CN112763225B - Sensor error identification and elimination method based on Laplace feature mapping algorithm - Google Patents
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Abstract
A sensor error identification and elimination method based on a Laplace feature mapping algorithm relates to the field of engine testing, and continuous and accurate measurement of engine temperature is completed by adopting an annular symmetrical temperature measurement structure, laplace feature mapping and a wavelet-based denoising algorithm. The method comprises the following steps: 1. in order to deal with the air flow disturbance generated by deflection of the tail flame of the engine, a measuring device with an annular symmetrical structure of the combustion chamber of the engine is arranged. 2. In order to effectively weaken and remove the measurement interference in time, the target temperature value is obtained through dimension reduction processing of a Laplace eigen mapping algorithm. 3. And finally, performing sensor temperature signal denoising processing through wavelet transform filtering, and selecting a wavelet basis Coif5 to perform temperature signal denoising processing to eliminate measurement errors caused by complex temperature change rules of the tail flame of the engine. The invention can effectively solve the problems of poor temperature measurement accuracy caused by complex temperature change rules and difficulty in timely and effective removal of measurement interference.
Description
Technical Field
The invention relates to a sensor error identification and elimination method, in particular to a Laplace feature mapping algorithm.
Background
The development of the engine generally needs ground test verification, and finally flight test assessment is carried out, the types of test beds are various, wherein the wind tunnel test is a test mode only second to the flight test, the working state of the engine is simulated more truly, but the air consumption is large, the consumption is high, the dependence on energy is large, and the wind tunnel test is not suitable for a large number of tests. The test bed can provide all conditions required by the actual operation of the engine, can adjust parameters such as air inlet, air exhaust, fuel flow, pressure, temperature and the like of the engine to be the same as data in the flight process, collects the internal temperature, pressure change, thrust, vibration, rotating speed and other state changes of the engine under different working conditions, and examines whether the engine meets design indexes or not by analyzing the data of engine accessories such as output voltage and current of a generator, so that the performance of the engine test bed and the advancement of the ground test technology of the engine are prerequisites for researching a high-quality engine, and the precision of the test bed is derived from the precision of a measurement and control system.
The measurement and control system of the test bed of the small aircraft and the missile engine has the annular characteristic, the height of the measurement ring is coaxial with the engine, and the measurement ring is arranged far in front of the engine in the air inlet direction and used for measuring the total air inlet pressure and the total air inlet temperature of the engine. In order to accurately obtain the total temperature value of the current position, a plurality of branch thermal resistance temperature sensors are used for measuring, finally, an average value is taken to represent the current total temperature value, and an annular distribution measurement mode is adopted.
In engineering, an engine tail nozzle can be generally used as a cavity, the area of the cavity is the same as that of a tail nozzle opening, the cavity can change along with the change of the state of an engine, tail flames are axisymmetric, the area of the cavity is the same as that of the tail nozzle opening, the tail flames can be divided into a core area and a mixing area according to the change situation of the temperature of the tail flames, the temperature of the core area is basically constant, the temperature is high, the core area is the main part of infrared radiation, and the temperature of the mixing area is low.
The emissivity of the tail flame of the engine is closely and complexly related to the material, the surface state, the wavelength and the temperature of the tail flame, the plume of the tail flame of the engine is a complex plasma field of two-phase flow of a gas phase and a condensed phase, the emissivity of the plume can change along with the change of decomposition products, the temperature and the like of a combustion state over time, and the accurate numerical value of the emissivity is difficult to obtain, which is the characteristic of the plume field and is also a difficult point in the temperature measurement technology.
In the existing thermal infrared imager for measuring the temperature of the plume of the tail flame of the engine, the emissivity of any position of the plume field in the working period of the engine at any time is generally regarded as the same constant, the influence of the time-domain change of the emissivity on the accuracy of a measurement result cannot be eliminated, the emissivity of the plume field of the engine is difficult to obtain in real time, the temperature of the plume field is changed quickly, the tail flame at the outlet of a combustion chamber may deflect, the accuracy of the temperature measurement result is poor, and the thermal infrared imager has the defects that the temperature measurement cannot be avoided and cannot be overcome in the application process of the existing thermal infrared imager.
The Laplacian eigenmap algorithm (LE) is a method for constructing the relationship between data from the perspective of local approximation, and LE is a dimension reduction algorithm based on a graph, which constructs data to be dimension reduced into the graph, and each node of the graph and the node closest to the node establish an edge relationship, and then it expects the points connected in the graph (the points close to each other in the original space) to be as close as possible in the space after dimension reduction, so that the original local structural relationship can be maintained after dimension reduction.
Disclosure of Invention
The invention aims to overcome the defects that the temperature measurement accuracy is poor and the measurement interference is not timely and effectively weakened and removed due to the complex temperature change rule in the prior art. In order to solve the problem of poor temperature measurement accuracy caused by complex temperature change rules, an annular temperature measurement structure is designed to timely and effectively weaken and remove measurement interference, wavelet transform filtering is adopted to perform noise reduction processing on a sensor temperature signal, and a sensor temperature measurement error based on a Laplace characteristic mapping algorithm is eliminated based on the problems, so that the accuracy of a measurement and control system of an engine test bed is accurately improved.
The purpose of the invention is realized by the following technical scheme: the method comprises the steps of firstly arranging an engine combustion chamber outlet temperature field measuring device to be of an annular symmetrical structure, measuring by using a plurality of thermal resistance temperature sensors for multiple times, taking an average value, carrying out multiple processes together, and obtaining a field of data points. Then, a weight matrix is created to be a thermal kernel function, then characteristic decomposition is carried out, then the dimension reduction process is expanded from one dimension to multiple dimensions to obtain a target function of an LE algorithm, and then filtering processing is carried out on the low-frequency oscillation signals of the sensor data by using a new noise reduction processing method of wavelet transform filtering, so that an accurate result of the temperature measured by the instantaneous temperature sensor is obtained, and the accuracy of a measurement and control system of the engine test bed is improved.
The flow chart of the implementation of the invention is shown in figure 1, and is divided into three steps, and the specific steps are as follows:
the method comprises the following steps: the temperature measuring device adopts an annular symmetrical temperature measuring structure which is uniformly distributed, and the problem that the temperature measuring accuracy is not high due to the complex temperature change rule can be effectively solved.
The invention arranges that a measuring device of the temperature field of the outlet of the engine combustion chamber is of an annular symmetrical structure, the height of a measuring ring is coaxial with the engine, and the measuring ring is arranged far ahead in the air inlet direction of the engine and is used for measuring the total air inlet pressure and the total air inlet temperature of the engine; in order to accurately obtain the total temperature value of the current position, a plurality of branch thermal resistance temperature sensors are used for measuring, and finally, the average value is obtained through multiple times of measurement; the temperature measuring device adopts an annular distribution measuring mode, a plurality of sensors are dispersedly arranged on the same cross section in the mode, measuring margin is increased, measuring points are uniformly distributed, and results are more accurate.
The invention considers the factor of airflow disturbance at the assembly position of the sensor, and for the sensor, on the premise of ensuring the consistency of the temperature sensing element (such as the packaging position of a thermal resistor, the packing density, the position of a thermal resistor wire winding and the like), the symmetry of the assembly position of the temperature sensing element relative to a tail flame port needs to be improved, if the tail flame at the outlet of a combustion chamber deflects, the distribution of the airflow inside the sensor is biased, so that the airflow flowing through the temperature sensing element of the sensor is different, the two paths of response time of the sensor are inconsistent, and the two paths of temperature sensing elements of the sensor need to be symmetrical left and right relative to the tail flame port. Under the dynamic condition, the temperature in the stagnation cavity of the sensor has gradient, if the positions of the thermal resistors in the two paths of temperature sensing elements in the axial direction are different, the temperatures sensed by the thermal resistors in the temperature change process are also different, so that the response time is inconsistent, and therefore the two paths of temperature sensing elements of the sensor, particularly the internal thermal resistors, need to be vertically symmetrical relative to the tail flame opening.
Step two: and processing by a Laplace characteristic mapping algorithm to obtain a target temperature value.
First a raw data set X = [ X ] is created for the measured temperature 1 ,x 2 ,...,x N ]∈R D×N Wherein D is a data dimension, N is a sample number, the number of adjacent domains and the dimension reduction dimension are set to be D respectively, and t belongs to R D×N (ii) a Finding out the neighborhood, creating neighborhood graph G, and searching each sample X by k-nearest neighbor method i The nearest k samples
The connecting line of the two adjacent points is an edge in the neighborhood graph G; g calculation comprises two steps of neighbor selection and weight calculation; firstly, measuring by using a plurality of thermal resistance temperature sensors to obtain a neighborhood of a data point, then defining weights for all edges, and constructing a weight matrix of high-dimensional spatial data as follows:
then, a weight matrix is established to be a thermal kernel function, and then characteristic decomposition is carried out:
Lz＝λDz
wherein, the Laplace matrix L = D-W, D is a degree matrix, D = diag { D } 1 ,d 2 ,d 3 ,...,d N }，d i Is a proximity matrix
For the case of one-dimensional reduction, the objective function of the LE algorithm is:
wherein, the (A) T Is a transposition of a matrix, W ij Has a value of x i And x j The greater the weight value between, x is illustrated i And x j The more similar, if W ij Is very large, according to the constraint of the above formula, y i -y j Approaching 0, i.e. and the high-dimensional data point x i And x j Corresponding low dimensional data y i And y j The method is also very similar, so that the local adjacency relation of the data before and after dimensionality reduction is maintained, and P is a constant vector of m dimensionality; taking the value of P as 1, through simplification and optimization of an objective function, the solution of the above formula is converted into the solution of the generalized eigenvalue of the following formula. Where λ is the eigenvalue, the dimensionality reduction result y * That is, the feature vector corresponding to the minimum non-0 feature value, namely:
Ly＝λDy
the dimension reduction process is expanded from one dimension to multiple dimensions, and the objective function of the LE algorithm is expressed as follows:
y * ＝argmintr(YLY T )
wherein tr () is the matrix tracing operation, the dimensionality reduction data y at this time * Is the eigenvector corresponding to m minimum non-0 eigenvalues, and the value of m is [1,5 ]]Finally obtaining the final temperature value
In degrees celsius.
Step three: because the temperature change rule of the tail flame of the engine is complex, the temperature measurement data inevitably has errors, finally, the noise reduction processing of the temperature signal of the sensor is carried out through wavelet transform filtering, and the noise reduction effect is the best when the wavelet basis is Coif5 through the experimental analysis of mainly comparing the wavelet basis Coif1, coif2, coif3, coif4, coif5 and other typical wavelet basis, so that the wavelet basis selects Coif5 to carry out filtering processing on the low-frequency signal of the temperature measurement data, the distortion problem of the traditional Fourier transform signal is greatly solved, the generated attenuation curve is smooth, the noise is effectively removed, and the characteristics of the original signal are kept.
The basic idea of wavelet threshold denoising is to set a critical threshold, compare the wavelet coefficient with the critical threshold, if less than the threshold, the coefficient is mainly caused by noise, and eliminate the coefficient; if the coefficient is larger than the critical threshold value, the coefficient is mainly caused by signals, the part of the coefficient is left, and then the wavelet inverse transformation is carried out on the processed wavelet coefficient to obtain the de-noised signals.
The wavelet denoising method comprises the following steps: wavelet transform is carried out on the signal f (t) with noise to obtain a group of wavelet decomposition coefficients W j,k By decomposing the wavelet coefficients W j,k Performing threshold processing to obtain estimated wavelet coefficient
Make it
As small as possible, using estimated wavelet coefficients
And performing wavelet reconstruction to obtain an estimated signal f (t), namely the denoised signal.
Aiming at the difficulty of fast and accurate extraction of wavelet coefficients in wavelet denoising, a very simple method is provided for wavelet coefficients W j,k And (6) estimating. After f (t) is continuously decomposed for several times, there is a space distribution non-uniform signal C (k) with wavelet coefficient W on each scale j,k With larger values at certain specific locations, which correspond to distorted locations and important information of the original signal C (k), and W at most other locations j,k Smaller, for white noise, its corresponding wavelet coefficient W j,k The distribution is uniform at each scale and W increases with the scale j,k The magnitude of the coefficient decreases. Therefore, the general denoising method is to find a suitable number as a threshold value and use a wavelet function W below the critical threshold value j,k Set to zero and for wavelet functions W above j,k Then it is retained or shrunk to obtain the estimated smallnessWave coefficient
It is understood to be essentially caused by the signal C (k), then
The original signal can be reconstructed by performing the reconstruction.
Discrete time series X of noisy temperature data processed by LE algorithm n (t) continuous wavelet transform:
wherein, the theta number represents complex conjugate, s is resolution scale, t is time, n is time translation amount, delta t is time transformation amount, psi is mother wavelet psi 0 And (4) calculating a non-dimensionalized result by the step to finally obtain required temperature signal data and forming a smooth data curve.
Compared with the prior art, the invention has the following advantages:
the invention adopts a symmetrical temperature measurement structure with annular uniform distribution for the temperature measurement mode, and the mode dispersedly arranges a plurality of sensors on the same cross section, increases the measurement margin and simultaneously achieves uniform distribution of the measurement points, so that the result is more accurate, the problem of low temperature measurement accuracy caused by complex temperature change rules can be effectively solved, and the temperature data of the tail flame of the engine can be accurately measured.
Meanwhile, a Laplace characteristic mapping algorithm is adopted, a weight matrix is created to be a thermal kernel function, characteristic decomposition is carried out, the dimension reduction process is expanded from one dimension to multiple dimensions, a target function of an LE algorithm is obtained, measurement interference is timely and effectively weakened and removed, the method can process large-scale and noisy multi-channel temperature signals, therefore, the method has obvious advantages in the aspect of processing the multi-channel temperature signals, the method is superior to other dimension reduction methods in the aspects of accuracy and measurement interference resistance, and the method considers that the Laplace characteristic mapping has low calculation complexity, so the method has obvious advantages in the aspect of running time consumption.
Finally, according to temperature signal data, the wavelet-based Coif5 is selected to perform noise reduction processing on the temperature signal of the sensor to eliminate the problems that the temperature change rule of the tail flame of the engine is complex and the temperature measurement data has inevitable errors.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a "0-0" cross-section total temperature and pressure measurement loop.
Fig. 3 is a flow chart of a laplacian feature mapping algorithm implementation.
Fig. 4 is a graph of temperature data after wavelet transform.
Detailed Description
The following description of the embodiments of the present invention is made with reference to the accompanying drawings and examples:
the test bed uses platinum as a temperature sensing element of the sensor, the figure 2 is an axial view of a measuring ring, the precision of the test bed can reach A-level tolerance and the like (0.15 +/-0.003 t ℃), the temperature coverage range of the platinum resistance sensor is-200-400 ℃, the size of a shell of the sensor is designed according to the size of a pipeline, the pressure of a measuring point and the flow rate of the measuring point, the platinum resistance is placed in the shell, and the error caused by a lead wire can be reduced by using a three-wire lead-out wire.
Executing the step one: taking a full-temperature and full-pressure measuring ring with a section of 0-0 of an engine as an example, fig. 2 is an axial view of the measuring ring, the height of the measuring ring is coaxial with the engine, and the measuring ring is arranged far in front of the air inlet direction of the engine and used for measuring the total air inlet pressure and the total air inlet temperature of the engine.
In the examples, 6 measurements of the thermal resistance temperature sensor were used, and the final measurement was averaged 3 times for a total of 6 runs. In fig. 2, ttnij1 to Ttnij6 adopt an annular distribution measurement mode, and a plurality of sensors are dispersedly arranged on the same cross section in the annular distribution measurement mode, so that measurement margins are increased, measurement points are uniformly distributed, and results are more accurate.
Executing the step two: first a raw data set X = [ X ] is created for the measured temperature 1 ,x 2 ,...,x N ]∈R D×N Wherein D is data dimension, N is sample number, the number of adjacent domains and the dimension reduction dimension are set to be D respectively, and t belongs to R D×N Finding out the neighborhood, creating a neighborhood graph G, and searching each sample X by a k-nearest neighbor method i The most recent k samples
The line connecting two adjacent points is the edge in the neighborhood graph G. The calculation of G comprises two steps of neighbor selection and weight calculation, namely, firstly, measuring by using a plurality of thermal resistance temperature sensors to obtain a neighborhood of data points, then defining weights for all edges, constructing a weight matrix of high-dimensional spatial data, then creating the weight matrix to become a thermonuclear function, and then performing characteristic decomposition, wherein the Laplace matrix L = D-W, D is a degree matrix, and W is an adjacent matrix. For the case of one-dimensional reduction, then the objective function is found to be y * . Taking the value of P as 1, through simplification and optimization of an objective function, the solution of the above formula is converted into the solution of the generalized eigenvalue of the following formula. In the following formula, λ is a characteristic value, and the dimensionality reduction result y is a characteristic vector corresponding to the minimum non-0 characteristic value, that is, ly = λ Dy expands the dimensionality reduction process from one dimension to multiple dimensions to obtain a new objective function
Where P is a constant vector of dimension m, the dimensionality reduced data y * The m minimum eigenvectors corresponding to the non-0 eigenvalues are represented by a Laplace eigen mapping algorithm flowchart as shown in FIG. 3, and the final temperature value is obtained by taking m =3
And step three is executed: according to the invention, coif5 is selected as a wavelet basis to filter the low-frequency signal of the temperature measurement data, so that the problem of distortion of the traditional Fourier transform signal is greatly solved, the generated attenuation curve is smooth, the noise is effectively removed, the characteristics of the original signal are maintained, and the process is adopted to filter the low-frequency oscillation signal of the sensor data to obtain the effective signal.
The measured temperatures are obtained five times in total, and after the laplace feature mapping processing, the temperature data are shown in table 1:
TABLE 1 measured temperature data processing Table
| Number of measurements | Measurement of temperature/. Degree.C |
| For the first time | 486.8 |
| For the second time | 459.7 |
| The third time | 519.3 |
| Fourth time | 501.9 |
| Fifth time | 479.5 |
Then selecting wavelet basis Coif5 filtering for carrying out sensor temperature signal denoising processing through wavelet transformation, obtaining a smoother and more accurate temperature attenuation signal curve diagram shown in 4 (b) compared with an original temperature signal curve diagram 4 (a), obtaining an accurate result of the temperature measured by the instantaneous temperature sensor, selecting the Coif5 wavelet basis, and ensuring the precision of the signal in the wavelet threshold denoising process; according to the specific characteristics of temperature data, the sparse characteristics of signals in wavelet domains under different wavelet bases are considered, the wavelet base with the best sparsity for input signals is selected, the denoising of wavelet threshold values is facilitated, the whole method is simple, convenient and quick, the rapid and accurate denoising of temperature signals is realized, the signal noise effect and the signal processing efficiency in the application of the temperature measuring device are improved, and the accuracy of a measurement and control system of an engine test bed is further improved.
Claims (3)
1. A sensor error identification and elimination method based on a Laplace feature mapping algorithm is characterized by comprising the following steps:
the method comprises the following steps: the measuring device for the temperature field of the outlet of the engine combustion chamber is arranged to be of an annular symmetrical structure, the height of the measuring ring is coaxial with the engine, and the measuring ring is arranged far ahead in the air inlet direction of the engine and used for measuring the total air inlet pressure and the total air inlet temperature of the engine; in order to accurately obtain the total temperature value of the current position, a plurality of branch thermal resistance temperature sensors are used for measuring, and finally, the average value is obtained through multiple times of measurement; the temperature measuring device adopts an annular distribution measuring mode, and a plurality of sensors are dispersedly arranged on the same cross section in the mode, so that the measuring margin is increased, the measuring points are uniformly distributed, and the result is more accurate;
step two: creating a raw data set of measured temperatures, X = [ X ], according to step one 1 ,x 2 ,...,x N ]∈R D×N Wherein D is a data dimension, N is a sample number, the number of adjacent domains and the dimension reduction dimension are set to be D respectively, and t belongs to R D×N (ii) a Finding out the neighborhood, creating neighborhood graph G, and searching each sample X by k-nearest neighbor method i The nearest k samples
The connecting line of two adjacent points is the edge in the neighborhood graph G(ii) a The calculation of G comprises two steps of neighbor selection and weight calculation; firstly, measuring by using a plurality of thermal resistance temperature sensors to obtain a neighborhood of a data point, then defining weights for all edges, constructing a weight matrix of high-dimensional space data, then establishing the weight matrix to be a thermal kernel function, then performing characteristic decomposition, expanding the dimension reduction process from one dimension to multiple dimensions to obtain a target function of an LE algorithm, and further obtaining a target temperature value y after dimension reduction * ；
Step three: and finally, performing noise reduction on the temperature signal of the sensor through wavelet transform filtering, and selecting a wavelet basis with the best noise reduction effect through experimental analysis of emphasis contrast wavelet bases Coif1, coif2, coif3, coif4, coif5 and other typical wavelet bases, so that the Coif5 is selected as the wavelet basis to perform filtering processing on the low-frequency signal of the temperature measurement data.
2. The method for identifying and eliminating sensor errors based on the laplacian eigenmap algorithm as claimed in claim 1, wherein the second step is:
first a raw data set X = [ X ] is created for the measured temperature 1 ,x 2 ,...,x N ]∈R D×N Wherein D is data dimension, N is sample number, the number of adjacent domains and the dimension reduction dimension are set to be D respectively, and t belongs to R D×N (ii) a Searching neighborhood, creating neighborhood graph G, and searching each sample X by k-nearest neighbor method i The nearest k samples
The connection line of the two adjacent points is the edge in the neighborhood graph G; the calculation of G comprises two steps of neighbor selection and weight calculation; firstly, measuring by using a plurality of thermal resistance temperature sensors to obtain a neighborhood of a data point, then defining weights for all edges, and constructing a weight matrix of high-dimensional spatial data as follows:
then, a weight matrix is established to be a thermal kernel function, and then characteristic decomposition is carried out:
Lz＝λDz
wherein, the Laplace matrix L = D-W, D is a degree matrix, D = diag { D } 1 ,d 2 ,d 3 ,...,d N }，d i Is a proximity matrix
For the case of one-dimensional reduction, the objective function of the LE algorithm is:
wherein, the (A) T Is a transpose of a matrix, W ij Has a value of x i And x j The greater the weight between, x is shown i And x j The more similar, if W ij Is very large, according to the constraint of the above formula, y i -y j Approaching 0, i.e. and high-dimensional data point x i And x j Corresponding low dimensional data y i And y j The method is also very similar, so that the local adjacency relation of the data before and after dimension reduction is kept, and P is a constant vector of m dimension; taking the value of P as 1, through simplifying and optimizing an objective function, the solution of the above formula is converted into the solution of the generalized eigenvalue of the following formula, wherein, lambda is the eigenvalue, and the dimensionality reduction result y is * The feature vector corresponding to the minimum non-0 feature value is as follows:
Ly＝λDy
the dimension reduction process is expanded from one dimension to multiple dimensions, and the objective function of the LE algorithm is expressed as follows:
y * ＝argmintr(YLY T )
wherein tr () is the matrix tracing operation, the dimensionality reduction data y at this time * Is the eigenvector corresponding to m minimum non-0 eigenvalues, and the value of m is [1,5 ]]Finally obtaining the final temperature value
In degrees Celsius。
3. The method for identifying and eliminating the sensor error based on the laplace feature mapping algorithm according to claim 1, wherein the third step is:
because the temperature change rule of the tail flame of the engine is complex, the temperature measurement data inevitably has errors, the noise reduction processing of the temperature signal of the sensor is carried out through wavelet transform filtering, and the noise reduction effect is the best when the wavelet basis is Coif5 through the experimental analysis of the key comparison wavelet basis Coif1, coif2, coif3, coif4, coif5 and other typical wavelet basis, therefore, the invention selects Coif5 to carry out filtering processing on the low-frequency signal of the temperature measurement data, thereby greatly overcoming the distortion problem of the traditional Fourier transform signal, producing a smooth attenuation curve, effectively removing noise and keeping the characteristics of the original signal;
the basic idea of wavelet threshold denoising is to set a critical threshold, compare the wavelet coefficient with the critical threshold, if less than, the coefficient is mainly caused by noise, and eliminate the coefficient; if the coefficient is larger than the critical threshold value, the coefficient is mainly caused by signals, the part of the coefficient is left, and then the wavelet inverse transformation is carried out on the processed wavelet coefficient to obtain a denoised signal;
the wavelet denoising method comprises the following steps: wavelet transform is carried out on the signal f (t) with noise to obtain a group of wavelet decomposition coefficients W j,k By decomposing the wavelet coefficients W j,k Performing threshold processing to obtain estimated wavelet coefficient
Make it
Using estimated wavelet coefficients as small as possible
Performing wavelet reconstruction to obtain an estimated signal f (t), namely a denoised signal;
it is difficult to fast to aim at wavelet coefficient in wavelet de-noisingThe speed is accurately obtained, and a very simple method is provided for wavelet coefficient W j,k Estimating; after f (t) is continuously decomposed for several times, there is a space distribution non-uniform signal C (k) with wavelet coefficient W on each scale j,k With larger values at certain specific locations, which correspond to distorted locations and important information of the original signal C (k), and W at most other locations j,k Smaller, for white noise, it corresponds to a wavelet coefficient W j,k The distribution is uniform at each scale and W increases with scale j,k The magnitude of the coefficient decreases; therefore, the general denoising method is to find a suitable number as a threshold value and use a wavelet function W below the critical threshold value j,k Set to zero and for wavelet functions W above j,k Then it is retained or shrunk to obtain the estimated wavelet coefficients
It can be understood as being essentially caused by the signal C (k), then for W j,k Reconstructing to reconstruct the original signal;
discrete time series X of noisy temperature data processed by LE algorithm n (t) continuous wavelet transform:
wherein, the theta number represents complex conjugate, s is resolution scale, t is time, n is time translation amount, delta t is time transformation amount, psi is mother wavelet psi 0 And (4) calculating a non-dimensionalized result by the step to finally obtain required temperature signal data and forming a smooth data curve.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1838109A (en) * | 2006-04-10 | 2006-09-27 | 西安交通大学 | Mode parameter recognition method based on experience mode decomposition and Laplace wavelet |
| CN109033415A (en) * | 2018-08-06 | 2018-12-18 | 浙江大学 | A kind of dimensionality reduction and method for visualizing of the multidimensional nerve signal based on laplacian eigenmaps |
| WO2019153388A1 (en) * | 2018-02-12 | 2019-08-15 | 大连理工大学 | Power spectral entropy random forest-based aeroengine rolling bearing fault diagnosis method |
| CN111982302A (en) * | 2020-08-24 | 2020-11-24 | 广东工业大学 | Temperature measurement method with noise filtering and environment temperature compensation |
-
2020
- 2020-12-25 CN CN202011562623.XA patent/CN112763225B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1838109A (en) * | 2006-04-10 | 2006-09-27 | 西安交通大学 | Mode parameter recognition method based on experience mode decomposition and Laplace wavelet |
| WO2019153388A1 (en) * | 2018-02-12 | 2019-08-15 | 大连理工大学 | Power spectral entropy random forest-based aeroengine rolling bearing fault diagnosis method |
| CN109033415A (en) * | 2018-08-06 | 2018-12-18 | 浙江大学 | A kind of dimensionality reduction and method for visualizing of the multidimensional nerve signal based on laplacian eigenmaps |
| CN111982302A (en) * | 2020-08-24 | 2020-11-24 | 广东工业大学 | Temperature measurement method with noise filtering and environment temperature compensation |
Non-Patent Citations (4)
| Title |
|---|
| 基于2D-KPCA的拉普拉斯特征映射人脸识别;徐梦珂 等;《计算机应用研究》;20170731(第07期);全文 * |
| 基于声发射的柴油机早期故障诊断研究;王欢欢;《CNKI中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20180115(第1期);全文 * |
| 基于拉普拉斯特征映射的滚动轴承故障识别;黄宏臣 等;《振动与冲击》;20150315(第05期);全文 * |
| 基于非线性相关系数核方法的超谱数据分类;张淼;《光学学报》;20090930;第29卷(第9期);全文 * |
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