, a signal which changes approximately periodically exists in the component signals, the signal is not a wanted target signal, but has a large influence on the determination of the reference signals, and therefore the reference signals need to be determined according to the change of the frequency of each signal.
In an embodiment, the possible period of each component signal is determined based on the average time width between the maxima points of each component signal. Specifically, the calculation mode of the possible period of the component signal is as follows:
wherein ,indicate->Possible periods of the strip component signal, +.>Indicate->The average time width between the maxima points of the strip component signal represents +.>The period of the bar component signal. Wherein (1)>Representing the number of maxima, +.>Represents the time width between every two adjacent maxima, < ->Indicate->The first->The abscissa (i.e., the instant point) corresponding to the maximum point,/and->Indicate->The first->The abscissa (i.e., the instant point) corresponding to each minimum point. Since none of the component signals obtained by the decomposition is a periodic signal, the period calculated here is also for describing the degree of variation of each of the component signals in the time domain.
Since the higher frequency signal is distributed in the original component signal among the component signals decomposed by the EMD algorithm, in order to avoid a high frequency signal from interfering with a low frequency signal to a large extent, it is prescribed herein that when calculating the reference signal, if the period difference between two adjacent component signals is smaller than 0.2 (empirical value), the calculation is participated, and if it is larger than this value, the calculation is not performed. I.e. if the difference in the possible periods of two adjacent component signals is smaller than a preset value, said component signals are taken as candidate component signals. Specifically, the preset value is 0.2. And calculating the difference between the possible period of each candidate component signal and the average possible period of all the component signals, and taking the candidate component signal corresponding to the difference as the reference signal. Specifically, the difference between the average possible period of each candidate component signal and all the component signals is calculated as follows:
wherein ,indicate->Possible period of the strip candidate component signal, +.>Indicate->Possible periods of the strip component signal, +.>Representing the average possible period of all component signals, < +.>Indicate->The difference between the possible period of the bar candidate component signal and the average possible period of all component signals. Will->The corresponding candidate component signal with the smallest value is used as the reference signal.
Step S13: and determining an estimated signal source of each component signal according to the reference signal, and denoising the ship body attitude signal by using the estimated signal source so as to obtain attitude data to be detected.
Referring to fig. 2, determining the estimated signal sources of each component signal in step S13 specifically includes:
step S21: and calculating the influence degree of each component signal on the reference signal.
The reference signal obtained from the above calculation is then subjected to non-gaussian verification, which refers to a statistical analysis of the signal or data to determine whether it meets a gaussian distribution. Because the application collects and obtains the ship attitude signals and is influenced by high-frequency noise and low-frequency noise, the influence degree of different noises on the ship attitude signals is different, the noise signals are in accordance with Gaussian distribution, and Gaussian property verification is carried out on the reference signals, so as to accurately describe whether the reference signals conform to the change characteristics of the original signals, and the application can be more accurate when estimating the mixing matrix according to the reference signals.
It is therefore necessary to determine whether the reference signal satisfies the gaussian characteristic according to the difference between the component signals and the reference signal, that is, the influence degree of the component signals on the reference signal, and if the influence degree of each component signal on the reference signal satisfies the gaussian distribution, it is described that the reference signal is a signal description without noise.
Specifically, the difference in possible periods between each of the component signals and the reference signal is calculated, e.g. the firstDifference in possible period between the bar component signal and the reference signal +.>. Calculating the difference in amplitude between each of said component signals and said reference signal, e.g. calculating +.>Difference in amplitude between the bar component signal and the reference signal +.>. The degree of influence of each of the component signals on the reference signal is calculated based on the difference in possible period between each of the component signals and the reference signal, the difference in amplitude, and the variance of each of the component signals. Specifically, the degree of influence of each component signal on the reference signal is calculated by:
in the formula ,indicate->The degree of influence of the strip component signal on the reference signal, < >>Indicate->Possible periods of the strip component signal, +.>Represents the possible period of the reference signal, +.>Indicate->The first->Amplitude of data points, +.>Representing the%>Amplitude of data points, +.>Representing the number of data points in the component signal, +.>Indicate->Variance of the bar component signal. />Indicate->The two component signals with smaller period difference show that the frequency of the two component signals is similar when changing, in the application, the influence on the ship attitude signal is larger, namely the influence degree of noise signals generated by the shaking of the water surface is the largest, so that the influence degree of two signals with similar periods on the original signal is larger. />Indicate->The larger the difference between the amplitude of the strip component signal and the amplitude of the reference signal, the larger the difference of the amplitude is, which means that the degree of change of the amplitude of the two groups of signals is larger, so that after the signals are overlapped, the higher-frequency signals are added to the original signals, and the influence degree of the signals on the reference signal is larger. The multiplication by the variance indicates the fluctuation degree of the data, the fluctuation degree indicates the stability of the signal, the higher the fluctuation degree is, the lower the stability of the signal is, the higher the random variation degree of the signal of the frequency is when the signal is acquired, and therefore, the larger degree of influence on the original signal is necessarily generated when the signal is superimposed on the original signal.
Step S22: an estimated signal source for each component signal is determined based on the extent to which the component signal affects the reference signal.
Specifically, a gaussian function is constructed based on the degree of influence of each of the component signals on the reference signal. In one embodiment, the influence degree of each component signal on the reference signal obtained by the calculation is calculated according to the influence degree of each component signal on the reference signal, and then the reference signal is non-Gaussian verified according to the change characteristics of the reference signalAnd standard deviation->I.e. calculating the mean and standard deviation of the degree of influence of each of the component signals on the reference signal. A gaussian function is constructed based on the degree of influence of each of the component signals on the reference signal, and the mean and standard deviation of the degree of influence of each of the component signals on the reference signal. Wherein the gaussian function is:
wherein ,indicate->The degree of influence of the strip component signal on the reference signal, < >>Indicate->The mean value of the degree of influence of the strip component signal on said reference signal,/->Indicate->Standard deviation of the extent of influence of the bar component signal on the reference signal,indicate->The degree of influence of the strip component signal on the reference signal +.>Values in a gaussian function.
In the application, the offset degree and the peak value of the Gaussian function represent the influence degree of each component signal on the reference signal, so that when the influence degree of each component signal and the reference signal obeys Gaussian distribution, the reference signal is an accurate reference signal.
The degree of deviation of the degree of influence of each component signal on the reference signal is calculated based on the difference between the actual value of each degree of influence and the fitted value of the gaussian function, and the maximum amplitude of the gaussian function. In one embodiment, the degree of offset of the influence degree of each component signal on the reference signal is calculated by:
in the formula ,indicate->The degree of deviation of the degree of influence of the strip component signal on the reference signal,/->Indicate->The degree of influence of the strip component signal on the reference signal, < >>Representing the value in the corresponding gaussian function at the level of influence,representing the maximum magnitude point of the gaussian function. />Indicated as +.>The smaller the difference between the actual value and the fit value of the gaussian function, the smaller the degree of offset. Multiplied by->Because the magnitude of the peak in the gaussian function depends on the standard deviation, which in turn represents the dispersion of the data, the smaller the peak, the smaller the dispersion of the data, and the more accurate the description of the degree of influence of each component signal on the reference signal.
An estimated signal source of the component signal is determined based on a degree of offset of the degree of influence of the component signal on the reference signal.
The degree of offset obtained from the above calculation is then used to determine the estimated signal source from the reference signal, since the larger the degree of offset, the larger the difference between the signal source and the reference signal is, and therefore the larger the degree of change in data at the time of estimating the signal source by the reference signal, and therefore correction by the degree of offset is required. Specifically, the amplitude of the jth data point in the estimated signal source corresponding to the component signal is obtained by calculating based on the offset degree of the influence degree of the component signal on the reference signal, the influence degree of the component signal on the reference signal and the amplitude of the jth data point in the reference signal, so that the estimated signal source of the component signal is obtained. In one embodiment, the magnitude of the jth data point in the estimated signal source corresponding to the component signal is calculated by:
in the formula ,indicate->The estimated signal source corresponding to the strip component signal is +.>Amplitude of data points, +.>Represent the firstDegree of offset of the degree of influence of the individual component signals on the reference signal,/->Representing the%>The magnitude of the data points is such that,indicate->The degree of influence of the strip component signal on the reference signal. Since each component signal has a positive and negative offset compared to the gaussian function, when negative, the component signal is said to have a substantially smaller effect on the reference signal than the gaussian fit value, and thus +.>When->When the number is negative, the correction coefficient obtained according to the offset degree is smaller than 1, the estimated signal source obtained according to the reference signal is smaller than the actual component signal change degree, when +.>When the correction coefficient is positive, the correction coefficient obtained according to the offset degree is larger than 1, and the estimated signal source obtained according to the reference signal is larger than the actual component signal change degree.
The method is characterized in that an estimated signal source of each component signal is obtained through the method, the estimated signal source represents the change of noise signals existing in the information acquisition process, and then the acquired ship attitude signals are subjected to denoising processing according to the estimated signal source. The blind source separation algorithm is used for carrying out noise separation on the acquired ship attitude signals by using the estimated signal source, so that original independent ship attitude signals are obtained and recorded as attitude data to be detected.
Step S14: and detecting the deviation condition of the yacht hull by using the attitude data to be detected.
Specifically, calculating the average amplitude of attitude data to be detected, thereby obtaining the hull deviation degree; if the degree of deflection is greater than 0, the hull is deflected to the right; if the degree of deflection is less than 0, it means that the hull deflects to the left.
In a specific embodiment, after the denoised attitude data to be detected is obtained according to the above steps, the degree of deviation of the hull of the yacht is obtained according to the attitude data to be detected, the deviation of the hull is mainly represented by the degree of change of the attitude data to be detected, so that 0 is set as reference data, and then the average difference between the data and the reference data is calculated to obtain the degree of deviation of the hull, and the calculation formula is as follows:
in the formula ,indicating the degree of deflection of the hull->Representing +.>Amplitude of data points, +.>Representing the number of data points in the pose data to be detected. Because the deflection of the hull will be to the left or to the right, the calculated +.>Exists positive and negative, when->When it indicates a leftward shift, when +.>And, at that time, a rightward shift is indicated.
According to the method, when the attitude data of the yacht hull are acquired through the sensor, the acquired attitude data are noisy due to the influence of various factors, so that the method performs denoising processing on the attitude data through an ICA algorithm. However, in the application, when the attitude data of the ship body is acquired, the acquired data can contain the data because the ship body shakes due to the fluctuation of the water surface, and the data has great influence on the attitude data of the ship body, so that the noise is accurately removed by the traditional denoising algorithm. Therefore, the application obtains the component signals by decomposing the ship body attitude signals, then obtains a reference signal according to the change of each component signal, describes the influence degree of each component signal on the ship body attitude signals through the reference signal, further carries out Gaussian fitting according to the influence degree, obtains the deviation degree of the influence degree of each component signal compared with a Gaussian function, and then estimates an independent signal source influencing the ship body attitude signals according to the deviation degree and the influence degree, thereby realizing denoising of the ship body attitude signals. The method can solve the problem that the ICA algorithm cannot distinguish noise signals with similar frequencies when estimating the independent signal sources by estimating the mixing matrix, so that the obtained denoising signals are more accurate.
The foregoing is only the embodiments of the present application, and therefore, the patent scope of the application is not limited thereto, and all equivalent structures or equivalent processes using the descriptions of the present application and the accompanying drawings, or direct or indirect application in other related technical fields, are included in the scope of the application.
Claims (9)
1. A yacht hull displacement detection method, comprising:
decomposing the acquired ship body attitude signals by using an EMD algorithm to obtain component signals corresponding to the ship body attitude signals, wherein each component signal represents a vibration mode of the signal in different frequency ranges;
determining a reference signal from the component signals;
determining an estimated signal source of each component signal according to the reference signal, and denoising the ship body attitude signal by using the estimated signal source so as to obtain attitude data to be detected;
and detecting the deviation condition of the yacht hull by using the attitude data to be detected.
2. A yacht hull displacement detection method as in claim 1, wherein determining a reference signal from the component signals comprises:
determining a possible period of each component signal based on an average time width between maximum points of each component signal;
if the difference value of the possible periods of two adjacent component signals is smaller than a preset value, taking the component signals as candidate component signals;
and calculating the difference between the possible period of each candidate component signal and the average possible period of all the component signals, and taking the candidate component signal corresponding to the difference as the reference signal.
3. A yacht hull displacement detection method as claimed in claim 2, wherein determining the estimated signal source of each component signal from the reference signal comprises:
calculating the influence degree of each component signal on the reference signal;
an estimated signal source for each component signal is determined based on the extent to which the component signal affects the reference signal.
4. A yacht hull displacement detection method according to claim 3, in which calculating the extent of influence of each of the component signals on the reference signal comprises:
calculating a difference in possible periods between each of the component signals and the reference signal;
calculating the difference in amplitude between each of the component signals and the reference signal;
the degree of influence of each of the component signals on the reference signal is calculated based on the difference in possible period between each of the component signals and the reference signal, the difference in amplitude, and the variance of each of the component signals.
5. A yacht hull displacement detection method as claimed in claim 3, wherein determining the estimated signal source for each component signal based on the extent to which the component signal affects the reference signal comprises:
constructing a Gaussian function based on the influence degree of each component signal on the reference signal;
calculating the offset degree of the influence degree of each component signal on the reference signal based on the difference between the actual value of each influence degree and the fitting value of the Gaussian function and the maximum amplitude of the Gaussian function;
an estimated signal source of the component signal is determined based on a degree of offset of the degree of influence of the component signal on the reference signal.
6. The yacht hull displacement detection method as claimed in claim 5, wherein determining the estimated signal source of each component signal based on the degree of displacement of the degree of influence of the component signal on the reference signal comprises:
and calculating the amplitude of the jth data point in the estimated signal source corresponding to the component signal based on the offset degree of the influence degree of the component signal on the reference signal, the influence degree of the component signal on the reference signal and the amplitude of the jth data point in the reference signal, so as to obtain the estimated signal source of the component signal.
7. The yacht hull displacement detection method as claimed in claim 6, wherein the amplitude of the jth data point in the estimated signal source corresponding to the component signal is calculated by:
wherein ,indicate->The estimated signal source corresponding to the strip component signal is +.>Amplitude of data points, +.>Indicate->The degree of deviation of the degree of influence of the strip component signal on the reference signal,/->Representing the%>Amplitude of data points, +.>Indicate->The degree of influence of the strip component signal on the reference signal.
8. The yacht hull displacement detection method as in claim 5, wherein constructing a gaussian function based on the degree of influence of each of the component signals on the reference signal comprises:
calculating the mean value and standard deviation of the influence degree of each component signal on the reference signal;
constructing a Gaussian function based on the influence degree of each component signal on the reference signal and the mean value and standard deviation of the influence degree of each component signal on the reference signal;
wherein the gaussian function is:
wherein ,indicate->The degree of influence of the strip component signal on the reference signal, < >>Indicate->The mean value of the degree of influence of the strip component signal on said reference signal,/->Indicate->Standard deviation of the extent of influence of the strip component signal on the reference signal, +>Indicate->Strip component signalDegree of influence on the reference signal->Values in a gaussian function.
9. The yacht hull displacement detection method as in claim 1, wherein detecting yacht hull displacement using attitude data to be detected comprises:
calculating the average amplitude of attitude data to be detected, thereby obtaining the hull deviation degree;
if the degree of deflection is greater than 0, the hull is deflected to the right;
if the degree of deflection is less than 0, it means that the hull deflects to the left.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310926558.1A CN116644281B (en) | 2023-07-27 | 2023-07-27 | Yacht hull deviation detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310926558.1A CN116644281B (en) | 2023-07-27 | 2023-07-27 | Yacht hull deviation detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116644281A true CN116644281A (en) | 2023-08-25 |
| CN116644281B CN116644281B (en) | 2023-10-24 |
Family
ID=87625151
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310926558.1A Active CN116644281B (en) | 2023-07-27 | 2023-07-27 | Yacht hull deviation detection method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116644281B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116902177A (en) * | 2023-09-14 | 2023-10-20 | 山东航宇游艇发展有限公司 | Yacht abnormal state intelligent monitoring method and system based on Internet of things |
| CN117195007A (en) * | 2023-11-08 | 2023-12-08 | 张家港长寿工业设备制造有限公司 | Heat exchanger performance prediction method and system |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1154617A (en) * | 1995-09-25 | 1997-07-16 | 索尼公司 | Data receiving apparatus, demodulator circuit and integrated circuit |
| JP2004093455A (en) * | 2002-09-02 | 2004-03-25 | Natl Space Development Agency Of Japan | Method and program for removing false signal |
| US20060222235A1 (en) * | 2005-03-29 | 2006-10-05 | Kenshi Kanegae | Defect inspection method |
| EP1830349A1 (en) * | 2006-03-01 | 2007-09-05 | Parrot | Method of noise reduction of an audio signal |
| US20140214364A1 (en) * | 2013-01-31 | 2014-07-31 | Yamaha Corporation | Method of estimating offset of magnetic sensor |
| WO2016112714A1 (en) * | 2015-01-15 | 2016-07-21 | 江苏南大五维电子科技有限公司 | Assistant docking method and system for vessel |
| US20160225216A1 (en) * | 2013-12-12 | 2016-08-04 | Grg Banking Equipment Co., Ltd. | Method and device for banknote identification based on thickness signal identification |
| CN108470018A (en) * | 2018-02-22 | 2018-08-31 | 中国铁道科学研究院 | Smoothing method and device based on the intrinsic mode functions that empirical mode decomposition decomposes |
| CN108564046A (en) * | 2018-04-19 | 2018-09-21 | 南京大学 | Based on the steel construction dynamic strain signal processing method for improving EEMD |
| JP2020087615A (en) * | 2018-11-21 | 2020-06-04 | 株式会社東具 | Signal light device |
| CN111880209A (en) * | 2020-07-21 | 2020-11-03 | 山东省科学院海洋仪器仪表研究所 | Ship body attitude calculation method and application |
| WO2021012315A1 (en) * | 2019-07-24 | 2021-01-28 | 清华大学 | Method and device for identifying time series abnormal pattern based on fuzzy matching |
| WO2021068939A1 (en) * | 2019-10-12 | 2021-04-15 | 中科新松有限公司 | Multi-component signal decomposition-based vibration recognizing method for joint of mechanical arm |
| CN115795272A (en) * | 2022-10-18 | 2023-03-14 | 电子科技大学长三角研究院(湖州) | Spectral line denoising method based on fractional order iterative discrete wavelet transform |
| CN115994481A (en) * | 2022-12-06 | 2023-04-21 | 西安电子科技大学 | Ship motion attitude prediction method based on multiple combinations |
| CN116150592A (en) * | 2023-04-20 | 2023-05-23 | 江阴华士医疗器械有限公司 | Wheelchair intelligent monitoring and early warning method and system based on artificial intelligence |
| CN116203646A (en) * | 2023-05-04 | 2023-06-02 | 山东省地质测绘院 | Exploration data processing system for determining geological resource quantity |
| CN116340728A (en) * | 2023-04-07 | 2023-06-27 | 平顶山天安煤业股份有限公司 | Method for processing inclination measurement signal of transmission tower and computer storage medium |
| CN116388798A (en) * | 2023-03-17 | 2023-07-04 | 西安电子科技大学 | Link16 frequency hopping signal denoising reconnaissance recognition algorithm |
| CN116465291A (en) * | 2023-04-19 | 2023-07-21 | 江苏科技大学 | Deep learning-based wireless power transmission system offset detection method and system |
-
2023
- 2023-07-27 CN CN202310926558.1A patent/CN116644281B/en active Active
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1154617A (en) * | 1995-09-25 | 1997-07-16 | 索尼公司 | Data receiving apparatus, demodulator circuit and integrated circuit |
| US5933461A (en) * | 1995-09-25 | 1999-08-03 | Sony Corporation | Data receiving apparatus, demodulator circuit and integrated circuit |
| JP2004093455A (en) * | 2002-09-02 | 2004-03-25 | Natl Space Development Agency Of Japan | Method and program for removing false signal |
| US20060222235A1 (en) * | 2005-03-29 | 2006-10-05 | Kenshi Kanegae | Defect inspection method |
| EP1830349A1 (en) * | 2006-03-01 | 2007-09-05 | Parrot | Method of noise reduction of an audio signal |
| US20140214364A1 (en) * | 2013-01-31 | 2014-07-31 | Yamaha Corporation | Method of estimating offset of magnetic sensor |
| US20160225216A1 (en) * | 2013-12-12 | 2016-08-04 | Grg Banking Equipment Co., Ltd. | Method and device for banknote identification based on thickness signal identification |
| WO2016112714A1 (en) * | 2015-01-15 | 2016-07-21 | 江苏南大五维电子科技有限公司 | Assistant docking method and system for vessel |
| CN108470018A (en) * | 2018-02-22 | 2018-08-31 | 中国铁道科学研究院 | Smoothing method and device based on the intrinsic mode functions that empirical mode decomposition decomposes |
| CN108564046A (en) * | 2018-04-19 | 2018-09-21 | 南京大学 | Based on the steel construction dynamic strain signal processing method for improving EEMD |
| JP2020087615A (en) * | 2018-11-21 | 2020-06-04 | 株式会社東具 | Signal light device |
| WO2021012315A1 (en) * | 2019-07-24 | 2021-01-28 | 清华大学 | Method and device for identifying time series abnormal pattern based on fuzzy matching |
| WO2021068939A1 (en) * | 2019-10-12 | 2021-04-15 | 中科新松有限公司 | Multi-component signal decomposition-based vibration recognizing method for joint of mechanical arm |
| CN111880209A (en) * | 2020-07-21 | 2020-11-03 | 山东省科学院海洋仪器仪表研究所 | Ship body attitude calculation method and application |
| CN115795272A (en) * | 2022-10-18 | 2023-03-14 | 电子科技大学长三角研究院(湖州) | Spectral line denoising method based on fractional order iterative discrete wavelet transform |
| CN115994481A (en) * | 2022-12-06 | 2023-04-21 | 西安电子科技大学 | Ship motion attitude prediction method based on multiple combinations |
| CN116388798A (en) * | 2023-03-17 | 2023-07-04 | 西安电子科技大学 | Link16 frequency hopping signal denoising reconnaissance recognition algorithm |
| CN116340728A (en) * | 2023-04-07 | 2023-06-27 | 平顶山天安煤业股份有限公司 | Method for processing inclination measurement signal of transmission tower and computer storage medium |
| CN116465291A (en) * | 2023-04-19 | 2023-07-21 | 江苏科技大学 | Deep learning-based wireless power transmission system offset detection method and system |
| CN116150592A (en) * | 2023-04-20 | 2023-05-23 | 江阴华士医疗器械有限公司 | Wheelchair intelligent monitoring and early warning method and system based on artificial intelligence |
| CN116203646A (en) * | 2023-05-04 | 2023-06-02 | 山东省地质测绘院 | Exploration data processing system for determining geological resource quantity |
Non-Patent Citations (2)
| Title |
|---|
| 彭章刚;周步祥;李世新;王精卫;周海忠;唐浩;: "基于ICA-EMD和Prony算法的区域电网低频振荡模式分析", 电测与仪表, no. 23 * |
| 江娜;: "基于EMD的数字调相信号去噪算法研究", 自动化与仪器仪表, no. 04 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116902177A (en) * | 2023-09-14 | 2023-10-20 | 山东航宇游艇发展有限公司 | Yacht abnormal state intelligent monitoring method and system based on Internet of things |
| CN116902177B (en) * | 2023-09-14 | 2023-12-08 | 山东航宇游艇发展有限公司 | Yacht abnormal state intelligent monitoring method and system based on Internet of things |
| CN117195007A (en) * | 2023-11-08 | 2023-12-08 | 张家港长寿工业设备制造有限公司 | Heat exchanger performance prediction method and system |
| CN117195007B (en) * | 2023-11-08 | 2024-02-06 | 张家港长寿工业设备制造有限公司 | Heat exchanger performance prediction method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116644281B (en) | 2023-10-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116644281B (en) | Yacht hull deviation detection method | |
| CN109975839B (en) | Joint filtering optimization method for vehicle satellite positioning data | |
| CN109143224B (en) | Multi-target association method and device | |
| US8898013B2 (en) | Navigation device and process integrating several hybrid inertial navigation systems | |
| CN111025273B (en) | Distortion drag array line spectrum feature enhancement method and system | |
| EP2912407A1 (en) | System and method for water column aided navigation | |
| CN109917151B (en) | Dynamic weight distribution-based ship relative wind fusion algorithm | |
| CN110555398B (en) | Fault diagnosis method for determining first arrival moment of fault based on optimal filtering smoothness | |
| US20220269289A1 (en) | Vessel anti-rolling control apparatus and anti-rolling control method | |
| CN112432644A (en) | Unmanned ship integrated navigation method based on robust adaptive unscented Kalman filtering | |
| CN111999747B (en) | Robust fault detection method for inertial navigation-satellite integrated navigation system | |
| EP2929366B1 (en) | Improvements in and relating to course and/or speed data | |
| CN103901421A (en) | Underwater sound array SMI-MVDR spatial spectrum estimation method based on diagonal load shedding | |
| CN112585498A (en) | Object detection device | |
| JPH10206523A (en) | Target detector | |
| CN113167856A (en) | Interference suppression method and signal restoration method | |
| CN113702937B (en) | Dual-channel incoherent detection method based on self-adaptive MTI filter | |
| US20150168545A1 (en) | Distance estimation device and method using the difference of wave speed between waves | |
| CN114298094A (en) | Line spectrum automatic extraction method based on principal component analysis | |
| JP7366582B2 (en) | ISAR radar device and ISAR radar signal processing method | |
| Marghany et al. | Fractal dimension algorithm for detecting oil spills using RADARSAT-1 SAR | |
| CN114459414B (en) | Depth detection method for semi-submersible vehicle | |
| CN111948638A (en) | Target length estimation method based on high-resolution range profile and application thereof | |
| JPWO2015178410A1 (en) | Horizontal metacenter height estimation apparatus and horizontal metacenter height estimation method | |
| CN110412536B (en) | Target detection method based on variable-scale median ratio |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |