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

Abstract

An online harmonic feature extraction method, system and computer-readable storage medium for multi-component periodic signal recognition, relating to the field of signal analysis and signal processing. The technical problem to be solved by the present invention is that the harmonic feature extraction method of periodic signals in the prior art has low recognition accuracy, is more sensitive to noise, has a complex decomposition process and poor real-time performance, making it difficult to meet the needs of online signal recognition in complex industrial control systems. Technical highlights: The present invention achieves the purpose of dynamically identifying the frequency of periodic signals, identifying and outputting the frequency of periodic signals in real time by designing a notch filter with an adjustable center frequency as an extreme value function; the present invention achieves the purpose of dynamically identifying the amplitude of periodic signals, identifying and outputting the amplitude of periodic signals in real time by designing a bandpass filter with an adjustable center frequency as an extreme value function, and combining a signal envelope and a phase-sensitive detection composite module. The method of the present invention is used to effectively extract the periodic signal output by the motor control system controller to identify periodic interference.

Description

Technical Field

[0001] The present invention relates to the fields of signal analysis and signal processing, and in particular to an online harmonic feature extraction method for multi-component periodic signal recognition. Background Art

[0002] In modern signal processing and control systems, real-time recognition of periodic signals is a core requirement in many applications. Whether in communications, automatic control, industrial automation, or medical signal processing, accurate identification of periodic signals is crucial for ensuring system stability and optimizing performance. For example, in communications systems, accurate signal recognition helps improve the efficiency and stability of data transmission; in automated production, identifying external interference signals prevents premature equipment failure and improves production efficiency; and in the medical field, analysis of periodic physiological signals effectively supports disease diagnosis and treatment.

[0003] A major challenge in identifying periodic signals is that the signal frequency and amplitude can change dynamically over time, making traditional signal processing methods difficult to handle. The classic Fourier transform (FT) is a widely used frequency domain analysis tool that can decompose a time domain signal into several sinusoidal components. However, the FT has the following limitations:

[0004] (1) Fourier transform requires global signal data for analysis and cannot draw valid conclusions when the signal is not fully acquired, which makes it only suitable for offline processing;

[0005] (2) The Fourier transform assumes that the signal is stable. Therefore, when faced with non-stationary signals or signals whose frequency changes with time, it can only identify the average frequency, resulting in a significant reduction in the recognition effect.

[0006] To address the shortcomings of the Fourier transform in processing dynamic and non-stationary signals, researchers have proposed the short-time Fourier transform (STFT) and the Hilbert-Huang transform (HHT). The short-time Fourier transform captures the local frequency characteristics of the signal by dividing the signal into short time windows and then performing a Fourier transform on the signal within each window. However, this method has an inevitable trade-off between the selection of window width and time resolution, which limits its application in high-precision real-time signal processing. The Hilbert-Huang transform decomposes the signal into a series of intrinsic mode functions (IMFs) through empirical mode decomposition (EMD) and describes the time-varying characteristics of the signal by analyzing the instantaneous frequency. This method performs well in processing nonlinear and non-stationary signals, but it is sensitive to noise and the decomposition process is complex, resulting in poor real-time performance.

[0007] Therefore, it is urgent to propose an online harmonic feature extraction method to meet the needs of online signal recognition in complex industrial control systems. Summary of the Invention

[0008] The technical problems to be solved by the present invention are:

[0009] The existing periodic signal harmonic feature extraction method has low recognition accuracy, is sensitive to noise, has a complex decomposition process, and has poor real-time performance, making it difficult to meet the needs of online signal recognition in complex industrial control systems.

[0010] The present invention provides a technical solution to solve the above technical problems:

[0011] To solve the above technical problems, the present invention provides an online harmonic feature extraction method for multi-component periodic signal identification. The harmonic feature extraction method runs in real time on the hardware platform of the motor control system and identifies the harmonic frequency and amplitude of the multi-component periodic signal online, including the following steps:

[0012] Step 1: Design an extreme value function D with an adjustable center frequency to preliminarily identify a periodic signal c input to the extreme value function D, and output a real-time frequency identification value (approximate value) for a single component in the periodic signal c;

[0013] Step 2: Establish an extreme value search closed-loop control structure based on the extreme value function D;

[0014] Step 3: Select parameters of each link of the extreme value search closed-loop control structure to ensure that the extreme value search closed-loop control structure meets the time scale separation principle to ensure its stability;

[0015] Step 4: Design an extreme value function S that combines the signal envelope and phase-sensitive detection composite module to preliminarily identify the periodic signal c input to the extreme value function S and output a real-time amplitude identification value (approximate value) for a single component in the periodic signal c;

[0016] 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;

[0017] This completes the construction of the control structure for identifying the frequency and amplitude of a single component of the periodic signal c.

[0018] Step 6: Connect n control structures designed from steps 1 to 5 in parallel to form n identification channels, and select a different center frequency u in each identification channel. i The initial value u i0 , i = 1, 2, ..., k, k is the number of frequency components, and different parameters are set for each identification channel to achieve real-time harmonic feature extraction of multi-component signals.

[0019] Furthermore, the method for performing real-time frequency identification on a single component in the periodic signal c in step 1 specifically includes the following steps:

[0020] 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.

[0021] 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 and keep the output of D at a minimum.

[0022] Furthermore, the process of establishing the extreme value search closed-loop control structure based on the extreme value function D in step 2 specifically includes the following steps:

[0023] 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;

[0024] Step 2.2, analyzing the dynamic characteristics of the extreme value function D and calculating the phase lag range generated during the signal processing;

[0025] Step 2.3: Based on the phase lag range calculated in step 2.2, design a phase compensation link K, and connect n phase compensation links in series to compensate for the phase lag. The specific number n of phase compensation links K is determined by the actual input signal.

[0026] Step 2.4: Design the integration process The turning frequency is ω b Qualcomm link The two frequencies are ω e The periodic signal Asin(ω e t) and Bsin(ω e t) as the excitation signal, establish the extreme value search closed-loop control structure, where A and B are two frequencies ω e The amplitude of the periodic signal, s is the complex variable in the Laplace transform.

[0027] Furthermore, the method for selecting parameters of each link of the extreme value search closed-loop control structure that satisfies the time scale separation principle in step 3 specifically includes the following steps:

[0028] Step 3.1. Calculate the maximum response time of the periodic signal c based on the frequency range of the input periodic signal c, denoted as τ c ;

[0029] Step 3.2: By choosing a reasonable high-pass link corner frequency ω as described in step 2.4 b , ensuring the response time τ of the extreme value search closed-loop control structure b <τ c ;

[0030] Step 3.3, by selecting a reasonable periodic excitation signal frequency ω as described in step 2.4 e , ensuring the response time τ of the excitation signal e <τ b ;

[0031] Step 3.4: Ensure the response time τ of the extreme value function D by designing a reasonable order of the extreme value function D d <τ e .

[0032] Furthermore, the method for real-time amplitude identification of a single component in the periodic signal c described in step 4 specifically includes the following steps:

[0033] Step 4.1. Design an 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, which is consistent with the real-time amplitude of the input periodic signal c.

[0034] Step 4.2: Use the center frequency u and the input periodic signal c to construct a signal c that is orthogonal to the input periodic signal c. ⊥ ;

[0035] Step 4.3: According to the trigonometric identity, use the input periodic signal c and its orthogonal signal c ⊥ Get a constant value that does not contain periodic components

[0036] 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.

[0037] Furthermore, the method for filtering the extra periodic components introduced by the excitation signal in the extreme value search closed-loop control structure in step 5 specifically includes the following steps:

[0038] Step 5.1: According to the periodic excitation signal frequency ω described in step 2.4 e , design notch filters H1 and H2 with their first and third harmonics as center frequencies;

[0039] 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.

[0040] Step 5.3: Extract the amplitude approximation 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.

[0041] Furthermore, the number n of the identification channels is greater than or equal to the number k of the frequency components.

[0042] Furthermore, the extreme value search closed-loop control structure in step 2.4 includes:

[0043] Input periodic signal c, first excitation signal Asin(ω e t), the second excitation signal Bsin(ω e t), extreme value function D, extreme value function S, phase detection link P, phase compensation link K, series notch filters H1 and H2, integrator, corner frequency ω b High-pass link, adder and multiplier;

[0044] The connection relationship between the modules is as follows:

[0045] The input periodic signal c and signal u are two input signals of the extreme value function S. The output signal of the extreme value function S is used as the input signal of the phase detection link P, forming branch 1;

[0046] The input periodic signal c and signal u are two input signals of the extreme value function D. The output signal of the extreme value function D is used as the input signal of the phase compensation link K, forming branch 2;

[0047] The branch 1 is connected in parallel with the branch 2;

[0048] 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;

[0049] The turning frequency is ω b The input signal of the high-pass link is the output signal of the phase compensation link K, and its output signal is the same as the second excitation signal Bsin(ω e t) as the integrator input signal through the multiplier;

[0050] The output signal of the integrator is connected to the first excitation signal Asin(ω e t) The signal u is obtained by calculation through the adder;

[0051] The signal u is an identifiable variable. 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. It is also the center frequency of the band-stop filter in step 1.1 and the band-pass filter in step 4.1.

[0052] The series-connected notch filters H1 and H2 output real-time estimated amplitude With real-time estimated frequency

[0053] The present invention also provides an online harmonic feature extraction system for multi-component periodic signal identification. The system has a program module corresponding to the steps of the method described in any one of the above technical solutions, and executes the steps in the above-mentioned online harmonic feature extraction method for multi-component periodic signal identification during operation.

[0054] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program is configured to implement the steps of the online harmonic feature extraction method for multi-component periodic signal identification of the method described in any of the above technical solutions when called by a processor.

[0055] Compared with the prior art, the beneficial technical effects of the present invention are:

[0056] (1) Since the frequency of a periodic signal does not explicitly contain extreme value information, the present invention achieves dynamic identification of the frequency of a periodic signal by designing a notch filter with an adjustable center frequency as an extreme value function, thereby achieving the purpose of real-time identification and output of the frequency of the periodic signal;

[0057] (2) Since the amplitude of a periodic signal does not explicitly contain extreme value information, the present invention realizes dynamic identification of the amplitude of a periodic signal by designing a bandpass filter with an adjustable center frequency as an extreme value function and combining the signal envelope with a phase-sensitive detection composite module, thereby achieving the purpose of real-time identification and output of the amplitude of the periodic signal;

[0058] (3) A signal processor is introduced to assist in filtering out the periodic signal introduced by the excitation signal in the extreme value search closed-loop control structure, thus ensuring the purity of the identification results;

[0059] (4) The parallel structure is used to process multiple signal channels, which expands the application field of multi-component signal recognition, and is particularly suitable for scenarios where complex industrial automation systems contain interference signals of various periodic forms.

[0060] The method of the present invention is applied to a precision servo mechanical system with multiple periodic component interferences. Taking identification accuracy as an indicator, it is verified that the method of the present invention can achieve the purpose of real-time identification of the frequency and amplitude of multiple periodic component interferences in a noisy environment.

[0061] The method of the present invention is used for effectively extracting a periodic signal output by a motor control system controller to identify periodic interference. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 Flowchart of an online harmonic feature extraction method for multi-component periodic signal recognition according to an embodiment of the present invention;

[0063] Figure 2 4 is a block diagram of an online harmonic feature extraction method for multi-component periodic signal recognition in an embodiment of the present invention;

[0064] Figure 3 This is a simulation result curve of the Fourier transform method (existing technology) in the simulation comparison experiment of the embodiment of the present invention;

[0065] Figure 4 This is a simulation result curve of the Hilbert-Huang transform method (existing technology) in a simulation comparison experiment of an embodiment of the present invention;

[0066] Figure 5 This is a simulation result curve of the support vector machine method (existing technology) in the simulation comparison experiment of the embodiment of the present invention;

[0067] Figure 6 1 is a simulation result curve of the method of the present invention in a simulation comparison experiment of an embodiment of the present invention;

[0068] Figure 7 Schematic diagram of an embodiment of the present invention wherein the method of the present invention is used for online harmonic feature extraction of disturbance signals in a motor control system;

[0069] Figure 8 This is a curve of multi-component periodic signal feature extraction results when the method of the present invention is used for online harmonic feature extraction of disturbance signals in a motor control system in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] In order to enable those skilled in the art to better understand the present invention, exemplary embodiments or examples of the present invention will be described below with reference to the accompanying drawings. Obviously, the described embodiments or examples are only some of the embodiments or examples of the present invention, and not all of them. Based on the embodiments or examples of the present invention, all other embodiments or examples obtained by those skilled in the art without creative work should fall within the scope of protection of the present invention.

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0072] Example 1

[0073] like Figure 1 and Figure 2 As shown, the present invention provides an online harmonic feature extraction method for multi-component periodic signal identification. The harmonic feature extraction method runs in real time on the hardware platform of the motor control system, and identifies the harmonic frequency and amplitude of the multi-component periodic signal online, including the following steps:

[0074] Step 1: Design an extreme value function D with an adjustable center frequency to preliminarily identify a periodic signal c input to the extreme value function D, and output a real-time frequency identification value (approximate value) for a single component in the periodic signal c;

[0075] The method for performing real-time frequency identification on a single component in a periodic signal c specifically comprises the following steps:

[0076] 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.

[0077] 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 and keep the output of D at a minimum.

[0078] Step 2: Establish an extreme value search closed-loop control structure based on the extreme value function D;

[0079] The specific steps of the process of establishing the extreme value search closed-loop control structure based on the extreme value function D include:

[0080] 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;

[0081] Step 2.2, analyzing the dynamic characteristics of the extreme value function D and calculating the phase lag range generated during the signal processing;

[0082] Step 2.3: Based on the phase lag range calculated in step 2.2, design a phase compensation link K, and connect n phase compensation links in series to compensate for the phase lag. The specific number n of phase compensation links K is determined by the actual input signal.

[0083] Step 2.4: Design the integration process The turning frequency is ω b Qualcomm link The two frequencies are ω e The periodic signal Asin(ω e t) and Bsin(ω e t) as the excitation signal, establish Figure 2 The extreme value search closed-loop control structure shown in the figure, where A and B are two frequencies ω e The amplitude of the periodic signal, s is the complex variable in the Laplace transform.

[0084] The extreme value search closed-loop control structure in step 2.4 includes:

[0085] Input periodic signal c, first excitation signal Asin(ω e t), the second excitation signal Bsin(ω e t), extreme value function D, extreme value function S, phase detection link P, phase compensation link K, series notch filters H1 and H2, integrator, corner frequency ω b High-pass link, adder and multiplier;

[0086] The connection relationship between the modules is as follows:

[0087] The input periodic signal c and signal u are two input signals of the extreme value function S. The output signal of the extreme value function S is used as the input signal of the phase detection link P, forming branch 1;

[0088] The input periodic signal c and signal u are two input signals of the extreme value function D. The output signal of the extreme value function D is used as the input signal of the phase compensation link K, forming branch 2;

[0089] The branch 1 is connected in parallel with the branch 2;

[0090] 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;

[0091] The turning frequency is ω b The input signal of the high-pass link is the output signal of the phase compensation link K, and its output signal is the same as the second excitation signal Bsin(ω e t) as the integrator input signal through the multiplier;

[0092] The output signal of the integrator is connected to the first excitation signal Asin(ω e t) The signal u is obtained by calculation through the adder;

[0093] The signal u is an identifiable variable. 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. It is also the center frequency of the band-stop filter in step 1.1 and the band-pass filter in step 4.1.

[0094] The series-connected notch filters H1 and H2 output real-time estimated amplitude With real-time estimated frequency

[0095] Step 3: Select parameters of each link of the extreme value search closed-loop control structure to ensure that the extreme value search closed-loop control structure meets the time scale separation principle to ensure its stability;

[0096] The method for selecting parameters of each link of the extreme value search closed-loop control structure that meets the time scale separation principle specifically includes the following steps:

[0097] Step 3.1. Calculate the maximum response time of the periodic signal c based on the frequency range of the input periodic signal c, denoted as τ c ;

[0098] Step 3.2: By choosing a reasonable high-pass link corner frequency ω as described in step 2.4 b , ensuring the response time τ of the extreme value search closed-loop control structure b <τ c ;

[0099] Step 3.3, by selecting a reasonable periodic excitation signal frequency ω as described in step 2.4 e , ensuring the response time τ of the excitation signal e <τ b ;

[0100] Step 3.4: Ensure the response time τ of the extreme value function D by designing a reasonable order of the extreme value function D d <τ e .

[0101] Step 4: Design an extreme value function S that combines the signal envelope and phase-sensitive detection composite module to preliminarily identify the periodic signal c input to the extreme value function S and output a real-time amplitude identification value (approximate value) for a single component in the periodic signal c;

[0102] The method for real-time amplitude identification of a single component in a periodic signal c specifically comprises the following steps:

[0103] Step 4.1. Design an 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, which is consistent with the real-time amplitude of the input periodic signal c.

[0104] Step 4.2: Use the center frequency u and the input periodic signal c to construct a signal c that is orthogonal to the input periodic signal c. ⊥ ;

[0105] Step 4.3: According to the trigonometric identity, use the input periodic signal c and its orthogonal signal c ⊥ Get a constant value that does not contain periodic components

[0106] 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.

[0107] 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;

[0108] The method for filtering the extra periodic components introduced by the excitation signal in the extreme value search closed-loop control structure specifically comprises the following steps:

[0109] Step 5.1: According to the periodic excitation signal frequency ω described in step 2.4 e , design notch filters H1 and H2 with their first and third harmonics as center frequencies;

[0110] 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.

[0111] Step 5.3: Extract the amplitude approximation 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.

[0112] This completes the construction of the control structure for identifying the frequency and amplitude of a single component of the periodic signal c.

[0113] Step 6: Connect n control structures designed from steps 1 to 5 in parallel to form n identification channels, and select a different center frequency u in each identification channel. i The initial value u i0 , i = 1, 2, ..., k, k is the number of frequency components, and different parameters are set for each identification channel to achieve real-time harmonic feature extraction of multi-component signals;

[0114] The number of identification channels n is greater than or equal to the number of frequency components k.

[0115] In order to simulate the experimental environment, numerical simulations were carried out under multiple environments on the Fourier transform method, the Hilbert-Huang transform method, the support vector machine method and the method of the present invention to discuss the noise sensitivity, the recognition ability of time-varying frequency signals and the real-time performance of the above methods.

[0116] The Fourier transform method is an offline method that must be analyzed after obtaining complete signal data. The simulation results of the Fourier transform method are as follows: Figure 3 As shown, Figure 3 (a) is the curve of the simulation experiment result for identifying constant frequency signal; Figure 3 (b) is the curve of the simulation experiment result for identifying time-varying frequency signals; Figure 3 (c) is the curve of the simulation experiment result of identifying the time-varying and then constant frequency signal. Figure 3 The simulation results shown show that this method:

[0117] 1. Insensitive to noise, it has almost the same recognition ability in a noisy environment as in a noise-free environment;

[0118] 2. Unable to reflect the time characteristics of the signal, that is, unable to process time-varying signals and can only output their average frequency;

[0119] 3. Identification is not real-time and must be performed offline.

[0120] The Hilbert-Huang transform method is also an offline method, which must be analyzed after obtaining complete signal data. The simulation results of the Hilbert-Huang transform method are as follows: Figure 4 As shown, Figure 4 (a) is the curve of the simulation experiment result for identifying constant frequency signal; Figure 4 (b) is the curve of the simulation experiment result for identifying time-varying frequency signals; Figure 4 (c) is the curve of the simulation experiment result of identifying the time-varying and then constant frequency signal. Figure 4 The simulation results shown show that this method:

[0121] 1. It is very sensitive to noise and it is almost impossible to obtain effective results in a noisy environment;

[0122] 2. It can reflect the time characteristics of the signal and process time-varying signals, but there is a "boundary effect", that is, undesirable divergent results are generated at the beginning, end or intersection of the data;

[0123] 3. Identification is not real-time and must be performed offline.

[0124] The support vector machine method is an online method that requires a large amount of data to learn first and then can achieve online identification. The simulation results of the support vector machine method are as follows Figure 5 As shown, Figure 5 (a) is the simulation experiment result curve of identifying constant frequency signal in the absence of noise; Figure 5 (b) is the simulation experiment result curve of identifying time-varying and then constant frequency signals in the absence of noise; Figure 5 (c) is the curve of the simulation experiment result of identifying the constant frequency signal in the case of noise; (d) is the curve of the simulation experiment result of identifying the time-varying and then constant frequency signal in the case of noise. Figure 5 The simulation results shown show that this method:

[0125] 1. Insensitive to noise, it has almost the same recognition ability in a noisy environment as in a noise-free environment;

[0126] 2. Able to reflect the time characteristics of the signal and process time-varying signals;

[0127] 3. Recognition has a "certain" real-time nature. It can achieve online recognition after learning a large amount of data sets, but the learning process is extremely time-consuming.

[0128] The method of the present invention is an online method that can output the identification value in real time as the data is output. The simulation results of the method of the present invention are as follows: Figure 6 As shown, Figure 6 (a) is the constant frequency signal simulation result curve for identifying two components; Figure 6 (b) is the curve of the simulation experiment result of identifying the frequency signal that first changes in time and then becomes constant. Figure 6 The simulation results shown show that the method of the present invention:

[0129] 1. Noise has a slight impact on recognition time, but does not affect recognition accuracy;

[0130] 2. Able to reflect the time characteristics of the signal and process time-varying signals;

[0131] 3. The recognition is real-time and can output the recognition value in real time with the acquired data;

[0132] 4. Able to process multi-component signals.

[0133] contrast Figures 3 to 6The experimental results shown in the figure show that the method of the present invention can realize real-time identification and output of the frequency and amplitude of multi-component periodic signals. Its identification accuracy is less affected by noise and can process time-varying signals. It is suitable for application in complex industrial automation systems. Its effect on online harmonic feature extraction of multi-component periodic signals is significantly better than the existing technical methods.

[0134] Example 3

[0135] like Figure 7 As shown, the method of the present invention is applied to the online harmonic feature extraction of interference signals in a motor control system containing two known interference components, with identification accuracy as the indicator. The identified interference signals are voltage / current interference signals that affect the motor angular position output.

[0136] The process of applying the method of the present invention to the hardware platform of the motor control system is as follows:

[0137] The method of the present invention is configured in an industrial control computer according to steps 1 to 6, wherein the number of identification channels n is set to 2. The motor control system is made to run at a variable speed first and then at a constant speed. The method of the present invention extracts the control signal of the motor control system controller as the signal to be identified c, outputs the identification value in real time, and obtains the multi-component periodic signal feature extraction result curve as shown in FIG. Figure 8 shown.

[0138] From Figure 8 It can be seen from the experimental result curve shown that the method of the present invention can adapt to the change of interference frequency and realize real-time identification of interference frequency; at the same time, it can also realize real-time identification of interference amplitude in a relatively short time; in the same experiment, facing the interference of two components, the method of the present invention can separately identify the interference of the two components.

[0139] Example 4

[0140] The present invention also provides an online harmonic feature extraction system for multi-component periodic signal identification, which has a program module corresponding to the steps of the method described in any one of the technical solutions of Example 1 and Example 2, and executes the steps of the above-mentioned online harmonic feature extraction method for multi-component periodic signal identification during operation.

[0141] Example 5

[0142] The present invention also provides a computer-readable storage medium, which stores a computer program. The computer program is configured to implement the steps of the online harmonic feature extraction method for multi-component periodic signal identification described in any one of the technical solutions of Example 1 and Example 2 when called by a processor.

[0143] Although the present invention is disclosed as above, the scope of protection disclosed by the present invention is not limited thereto. Those skilled in the art of the present invention may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the scope of protection of the present invention.

Claims

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 the hardware platform of the motor control system, and identifies the harmonic frequency and amplitude of the multi-component periodic signal online, including the following steps: Step 1: Design an extreme value function D with an adjustable center frequency to preliminarily identify a periodic signal c input to the extreme value function D and output a real-time frequency identification value for a single component in the periodic signal c; Step 2: Establish an extreme value search closed-loop control structure based on the extreme value function D; Step 3: Select parameters of each link of the extreme value search closed-loop control structure to ensure that the extreme value search closed-loop control structure meets the time scale separation principle to ensure its stability; Step 4: Design an extreme value function S that combines the signal envelope and phase-sensitive detection composite module 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; 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; This completes the construction of the control structure for identifying the frequency and amplitude of a single component of the periodic signal c. Step 6: Connect n control structures designed from steps 1 to 5 in parallel to form n identification channels, and select a different center frequency u in each identification channel. i The initial value u i0 , i = 1, 2, ..., k, k is the number of frequency components, and different parameters are set for each identification channel to achieve real-time harmonic feature extraction of multi-component signals; The method for real-time amplitude identification of a single component in the periodic signal c described in step 4 specifically includes the following steps: Step 4.

1. Design an 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, which is consistent with the real-time amplitude of the input periodic signal c. Step 4.2: Use the center frequency u and the input periodic signal c to construct a signal c that is orthogonal to the input periodic signal c. ⊥ ; Step 4.3: According to the trigonometric identity, use the input periodic signal c and its orthogonal signal c ⊥ Get a constant value that does not contain periodic components 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.

2. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 1, characterized in that, The method for performing real-time frequency identification on a single component in the periodic signal c described in step 1 specifically includes the following steps: 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. 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 and keep the output of D at a minimum.

3. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 2, characterized in that, The specific steps of the process of establishing the extreme value search closed-loop control structure based on the extreme value function D in step 2 include: 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; Step 2.2, analyzing the dynamic characteristics of the extreme value function D and calculating the phase lag range generated during the signal processing; Step 2.3: Based on the phase lag range calculated in step 2.2, design a phase compensation link K, and connect n phase compensation links in series to compensate for the phase lag. The specific number n of phase compensation links K is determined by the actual input signal. Step 2.4: Design the integration process The turning frequency is ω b Qualcomm link The two frequencies are ω e The periodic signal Asin(ω e t) and Bsin(ω e t) as the excitation signal, establish the extreme value search closed-loop control structure, where A and B are two frequencies ω e The amplitude of the periodic signal, s is the complex variable in the Laplace transform.

4. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 3, 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 described in step 3 specifically includes the following steps: Step 3.

1. Calculate the maximum response time of the periodic signal c based on the frequency range of the input periodic signal c, denoted as τ c ; Step 3.2: Select a reasonable high-pass link corner frequency ω b , ensuring the response time τ of the extreme value search closed-loop control structure b <τ c ; Step 3.3, by selecting a reasonable periodic excitation signal frequency ω e , ensuring the response time τ of the excitation signal e <τ b ; Step 3.4: Ensure the response time τ of the extreme value function D by designing a reasonable order of the extreme value function D d <τ e .

5. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 4, characterized in that, The method for filtering the extra periodic components introduced by the excitation signal in the extreme value search closed-loop control structure in step 5 specifically includes the following steps: Step 5.1: According to the periodic excitation signal frequency ω described in step 2.4 e , design notch filters H1 and H2 with their first and third harmonics as center frequencies; 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. Step 5.3: Extract the amplitude approximation 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.

6. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 5, characterized in that: The number n of the identification channels is greater than or equal to the number k of the frequency components.

7. An online harmonic feature extraction method for multi-component periodic signal identification according to claim 6, characterized in that: The extreme value search closed-loop control structure in step 2.4 includes: Input periodic signal c, first excitation signal Asin(ω e t), the second excitation signal Bsin(ω e t), extreme value function D, extreme value function S, phase detection link P, phase compensation link K, series notch filters H1 and H2, integrator, corner frequency ω b High-pass link, adder and multiplier; The connection relationship between the modules is as follows: The input periodic signal c and signal u are two input signals of the extreme value function S. The output signal of the extreme value function S is used as the input signal of the phase detection link P, forming branch 1; The input periodic signal c and signal u are two input signals of the extreme value function D. The output signal of the extreme value function D is used as the input signal of the phase compensation link K, forming branch 2; The branch 1 is connected in parallel with the branch 2; 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; The turning frequency is ω b The input signal of the high-pass link is the output signal of the phase compensation link K, and its output signal is the same as the second excitation signal Bsin(ω e t) as the integrator input signal through the multiplier; The output signal of the integrator is connected to the first excitation signal Asin(ω e t) The signal u is obtained by calculation through the adder; The signal u is an identifiable variable. 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. It is also the center frequency of the band-stop filter in step 1.1 and the band-pass filter in step 4.

1. The series-connected notch filters H1 and H2 output real-time estimated amplitude With real-time estimated frequency 8. 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 7, and executes the steps of the online harmonic feature extraction method for multi-component periodic signal identification when running.

9. 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 of the online harmonic feature extraction method for multi-component periodic signal identification according to any one of claims 1 to 7 when called by a processor.

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