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CN119598862A - An online harmonic feature extraction method, system and computer-readable storage medium for multi-component periodic signal recognition - Google Patents

An online harmonic feature extraction method, system and computer-readable storage medium for multi-component periodic signal recognition Download PDF

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CN119598862A
CN119598862A CN202411674733.3A CN202411674733A CN119598862A CN 119598862 A CN119598862 A CN 119598862A CN 202411674733 A CN202411674733 A CN 202411674733A CN 119598862 A CN119598862 A CN 119598862A
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periodic signal
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刘清泉
霍鑫
刘康志
褚明慧
弭宝涵
王林瑞
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Harbin Institute of Technology Shenzhen
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Abstract

An online harmonic feature extraction method, an online harmonic feature extraction system and a computer readable storage medium for multi-component periodic signal identification relate to the field of signal analysis and signal processing. The method for extracting the periodic signal harmonic characteristics has the technical problems that the periodic signal harmonic characteristic extraction method in the prior art is low in identification precision, sensitive to noise, complex in decomposition process and poor in real-time performance, and is difficult to meet the signal online identification requirement in a complex industrial control system. The invention realizes the purposes of dynamically identifying the frequency of the periodic signal and identifying and outputting the frequency of the periodic signal in real time by designing a trap with the adjustable center frequency as an extremum function, and realizes the purposes of dynamically identifying the amplitude of the periodic signal and identifying and outputting the amplitude of the periodic signal in real time by designing a band-pass filter with the adjustable center frequency as an extremum function and combining a signal envelope and a phase sensitivity detection composite module. The method is used for effectively extracting the periodic signal output by the motor control system controller to identify the periodic interference.

Description

Online harmonic feature extraction method, system and computer readable storage medium for multi-component periodic signal identification
Technical Field
The invention relates to the field of signal analysis and signal processing, in particular to an online harmonic characteristic extraction method for multi-component periodic signal identification.
Background
In modern signal processing and control systems, real-time identification of periodic signals is a central requirement in many applications. The accurate identification of periodic signals is of great importance in the fields of communication, automatic control, industrial automation, medical signal processing and the like, so as to ensure the stability and the optimal performance of the system. For example, in a communication system, accurate signal identification is helpful to improve the efficiency and stability of data transmission, in automated production, identification of external interference signals can prevent premature equipment damage and improve production efficiency, and in the medical field, analysis of periodic physiological signals can provide effective support for disease diagnosis and treatment.
A significant challenge in identifying periodic signals is that the frequency and amplitude of the signals may change dynamically over time, which makes conventional signal processing methods difficult to handle. Classical fourier transforms (Fourier Transform, FT) are a widely used frequency domain analysis tool that can decompose a time domain signal into several sinusoidal components. However, fourier transforms have the following limitations:
(1) The fourier transform requires global signal data for analysis, and cannot draw a valid conclusion when the signal has not been fully acquired, which results in that it is only suitable for offline processing;
(2) The fourier transform assumes that the signal is stable, so that it can only identify the average frequency in the face of a non-stationary signal or a signal whose frequency varies with time, resulting in a greatly reduced identification effect.
To address the deficiencies of fourier transforms in processing dynamic and non-stationary signals, researchers have proposed Short-time fourier transforms (STFT) and Hilbert-Huang transforms (HHT). Short-time fourier transform captures the local frequency characteristics of the signal by dividing the signal into short time windows and then fourier transforming the signal within each window, however, this method has an unavoidable tradeoff between selection window width and time resolution, which limits its application in high precision real-time signal processing, hilbert-yellow transform decomposes the signal into a series of eigenmode functions (INTRINSIC MODE FUNCTIONS, IMF) by empirical mode decomposition (EMPIRICAL MODE DECOMPOSITION, EMD), and describes the time-varying characteristics of the signal by analyzing the instantaneous frequency, which is excellent in nonlinear and non-stationary signal processing, but is relatively sensitive to noise, and the decomposition process is complex, resulting in poor real-time.
Therefore, it is needed to provide an online harmonic feature extraction method to meet the signal online identification requirement in a complex industrial control system.
Disclosure of Invention
The technical problems to be solved by the invention are as follows:
the periodic signal harmonic characteristic extraction method in the prior art has the advantages of low recognition precision, relatively sensitive noise, complex decomposition process and poor real-time performance, and is difficult to meet the signal online recognition requirement in a complex industrial control system.
The invention provides a technical scheme for solving the technical problems:
in order to solve the technical problems, the invention provides an on-line harmonic feature extraction method for multi-component periodic signal identification, wherein the harmonic feature extraction method runs on a hardware platform of a motor control system in real time, identifies the harmonic frequency and amplitude of the multi-component periodic signal on line, and comprises the following steps:
step 1, designing an extremum function D with adjustable center frequency, which is used for primarily identifying a periodic signal c input to the extremum function D and outputting a real-time frequency identification value (approximate value) of a single component in the periodic signal c;
Step 2, establishing an extremum searching closed-loop control structure based on an extremum function D;
step 3, selecting parameters of each link of the extremum searching closed-loop control structure, and ensuring that the extremum searching closed-loop control structure meets a time scale separation principle so as to ensure the stability of the extremum searching closed-loop control structure;
Step 4, designing an extremum function S of a combined signal envelope and phase sensitivity detection composite module, which is used for primarily identifying a periodic signal c input to the extremum function S and outputting a real-time amplitude identification value (approximate value) of a single component in the periodic signal c;
Step 5, introducing a signal processor, and filtering out extra period components of the extremum searching closed-loop control structure, which are introduced by the excitation signal;
so far, the construction of a control structure for identifying the frequency and the amplitude of a single component of the periodic signal c is completed;
And 6, connecting n control structures designed in the steps 1 to 5 in parallel to form n identification channels, selecting initial values u i0, i=1, 2 of different center frequencies u i in each identification channel, wherein k and k are the number of frequency components, setting different parameters for each identification channel, and realizing real-time harmonic characteristic extraction of multi-component signals.
Further, the method for performing real-time frequency identification on the single component in the periodic signal c in the step 1 specifically includes the following steps:
Step 1.1, designing a band elimination filter with an extremum function D as a central frequency u, wherein when the central frequency u of the extremum function is consistent with the real-time frequency of an input periodic signal c, the output of the extremum function D has a minimum value of 0;
And 1.2, adjusting the central frequency u of the extremum function D in real time to inhibit the periodic signal in a specific frequency range so as to maintain the output of the D to be minimum.
Further, the specific steps of the process of establishing the extremum searching closed-loop control structure based on the extremum function D in the step 2 include:
Step 2.1, taking the extremum function D designed in the step 1 as a function with extremum in an extremum searching algorithm;
Step 2.2, analyzing the dynamic characteristics of the extremum function D, and calculating a phase lag range generated in the signal processing process;
step 2.3, designing phase compensation links K according to the phase lag range calculated in the step 2.2, and connecting eta phase compensation links in series to compensate phase lag, wherein the specific number eta of the phase compensation links K is determined by an actual input signal;
step 2.4, designing an integration link High-pass link with turning frequency omega b Two periodic signals Asin (omega e t) and Bsin (omega e t) with the frequency of omega e are used as excitation signals, and an extremum searching closed-loop control structure is established, wherein A, B is the amplitude of the two periodic signals with the frequency of omega e, and s is a complex variable in the Laplace transformation.
Further, the specific steps of the method for selecting the parameters of each link of the extremum searching closed-loop control structure meeting the time scale separation principle in the step 3 include:
Step 3.1, calculating the maximum response time of the periodic signal c according to the frequency range of the input periodic signal c, and recording the maximum response time as tau c;
Step 3.2, ensuring response time tau b＜τc of the extremum searching closed-loop control structure by selecting reasonable high-pass link turning frequency omega b in step 2.4;
Step 3.3, ensuring response time τ e＜τb of the excitation signal by selecting reasonable frequency ω e of the periodic excitation signal described in step 2.4;
And 3.4, ensuring response time tau d＜τe of the extremum function D by designing the order of the extremum function D reasonably.
Further, the method for identifying the real-time amplitude of the single component in the periodic signal c in step 4 specifically includes the following steps:
Step 4.1, designing a band-pass filter with the extremum function S as a central frequency u, and when the central frequency u of the extremum function S is consistent with the real-time frequency of the input periodic signal c, outputting the extremum function S with a maximum value, wherein the maximum value is consistent with the real-time amplitude of the input periodic signal c;
Step 4.2, constructing a signal c ⊥ orthogonal to the input periodic signal c by using the center frequency u and the input periodic signal c;
Step 4.3, obtaining constant values not including periodic components by using the input periodic signal c and the orthogonal signal c ⊥ thereof according to triangle identity
Step 4.4, designing a link P based on a phase sensitivity detection technology to primarily obtain a real-time amplitude approximation value of the input periodic signal c by coping with noise in the environment and the high-frequency component introduced in step 4.3.
Further, the method for filtering the extra periodic component of the extremum searching closed-loop control structure introduced by the excitation signal in the step 5 specifically includes the steps of:
Step 5.1, designing wave traps H 1 and H 2 with frequency doubling and frequency tripling as central frequencies according to the frequency omega e of the periodic excitation signal in step 2.4;
step 5.2, extracting the central frequency u of the extremum function D in step 1.1, and obtaining the real-time estimated frequency of the input periodic signal c through the serial wave traps H 1 and H 2
Step 5.3, extracting the approximate amplitude value estimated in step 4.4, and obtaining the real-time estimated amplitude value of the input periodic signal c through the serially connected wave traps H 1 and H 2
Further, the number n of the identification channels is greater than or equal to the number k of the frequency components.
Further, the extremum searching closed-loop control structure in the step 2.4 includes:
The method comprises the steps of inputting a periodic signal c, a first excitation signal Asin (omega e t), a second excitation signal Bsin (omega e t), an extremum function D, an extremum function S, a phase detection link P, a phase compensation link K, serially connected wave traps H 1 and H 2, an integrator, a high-pass link with turning frequency omega b, an adder and a multiplier;
The connection relation of the modules is as follows:
the input periodic signal c and the signal u are two input signals of an extremum function S, and the output signal of the extremum function S is used as the input signal of a phase detection link P to form a branch 1;
the input periodic signal c and the signal u are two input signals of an extremum function D, and the output signal of the extremum function D is used as the input signal of a phase compensation link K to form a branch 2;
The branch 1 is connected with the branch 2 in parallel;
the output signal and the signal u of the phase detection link P are input signals of the wave traps H 1 and H 2 connected in series;
The input signal of the high-pass link with turning frequency omega b is the output signal of the phase compensation link K, and the output signal and the second excitation signal Bsin (omega e t) act as the input signal of the integrator through a multiplier;
the output signal of the integrator and a first excitation signal Asin (omega e t) are subjected to adder action to calculate a signal u;
The signal u is an identifiable variable, and is one of the extremum function D, extremum function S and input signals of the serially connected wave traps H 1 and H 2, and is also the center frequency of the band-stop filter in step 1.1 and the band-pass filter in step 4.1;
The serial wave traps H 1 and H 2 output real-time estimated amplitude values And estimating frequency in real time
The invention also provides an online harmonic characteristic extraction system for multi-component periodic signal recognition, which is provided with a program module corresponding to the steps of the method in any one of the technical schemes, and the steps in the online harmonic characteristic extraction method for multi-component periodic signal recognition are executed in running.
The invention also provides a computer readable storage medium storing a computer program configured to implement the steps in the online harmonic feature extraction method for multi-component periodic signal identification of the method of any one of the above technical solutions when invoked by a processor.
Compared with the prior art, the invention has the beneficial technical effects that:
(1) Because the frequency of the periodic signal does not contain extremum information, the invention realizes the dynamic identification of the frequency of the periodic signal and the real-time identification and output of the frequency of the periodic signal by designing a trap with an adjustable center frequency as an extremum function;
(2) Because the amplitude of the periodic signal does not contain extremum information, the invention realizes the dynamic identification of the amplitude of the periodic signal and realizes the purpose of identifying and outputting the amplitude of the periodic signal in real time by designing a band-pass filter with adjustable center frequency as an extremum function and combining a signal envelope and a phase sensitive detection composite module;
(3) The signal processor is introduced to assist in filtering out periodic signals additionally introduced by the excitation signals in the extremum searching closed-loop control structure, so that the purity of the identification result is ensured;
(4) The parallel structure is adopted to process a plurality of signal channels, so that the application field of multi-component signal identification is expanded, and the method is particularly suitable for scenes containing interference signals in various periodic forms in a complex industrial automation system.
The method is applied to a precise servo mechanical system with multiple periodic component interference, and the identification precision is taken as an index, so that the method can realize the real-time identification of the frequency and the amplitude of the multiple periodic component interference in a noise environment.
The method is used for effectively extracting the periodic signal output by the motor control system controller to identify the periodic interference.
Drawings
FIG. 1 is a flowchart of an online harmonic feature extraction method for multi-component periodic signal identification in an embodiment of the invention;
FIG. 2 is a block diagram of an online harmonic feature extraction method for multi-component periodic signal identification in an embodiment of the invention;
FIG. 3 is a graph showing simulation results of a Fourier transform method (prior art) in a simulation comparison experiment according to an embodiment of the present invention;
FIG. 4 is a graph showing simulation results of a Hilbert-Huang transform method (prior art) in a comparative simulation experiment according to an embodiment of the present invention;
FIG. 5 is a graph showing simulation results of a support vector machine method (prior art) in a simulation comparison experiment according to an embodiment of the present invention;
FIG. 6 is a graph of simulation results of the method of the present invention in a comparative simulation experiment of an embodiment of the present invention;
FIG. 7 is a schematic diagram of the method of the present invention for online harmonic feature extraction of a disturbance signal of a motor control system according to an embodiment of the present invention;
FIG. 8 is a graph of the result of multi-component periodic signal feature extraction for on-line harmonic feature extraction of a motor control system disturbance signal according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, exemplary embodiments or examples of the present invention will be described below with reference to the accompanying drawings. It is apparent that the described embodiments or examples are only implementations or examples of a part of the invention, not all. All other embodiments or examples, which may be made by one of ordinary skill in the art without undue burden, are intended to be within the scope of the present invention based on the embodiments or examples herein.
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
Example 1
As shown in fig. 1 and 2, the present invention provides an on-line harmonic feature extraction method for multi-component periodic signal recognition, wherein the harmonic feature extraction method operates on a hardware platform of a motor control system in real time, and recognizes harmonic frequencies and amplitudes of the multi-component periodic signal on line, and the method comprises the following steps:
step 1, designing an extremum function D with adjustable center frequency, which is used for primarily identifying a periodic signal c input to the extremum function D and outputting a real-time frequency identification value (approximate value) of a single component in the periodic signal c;
the method for carrying out real-time frequency identification on the single component in the periodic signal c specifically comprises the following steps:
Step 1.1, designing a band elimination filter with an extremum function D as a central frequency u, wherein when the central frequency u of the extremum function is consistent with the real-time frequency of an input periodic signal c, the output of the extremum function D has a minimum value of 0;
And 1.2, adjusting the central frequency u of the extremum function D in real time to inhibit the periodic signal in a specific frequency range so as to maintain the output of the D to be minimum.
Step 2, establishing an extremum searching closed-loop control structure based on an extremum function D;
the process for establishing the extremum searching closed-loop control structure based on the extremum function D specifically comprises the following steps:
Step 2.1, taking the extremum function D designed in the step 1 as a function with extremum in an extremum searching algorithm;
Step 2.2, analyzing the dynamic characteristics of the extremum function D, and calculating a phase lag range generated in the signal processing process;
step 2.3, designing phase compensation links K according to the phase lag range calculated in the step 2.2, and connecting eta phase compensation links in series to compensate phase lag, wherein the specific number eta of the phase compensation links K is determined by an actual input signal;
step 2.4, designing an integration link High-pass link with turning frequency omega b Using two periodic signals Asin (ω e t) and Bsin (ω e t) with frequencies ω e as excitation signals, an extremum seeking closed loop control structure as shown in fig. 2 is established, wherein A, B is the amplitude of the two periodic signals with frequencies ω e, and s is the complex variable in the laplace transform.
The extremum searching closed-loop control structure in the step 2.4 comprises the following steps:
The method comprises the steps of inputting a periodic signal c, a first excitation signal Asin (omega e t), a second excitation signal Bsin (omega e t), an extremum function D, an extremum function S, a phase detection link P, a phase compensation link K, serially connected wave traps H 1 and H 2, an integrator, a high-pass link with turning frequency omega b, an adder and a multiplier;
The connection relation of the modules is as follows:
the input periodic signal c and the signal u are two input signals of an extremum function S, and the output signal of the extremum function S is used as the input signal of a phase detection link P to form a branch 1;
the input periodic signal c and the signal u are two input signals of an extremum function D, and the output signal of the extremum function D is used as the input signal of a phase compensation link K to form a branch 2;
The branch 1 is connected with the branch 2 in parallel;
the output signal and the signal u of the phase detection link P are input signals of the wave traps H 1 and H 2 connected in series;
The input signal of the high-pass link with turning frequency omega b is the output signal of the phase compensation link K, and the output signal and the second excitation signal Bsin (omega e t) act as the input signal of the integrator through a multiplier;
the output signal of the integrator and a first excitation signal Asin (omega e t) are subjected to adder action to calculate a signal u;
The signal u is an identifiable variable, and is one of the extremum function D, extremum function S and input signals of the serially connected wave traps H 1 and H 2, and is also the center frequency of the band-stop filter in step 1.1 and the band-pass filter in step 4.1;
The serial wave traps H 1 and H 2 output real-time estimated amplitude values And estimating frequency in real time
Step 3, selecting parameters of each link of the extremum searching closed-loop control structure, and ensuring that the extremum searching closed-loop control structure meets a time scale separation principle so as to ensure the stability of the extremum searching closed-loop control structure;
the method for selecting the parameters of each link of the extremum searching closed-loop control structure meeting the time scale separation principle comprises the following specific steps:
Step 3.1, calculating the maximum response time of the periodic signal c according to the frequency range of the input periodic signal c, and recording the maximum response time as tau c;
Step 3.2, ensuring response time tau b＜τc of the extremum searching closed-loop control structure by selecting reasonable high-pass link turning frequency omega b in step 2.4;
Step 3.3, ensuring response time τ e＜τb of the excitation signal by selecting reasonable frequency ω e of the periodic excitation signal described in step 2.4;
And 3.4, ensuring response time tau d＜τe of the extremum function D by designing the order of the extremum function D reasonably.
Step 4, designing an extremum function S of a combined signal envelope and phase sensitivity detection composite module, which is used for primarily identifying a periodic signal c input to the extremum function S and outputting a real-time amplitude identification value (approximate value) of a single component in the periodic signal c;
The method for identifying the real-time amplitude of the single component in the periodic signal c specifically comprises the following steps:
Step 4.1, designing a band-pass filter with the extremum function S as a central frequency u, and when the central frequency u of the extremum function S is consistent with the real-time frequency of the input periodic signal c, outputting the extremum function S with a maximum value, wherein the maximum value is consistent with the real-time amplitude of the input periodic signal c;
Step 4.2, constructing a signal c ⊥ orthogonal to the input periodic signal c by using the center frequency u and the input periodic signal c;
Step 4.3, obtaining constant values not including periodic components by using the input periodic signal c and the orthogonal signal c ⊥ thereof according to triangle identity
Step 4.4, designing a link P based on a phase sensitivity detection technology to primarily obtain a real-time amplitude approximation value of the input periodic signal c by coping with noise in the environment and the high-frequency component introduced in step 4.3.
Step 5, introducing a signal processor, and filtering out extra period components of the extremum searching closed-loop control structure, which are introduced by the excitation signal;
The method for filtering extra period components of the extremum searching closed-loop control structure introduced by the excitation signal specifically comprises the following steps:
Step 5.1, designing wave traps H 1 and H 2 with frequency doubling and frequency tripling as central frequencies according to the frequency omega e of the periodic excitation signal in step 2.4;
step 5.2, extracting the central frequency u of the extremum function D in step 1.1, and obtaining the real-time estimated frequency of the input periodic signal c through the serial wave traps H 1 and H 2
Step 5.3, extracting the approximate amplitude value estimated in step 4.4, and obtaining the real-time estimated amplitude value of the input periodic signal c through the serially connected wave traps H 1 and H 2
The construction of the control structure for identifying the frequency and amplitude of the single component of the periodic signal c is completed.
Step 6, connecting n control structures designed in the steps 1 to 5 in parallel to form n identification channels, selecting initial values u i0, i=1, 2, & gt, k and k of different center frequencies u i in each identification channel, setting different parameters for each identification channel, and realizing real-time harmonic characteristic extraction of multi-component signals;
The number n of the identification channels is greater than or equal to the number k of the frequency components. Example 2
In order to simulate the experimental environment, numerical simulation under multiple environments is performed on a fourier Transform method (Fourier Transform), a Hilbert-Huang Transform method (Hilbert-Huang Transform), a support vector machine method (Support vector machine) and the method of the present invention, so as to discuss noise sensitivity, identification capability for time-varying frequency signals and real-time identification of the methods.
The fourier transform method is an off-line method that must be analyzed after the complete signal data is obtained. The simulation results of the Fourier transform method are shown in FIG. 3, wherein FIG. 3 (a) is a simulation experiment result curve for identifying a constant frequency signal, FIG. 3 (b) is a simulation experiment result curve for identifying a time-varying frequency signal, and FIG. 3 (c) is a simulation experiment result curve for identifying a time-varying-first-constant frequency signal. The method can be seen from the simulation results as shown in fig. 3:
1. Is insensitive to noise, and has almost the same identification capacity in a noise environment as in a noise-free environment;
2. The time characteristic of the signal cannot be reflected, namely the time-varying signal cannot be processed, and only the average frequency of the time-varying signal can be output;
3. the identification is not real-time and must be performed off-line.
The hilbert-yellow transform method is also an off-line method that must be analyzed after the complete signal data is obtained. The simulation result of the Hilbert-Huang transform method is shown in FIG. 4, wherein FIG. 4 (a) is a simulation experiment result curve for identifying a constant frequency signal, FIG. 4 (b) is a simulation experiment result curve for identifying a time-varying frequency signal, and FIG. 4 (c) is a simulation experiment result curve for identifying a time-varying and then constant frequency signal. The method can be seen from the simulation results as shown in fig. 4:
1. Is very sensitive to noise, and can hardly obtain effective results in a noisy environment;
2. The time characteristics of the signal can be reflected, the time-varying signal can be processed, but there is a "boundary effect", i.e., an undesirable divergent result is produced at the beginning, end, or junction of the data;
3. the identification is not real-time and must be performed off-line.
The support vector machine method is an online method, which requires a large amount of data to learn first, and then can realize online identification. The simulation result of the support vector machine method is shown in fig. 5, wherein fig. 5 (a) is a simulation experiment result curve for identifying a constant frequency signal under the condition of no noise, fig. 5 (b) is a simulation experiment result curve for identifying a time-varying first and then a constant frequency signal under the condition of no noise, fig. 5 (c) is a simulation experiment result curve for identifying a constant frequency signal under the condition of noise, and fig. 5 (d) is a simulation experiment result curve for identifying a time-varying first and then a constant frequency signal under the condition of noise. The method can be seen from the simulation results as shown in fig. 5:
1. Is insensitive to noise, and has almost the same identification capacity in a noise environment as in a noise-free environment;
2. The time characteristic of the signal can be reflected, and the time-varying signal can be processed;
3. the identification has 'certain' real-time property, and can realize online identification after learning a large number of data sets, but the learning process takes a very long time.
The method is an online method, and can output the identification value in real time along with the output of data. The simulation results of the method are shown in fig. 6, wherein fig. 6 (a) is a curve of a simulation experiment result of a constant frequency signal for identifying two components, and fig. 6 (b) is a curve of a simulation experiment result of a frequency signal for identifying time-varying first and then constant. The method of the present invention can be seen from the simulation results shown in fig. 6:
1. noise has a slight influence on the identification time, but does not influence the identification precision;
2. The time characteristic of the signal can be reflected, and the time-varying signal can be processed;
3. the identification has real-time performance, and can output an identification value along with the obtained data in real time;
4. The multi-component signal can be processed.
Compared with the experimental results shown in fig. 3-6, the method provided by the invention realizes real-time identification and output of the frequency and amplitude of the multi-component periodic signal, has small influence on the identification accuracy by noise, can process the time-varying signal, is suitable for being applied to a complex industrial automation system, and has an effect obviously superior to that of the prior art in the aspect of on-line harmonic characteristic extraction of the multi-component periodic signal.
Example 3
As shown in fig. 7, the method of the invention is applied to the on-line harmonic feature extraction of the interference signal of the motor control system containing two known component interferences, and the identification precision is used as an index. The identified interference signal is a voltage/current interference signal affecting the motor angular position output.
The process of the method applied to the hardware platform of the motor control system is as follows:
the method is configured in an industrial control computer according to steps 1 to 6, wherein the number n of identification channels is set to 2. The method extracts the control signal of the motor control system controller as the signal c to be identified, outputs the identification value in real time, and obtains a multi-component periodic signal characteristic extraction result curve as shown in figure 8.
As can be seen from the experimental result curve shown in FIG. 8, the method can adapt to the change of the interference frequency, realize the real-time identification of the interference frequency, simultaneously realize the real-time identification of the interference amplitude in a short time, and respectively identify the interference of two components in the same experiment.
Example 4
The invention also provides an online harmonic feature extraction system for multi-component periodic signal recognition, which is provided with a program module corresponding to the steps of the method in any one of the technical schemes of the embodiment 1 and the embodiment 2, and the steps in the online harmonic feature extraction method for multi-component periodic signal recognition are executed in running.
Example 5
The present invention also provides a computer readable storage medium storing a computer program configured to implement the steps in the online harmonic feature extraction method for multi-component periodic signal identification of the method according to any one of the embodiments 1 and 2 when invoked by a processor.
Although the present disclosure is disclosed above, the scope of the present disclosure is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the disclosure, and such changes and modifications would be within the scope of the disclosure.

Claims (10)

1.一种用于多分量周期信号识别的在线谐波特征提取方法，其特征在于，所述谐波特征提取方法实时运行于电机控制系统的硬件平台上，在线对多分量周期信号的谐波频率与幅值进行辨识，包括以下步骤：1. An online harmonic feature extraction method for multi-component periodic signal identification, characterized in that the harmonic feature extraction method runs in real time on a hardware platform of a motor control system, and online identifies the harmonic frequency and amplitude of the multi-component periodic signal, comprising the following steps: 步骤1、设计一个可调中心频率的极值函数D，用于初步辨识输入给极值函数D的周期信号c，输出对于周期信号c中单一成分的实时频率辨识值；Step 1, design an extreme value function D with an adjustable center frequency, which is used to preliminarily identify the periodic signal c input to the extreme value function D, and output the real-time frequency identification value of a single component in the periodic signal c; 步骤2、基于极值函数D建立极值搜索闭环控制结构；Step 2: Establish an extreme value search closed-loop control structure based on the extreme value function D; 步骤3、选择极值搜索闭环控制结构的各环节参数，确保极值搜索闭环控制结构满足时间尺度分离原则以保证其稳定性；Step 3, selecting parameters of each link of the extreme value search closed-loop control structure to ensure that the extreme value search closed-loop control structure satisfies the time scale separation principle to ensure its stability; 步骤4、设计结合信号包络与相位灵敏检测复合模块的极值函数S，用于初步辨识输入给极值函数S的周期信号c，输出对于周期信号c中单一成分的实时幅值辨识值；Step 4: design an extreme value function S that combines the signal envelope and phase sensitive detection composite module, which is used to preliminarily identify the periodic signal c input to the extreme value function S, and output a real-time amplitude identification value for a single component in the periodic signal c; 步骤5、引入信号处理器，过滤掉极值搜索闭环控制结构因激励信号引入的额外周期成分；Step 5: introduce a signal processor to filter out the extra periodic components introduced by the excitation signal into the extreme value search closed-loop control structure; 至此完成用于对周期信号c单一成分频率和幅值进行辨识的控制结构的构建；So far, the construction of the control structure for identifying the frequency and amplitude of a single component of the periodic signal c has been completed; 步骤6、将n个由步骤1至步骤5所设计的控制结构并联构成n个辨识通道，在每个辨识通道选择不同中心频率ui的初始值ui0，i＝1,2,…,k，k为频率分量的数量，并为每个辨识通道设置不同的参数，实现多分量信号的实时谐波特征提取。Step 6: Connect n control structures designed by steps 1 to 5 in parallel to form n identification channels. Select an initial value u i0 of a different center frequency u i in each identification channel, i = 1, 2, ..., k, k is the number of frequency components, and set different parameters for each identification channel to realize real-time harmonic feature extraction of multi-component signals. 2.根据权利要求1所述的一种用于多分量周期信号识别的在线谐波特征提取方法，其特征在于，步骤1中所述对于周期信号c中单一成分进行实时频率辨识的方法具体步骤包括：2. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 1, characterized in that the method for real-time frequency identification of a single component in the periodic signal c in step 1 specifically comprises the following steps: 步骤1.1、设计极值函数D为中心频率为u的带阻滤波器，当极值函数中心频率u和输入周期信号c的实时频率一致时，极值函数D的输出存在极小值0；Step 1.1, design the extreme value function D as a band-stop filter with a center frequency u. When the center frequency u of the extreme value function is consistent with the real-time frequency of the input periodic signal c, the output of the extreme value function D has a minimum value of 0; 步骤1.2、实时调整极值函数D的中心频率u，以抑制特定频率范围的周期信号，使D的输出维持极小值。Step 1.2: Adjust the center frequency u of the extreme value function D in real time to suppress the periodic signal in a specific frequency range so that the output of D maintains a minimum value. 3.根据权利要求2所述的一种用于多分量周期信号识别的在线谐波特征提取方法，其特征在于，步骤2中所述基于极值函数D建立极值搜索闭环控制结构的过程具体步骤包括：3. The online harmonic feature extraction method for multi-component periodic signal identification according to claim 2 is characterized in that the process of establishing an extreme value search closed-loop control structure based on the extreme value function D in step 2 specifically comprises the following steps: 步骤2.1、将步骤1中设计的极值函数D作为极值搜索算法中存在极值的函数；Step 2.1, use the extreme value function D designed in step 1 as the function with extreme value in the extreme value search algorithm; 步骤2.2、分析所述极值函数D的动态特性，计算信号处理过程中产生的相位滞后范围；Step 2.2, analyzing the dynamic characteristics of the extreme value function D, and calculating the phase lag range generated during the signal processing; 步骤2.3、根据步骤2.2计算得到的相位滞后范围，设计相位补偿环节K，将η个相位补偿环节串联以补偿相位滞后，相位补偿环节K的具体数量η由实际输入信号决定；Step 2.3, according to the phase lag range calculated in step 2.2, design a phase compensation link K, connect n phase compensation links in series to compensate for the phase lag, and the specific number n of the phase compensation links K is determined by the actual input signal; 步骤2.4、设计积分环节转折频率为ωb的高通环节将两个频率为ωe的周期信号Asin(ωet)和Bsin(ωet)作为激励信号，建立极值搜索闭环控制结构，其中A、B为两个频率为ωe的周期信号的幅值，s为拉普拉斯变换中的复变量。Step 2.4: Design the integration phase High-pass link with a turning frequency of ω b Two periodic signals Asin(ω e t) and Bsin(ω e t) with a frequency of ω e are taken as excitation signals, and an extreme value searching closed-loop control structure is established, where A and B are the amplitudes of the two periodic signals with a frequency of ω e , and s is the complex variable in the Laplace transform. 4.根据权利要求3所述的一种用于多分量周期信号识别的在线谐波特征提取方法，其特征在于，步骤3中所述满足时间尺度分离原则的极值搜索闭环控制结构各环节参数的选择方法具体步骤包括：4. The online harmonic feature extraction method for multi-component periodic signal identification according to claim 3 is characterized in that the method for selecting parameters of each link of the extreme value search closed-loop control structure that meets the time scale separation principle in step 3 specifically comprises the following steps: 步骤3.1、根据输入周期信号c的频率范围，计算周期信号c的最大响应时间，记为τc；Step 3.1, according to the frequency range of the input periodic signal c, calculate the maximum response time of the periodic signal c, denoted as τ c ; 步骤3.2、通过选择合理的步骤2.4中所述的高通环节转折频率ωb，确保极值搜索闭环控制结构的响应时间τbτc；Step 3.2, by selecting a reasonable high-pass link corner frequency ω b described in step 2.4, ensure the response time τ b τ c of the extreme value search closed-loop control structure; 步骤3.3、通过选择合理的步骤2.4中所述的周期激励信号频率ωe，确保激励信号的响应时间τe＜τb；Step 3.3: by selecting a reasonable frequency ω e of the periodic excitation signal described in step 2.4, ensure that the response time τ e of the excitation signal is less than τ b ; 步骤3.4、通过设计合理的极值函数D的阶数，确保极值函数D的响应时间τd＜τe。Step 3.4: Ensure that the response time τ d <τ e of the extreme value function D is τ d <τ e by designing a reasonable order of the extreme value function D. 5.根据权利要求4所述的一种用于多分量周期信号识别的在线谐波特征提取方法，其特征在于，步骤4中所述对于周期信号c中单一成分进行实时幅值辨识的方法具体步骤包括：5. The online harmonic feature extraction method for multi-component periodic signal identification according to claim 4 is characterized in that the method for real-time amplitude identification of a single component in the periodic signal c in step 4 specifically comprises the following steps: 步骤4.1、设计极值函数S为中心频率为u的带通滤波器，当极值函数S的中心频率u和输入周期信号c的实时频率一致时，极值函数S的输出存在极大值，该极大值与输入周期信号c的实时幅值一致；Step 4.1, design the extreme value function S as a bandpass filter with a center frequency u. When the center frequency u of the extreme value function S is consistent with the real-time frequency of the input periodic signal c, the output of the extreme value function S has a maximum value, and the maximum value is consistent with the real-time amplitude of the input periodic signal c; 步骤4.2、利用中心频率u和输入周期信号c构造与输入周期信号c正交的信号c⊥；Step 4.2, using the center frequency u and the input periodic signal c to construct a signal c ⊥ orthogonal to the input periodic signal c; 步骤4.3、根据三角恒等式，利用输入周期信号c及其正交信号c⊥获得不包含周期成分的常值 Step 4.3: According to the trigonometric identity, use the input periodic signal c and its orthogonal signal c ⊥ to obtain a constant value that does not contain periodic components. 步骤4.4、设计基于相位灵敏检测技术的环节P，以应对环境中的噪声和步骤4.3中引入的高频成分，初步获得输入周期信号c的实时幅值近似值。Step 4.4: Design link P based on phase-sensitive detection technology to cope with the noise in the environment and the high-frequency components introduced in step 4.3, and preliminarily obtain the real-time amplitude approximation of the input periodic signal c. 6.根据权利要求5所述的一种用于多分量周期信号识别的在线谐波特征提取方法，其特征在于，步骤5中所述过滤极值搜索闭环控制结构因激励信号引入的额外周期成分的方法具体步骤包括：6. The online harmonic feature extraction method for multi-component periodic signal identification according to claim 5, characterized in that the method of filtering the extra periodic components introduced by the excitation signal of the extreme value search closed-loop control structure in step 5 specifically comprises the following steps: 步骤5.1、根据步骤2.4中所述的周期激励信号频率ωe，设计以其一倍频和三倍频为中心频率的陷波器H1与H2；Step 5.1, according to the periodic excitation signal frequency ω e described in step 2.4, design the notch filters H 1 and H 2 with the first harmonic and the third harmonic as the center frequency; 步骤5.2、提取步骤1.1中所述的极值函数D的中心频率u，令其通过串联的陷波器H1与H2，获得输入周期信号c的实时估计频率 Step 5.2: Extract the center frequency u of the extreme value function D described in step 1.1, and pass it through the series-connected notch filters H1 and H2 to obtain the real-time estimated frequency of the input periodic signal c. 步骤5.3、提取步骤4.4中所估计的幅值近似值，令其通过串联的陷波器H1与H2，获得输入周期信号c的实时估计幅值 Step 5.3: Extract the approximate amplitude value estimated in step 4.4 and pass it through the series-connected notch filters H1 and H2 to obtain the real-time estimated amplitude of the input periodic signal c. 7.根据权利要求6所述的一种用于多分量周期信号识别的在线谐波特征提取方法，其特征在于，所述辨识通道数量n大于等于频率分量的数量k。7. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 6, characterized in that the number n of identification channels is greater than or equal to the number k of frequency components. 8.根据权利要求7所述的一种用于多分量周期信号识别的在线谐波特征提取方法，其特征在于，所述步骤2.4中极值搜索闭环控制结构包括：8. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 7, characterized in that the extreme value search closed-loop control structure in step 2.4 comprises: 输入周期信号c、第一激励信号Asin(ωet)、第二激励信号Bsin(ωet)、极值函数D、极值函数S、相位检测环节P、相位补偿环节K、串联的陷波器H1与H2、积分器、转折频率为ωb的高通环节、加法器与乘法器；Input periodic signal c, first excitation signal Asin(ω e t), second excitation signal Bsin(ω e t), extreme value function D, extreme value function S, phase detection link P, phase compensation link K, series-connected notch filters H1 and H2 , integrator, high-pass link with a turning frequency of ω b , adder and multiplier; 所述各模块的连接关系为：The connection relationship between the modules is as follows: 输入周期信号c与信号u为极值函数S的两个输入信号，极值函数S的输出信号作为相位检测环节P的输入信号，构成支路1；The input periodic signal c and the signal u are two input signals of the extreme value function S, and the output signal of the extreme value function S is used as the input signal of the phase detection link P, forming branch 1; 输入周期信号c与信号u为极值函数D的两个输入信号，极值函数D的输出信号作为相位补偿环节K的输入信号，构成支路2；The input periodic signal c and the signal u are two input signals of the extreme value function D, and the output signal of the extreme value function D is used as the input signal of the phase compensation link K to form branch 2; 所述支路1与支路2并联；The branch 1 is connected in parallel with the branch 2; 相位检测环节P的输出信号与信号u为串联的陷波器H1与H2的输入信号；The output signal of the phase detection link P and the signal u are the input signals of the series-connected notch filters H1 and H2 ; 转折频率为ωb的高通环节的输入信号为相位补偿环节K的输出信号，其输出信号与第二激励信号Bsin(ωet)经乘法器作用作为积分器输入信号；The input signal of the high-pass link with a turning frequency of ω b is the output signal of the phase compensation link K, and its output signal and the second excitation signal Bsin(ω e t) are used as the integrator input signal through the multiplier; 所述积分器的输出信号与第一激励信号Asin(ωet)经加法器作用计算得到信号u；The output signal of the integrator and the first excitation signal Asin(ω e t) are calculated by an adder to obtain a signal u; 所述信号u是一个可识别的变量，信号u是极值函数D、极值函数S与串联的陷波器H1与H2的输入信号之一，也是步骤1.1所述带阻滤波器和步骤4.1所述带通滤波器的中心频率；The signal u is an identifiable variable, and the signal u is one of the input signals of the extreme value function D, the extreme value function S and the series-connected notch filters H1 and H2 , and is also the center frequency of the band-stop filter in step 1.1 and the band-pass filter in step 4.1; 串联的陷波器H1与H2输出实时估计幅值与实时估计频率 The output amplitude of the series-connected notch filters H1 and H2 is estimated in real time With real-time estimated frequency 9.一种用于多分量周期信号识别的在线谐波特征提取系统，其特征在于，该系统具有与上述权利要求1～8任一项权利要求所述方法的步骤对应的程序模块，运行时执行所述的用于多分量周期信号识别的在线谐波特征提取方法中的步骤。9. An online harmonic feature extraction system for multi-component periodic signal identification, characterized in that the system has a program module corresponding to the steps of the method described in any one of claims 1 to 8 above, and executes the steps in the online harmonic feature extraction method for multi-component periodic signal identification during operation. 10.一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有计算机程序，所述计算机程序配置为由处理器调用时实现权利要求1～8中任一项所述的用于多分量周期信号识别的在线谐波特征提取方法中的步骤。10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, and the computer program is configured to implement the steps in the online harmonic feature extraction method for multi-component periodic signal identification according to any one of claims 1 to 8 when called by a processor.
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