CN117763329A - Signal time-frequency characteristic extraction method and device based on iteration data - Google Patents
Signal time-frequency characteristic extraction method and device based on iteration data Download PDFInfo
- Publication number
- CN117763329A CN117763329A CN202311535352.2A CN202311535352A CN117763329A CN 117763329 A CN117763329 A CN 117763329A CN 202311535352 A CN202311535352 A CN 202311535352A CN 117763329 A CN117763329 A CN 117763329A
- Authority
- CN
- China
- Prior art keywords
- time
- frequency
- frequency distribution
- signal
- component
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000605 extraction Methods 0.000 title claims abstract description 52
- 238000009826 distribution Methods 0.000 claims abstract description 151
- 238000000034 method Methods 0.000 claims abstract description 37
- 230000004927 fusion Effects 0.000 claims abstract description 34
- 238000012544 monitoring process Methods 0.000 claims abstract description 31
- 238000007781 pre-processing Methods 0.000 claims abstract description 20
- 239000006185 dispersion Substances 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000004590 computer program Methods 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 abstract description 7
- 230000002708 enhancing effect Effects 0.000 abstract description 2
- 238000000354 decomposition reaction Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 6
- 238000004891 communication Methods 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000003745 diagnosis Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000001360 synchronised effect Effects 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- KLDZYURQCUYZBL-UHFFFAOYSA-N 2-[3-[(2-hydroxyphenyl)methylideneamino]propyliminomethyl]phenol Chemical compound OC1=CC=CC=C1C=NCCCN=CC1=CC=CC=C1O KLDZYURQCUYZBL-UHFFFAOYSA-N 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 201000001098 delayed sleep phase syndrome Diseases 0.000 description 1
- 208000033921 delayed sleep phase type circadian rhythm sleep disease Diseases 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000012805 post-processing Methods 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The application discloses a signal time-frequency characteristic extraction method and device based on iteration data, which relate to the technical field of signal processing and are characterized in that: the method comprises the following steps: acquiring a state monitoring signal of equipment; preprocessing the state monitoring signal to obtain time-frequency distribution; sequentially carrying out time-frequency fusion and time-frequency clustering on the time-frequency distribution to obtain component time-frequency distribution, and determining the quantity of the component time-frequency distribution; updating punishment parameters according to the current time-frequency distribution, and judging whether the updated punishment parameters exceed a threshold value; if not, the step S2-S4 is circulated to iterate the time-frequency distribution; if yes, the time-frequency distribution of the signals is identified by using a ridge line classification extraction method, so that the types of the signal components are obtained, and the instantaneous frequency or group delay of the signal components is estimated. By the extraction method, the time-frequency ridge line can be extracted by enhancing time-frequency representation and classification, and meanwhile, the requirements of time resolution and frequency resolution are met, so that accurate estimation of the instantaneous frequency and group delay of the signal is realized.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to a signal time-frequency characteristic extraction method and device based on iteration data.
Background
This section is intended to provide a background or context for the embodiments recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
The mechanical vibration signal contains important information about the health condition of the machine, and the state monitoring and fault diagnosis of the mechanical equipment are realized by extracting relevant characteristics in the vibration signal, so that the method has important significance for guaranteeing the safe and stable operation of the mechanical equipment. However, due to the complex operating environment (multi-source excitation, time-varying conditions, etc.), the state monitoring signal often exhibits complex non-stationary modulation characteristics, and even coexistence of frequency modulation components, dispersion components, and discontinuous components presents a significant challenge to signal characteristic extraction.
The existing signal analysis method mainly comprises two main types of signal decomposition and time-frequency analysis. The signal decomposition method decomposes the vibration signal into a series of subcomponents with definite physical meaning according to a preset signal model. Over the past decades, scholars at home and abroad have developed a number of signal decomposition methods to extract the fundamental component components within the signal, such as empirical mode decomposition, variational mode decomposition, and nonlinear frequency modulation component decomposition. However, the signal model of these decomposition methods cannot characterize both frequency modulation and dispersion characteristics, and thus it is difficult to analyze complex non-stationary signals. Time-frequency analysis is one of the most effective methods of characterizing frequency modulation and dispersion characteristics. In order to increase the resolution of time-frequency distribution, scholars have developed many post-processing methods based on time-frequency reassignment, such as synchronous compression transformation, synchronous extraction transformation and modified versions thereof. However, the above method moves the time-frequency coefficient only in one direction to maintain the signal reconstruction capability, and thus cannot improve both the time resolution and the frequency resolution, and thus it is difficult to accurately characterize both the frequency modulation characteristic and the frequency dispersion characteristic of the signal.
Disclosure of Invention
Aiming at the technical problems, the invention provides a signal time-frequency characteristic extraction method and device based on iteration data, which can extract a time-frequency ridge line by enhancing time-frequency representation and classification, simultaneously meet the requirements of time resolution and frequency resolution, and realize accurate estimation of instantaneous frequency and group delay of signals.
In order to solve the technical problems, the technical scheme adopted by the invention comprises four aspects.
In a first aspect, a signal time-frequency feature extraction method based on iterative data is provided, including the following steps:
s1, acquiring a state monitoring signal of equipment;
s2, preprocessing the state monitoring signal to obtain time-frequency distribution after removing noise time-frequency coefficients lower than a threshold value;
s3, sequentially carrying out time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution to obtain high-resolution time-frequency distribution of the components, removing dispersed noise time-frequency values of the high-resolution time-frequency distribution of the components, and determining the quantity of the time-frequency distribution of the components;
s4, updating punishment parameters according to the current time-frequency distribution, and judging whether the updated punishment parameters exceed a threshold value;
if not, the method loops to the steps S2-S4 to perform preprocessing, time-frequency fusion and iteration of time-frequency clustering operation on the time-frequency distribution;
if so, determining that the time-frequency distribution result tends to be stable, identifying the time-frequency distribution of the signal by using a ridge line classification extraction method, obtaining the signal component type and estimating the instantaneous frequency or group delay of the signal component type.
In some embodiments, the preprocessing the state monitoring signal to obtain a time-frequency distribution after removing noise time-frequency coefficients below a threshold value includes:
performing short-time Fourier transform on the state monitoring signals to obtain time-frequency distribution of the state monitoring signals;
normalizing the time-frequency coefficient of the time-frequency distribution;
and carrying out threshold denoising operation on the normalized time-frequency distribution, and removing the time-frequency coefficient lower than the threshold noise to obtain the time-frequency distribution with the time-frequency coefficient lower than the threshold noise removed.
In some embodiments, the sequentially performing time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution to obtain high-resolution time-frequency distribution of the components and remove dispersed noise time-frequency values thereof, and determining the number of the component time-frequency distribution includes:
performing time-frequency fusion operation on the denoised time-frequency distribution to obtain time-frequency distribution represented by high-resolution time-frequency;
and performing time-frequency clustering operation on the fused time-frequency distribution, reaching the high-resolution time-frequency distribution of the components, removing the scattered noise time-frequency values of the components, and determining the quantity of the time-frequency distribution of the components.
In some embodiments, the identifying the signal time-frequency distribution by using the ridge line classification extraction method, obtaining the signal component type and estimating the instantaneous frequency or group delay thereof comprises:
s51, extracting a ridge line of a k component time-frequency distribution along a time direction and a frequency direction from the signal time-frequency distribution respectively;
s52, calculating the average distance indexes of the ridge lines of the time-frequency distribution of the extracted kth component in the time direction and the frequency direction respectively;
s53, classifying the time-frequency distribution according to the average distance index in the time direction and the average distance index in the frequency direction, and determining the signal component type;
s54, determining the instantaneous frequency or group delay of the signal component according to the type of the component.
In some embodiments, the signal component types include frequency modulated components and dispersion components; said determining the instantaneous frequency or group delay of the signal component based on the signal component type, comprising:
when the signal component type is a frequency modulation component, the ridge line extracted along the time direction is the instantaneous frequency of the component;
when the signal component type is a dispersion component, the ridge line extracted in the frequency direction is the group delay of the component.
In some embodiments, the identifying the signal time-frequency distribution by using the ridge line classification extraction method, obtaining the signal component type and estimating the instantaneous frequency or group delay thereof, further includes:
judging whether k is the last component in the time-frequency distribution;
if not, the process loops to steps S51-S54 until all components of the time-frequency distribution determine their instantaneous frequency or group delay.
In some embodiments, the classifying the time-frequency distribution according to the average distance index in the time direction and the average distance index in the frequency direction, and determining the signal component type includes:
comparing the average distance index in the time direction and the frequency direction with a preset threshold value;
when the average distance index is larger than a preset threshold value, determining the average distance index in the kth time direction as a frequency modulation component, and determining a dispersion component by the average distance index in the kth frequency direction;
classifying the score using a ridge slope and a preset constant when the average distance index is equal to a preset threshold; specific:
comparing the slope of the ridge line with a preset constant;
when the slope of the ridge line is smaller than or equal to a preset constant, the kth component is determined to be a frequency modulation component;
when the slope of the ridge line is greater than a preset constant, the kth component is determined to be a dispersion component.
In a second aspect, a signal time-frequency feature extraction device is provided, including:
the acquisition module is used for monitoring signals of the state of the equipment;
the data preprocessing module is used for preprocessing the state monitoring signals to obtain time-frequency distribution after removing noise time-frequency coefficients lower than a threshold value;
the data fusion clustering module is used for sequentially carrying out time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution, obtaining high-resolution time-frequency distribution of the components, removing dispersed noise time-frequency values of the high-resolution time-frequency distribution, and determining the quantity of the time-frequency distribution of the components;
the computing module is used for updating the punishment parameters according to the current time-frequency distribution and judging whether the updated punishment parameters exceed a threshold value or not;
and the analysis and identification module is used for identifying the time-frequency distribution of the signals by using a ridge line classification and extraction method, obtaining the types of the signal components and estimating the instantaneous frequency or group delay of the signal components.
In a third aspect, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor implements the steps of the aforementioned signal time-frequency feature extraction method when the processor executes the computer program.
In a fourth aspect, a computer readable storage medium is provided, which when executed by a processor implements the steps of the signal time-frequency feature extraction method as described above.
One or more embodiments of the above-described solution may have the following advantages or benefits compared to the prior art:
the application provides a signal time-frequency characteristic extraction method and device based on iteration data, wherein the signal time-frequency characteristic extraction method comprises the following steps: s1, acquiring a state monitoring signal of equipment; s2, preprocessing the state monitoring signal to obtain time-frequency distribution after removing noise time-frequency coefficients lower than a threshold value; s3, sequentially carrying out time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution to obtain high-resolution time-frequency distribution of the components, removing dispersed noise time-frequency values of the high-resolution time-frequency distribution of the components, and determining the quantity of the time-frequency distribution of the components; s4, updating punishment parameters according to the current time-frequency distribution, and judging whether the updated punishment parameters exceed a threshold value; if not, the method loops to the steps S2-S4 to perform preprocessing, time-frequency fusion and iteration of time-frequency clustering operation on the time-frequency distribution; and S5, if so, determining that the time-frequency distribution result tends to be stable, and identifying the time-frequency distribution of the signals by using a ridge line classification extraction method to obtain the signal component type and estimating the instantaneous frequency or group delay of the signal component type. According to the extraction method, self-adaptive time-frequency fusion denoising can be realized in an iterative mode, so that the self-adaptability and noise robustness are improved, the time-frequency resolution of time-frequency analysis is improved through time-frequency data fusion, the requirements of time resolution and frequency resolution are met, the high-quality time-frequency distribution of each component is segmented by using a time-frequency clustering technology, the duration and frequency bandwidth of discontinuous components of a signal can be accurately estimated, and the accurate estimation of the instantaneous frequency and group delay of the signal is realized.
Drawings
The present application will be described in more detail hereinafter based on embodiments and with reference to the accompanying drawings;
fig. 1 is an exemplary flowchart of a signal time-frequency feature extraction method based on iterative data according to a first embodiment of the present invention;
FIG. 2 is an exemplary flow chart corresponding to step S2 shown in FIG. 1 in an embodiment of the invention;
FIG. 3 is an exemplary flow chart corresponding to step S3 shown in FIG. 1 in an embodiment of the invention;
FIG. 4 is an exemplary flow chart corresponding to step S5 shown in FIG. 1 in an embodiment of the invention;
FIG. 5 is an exemplary flow chart corresponding to step S53 shown in FIG. 4 in an embodiment of the invention;
fig. 6 is an exemplary flowchart corresponding to step S533 shown in fig. 5 in an embodiment of the present invention;
FIG. 7 is a schematic diagram of a component type determining principle in a first embodiment of the present invention;
FIG. 8 is a schematic diagram showing a simulation signal time-frequency representation in a second embodiment of the present invention;
FIG. 9 is a schematic representation of a simulation signal after time-frequency feature extraction in a second embodiment of the present invention;
FIG. 10 is a schematic diagram showing the time-frequency representation of vibration signals of a third center box according to an embodiment of the present invention;
FIG. 11 is a schematic representation of a third embodiment of the present invention after time-frequency feature extraction of a vibration signal of a gearbox;
fig. 12 is a schematic block diagram of a signal time-frequency feature extraction device provided in a fourth embodiment of the present invention;
FIG. 13 is a schematic block diagram of an electronic device provided in a fifth embodiment of the invention;
fig. 14 is a schematic diagram of a computer-readable storage medium provided in the sixth embodiment of the present invention.
In the drawings, like parts are given like reference numerals, and the drawings are not drawn to scale.
Detailed Description
The disclosure is further described below with reference to the embodiments shown in the drawings.
Embodiment one:
the embodiment of the application discloses a signal time-frequency characteristic extraction method based on iterative data, which comprises the following steps as shown in fig. 1: s1, acquiring a state monitoring signal of equipment; s2, preprocessing the state monitoring signal to obtain time-frequency distribution after removing noise time-frequency coefficients lower than a threshold value; s3, sequentially carrying out time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution to obtain high-resolution time-frequency distribution of the components, removing dispersed noise time-frequency values of the high-resolution time-frequency distribution of the components, and determining the quantity of the time-frequency distribution of the components; s4, updating punishment parameters according to the current time-frequency distribution, and judging whether the updated punishment parameters exceed a threshold value; if not, the method loops to the steps S2-S4 to perform preprocessing, time-frequency fusion and iteration of time-frequency clustering operation on the time-frequency distribution; and S5, if so, determining that the time-frequency distribution result tends to be stable, and identifying the time-frequency distribution of the signals by using a ridge line classification extraction method to obtain the signal component type and estimating the instantaneous frequency or group delay of the signal component type. According to the extraction method, self-adaptive time-frequency fusion denoising can be realized in an iterative mode, so that the self-adaptability and noise robustness are improved, the time-frequency resolution of time-frequency analysis is improved through time-frequency data fusion, the requirements of time resolution and frequency resolution are met, the high-quality time-frequency distribution of each component is segmented by using a time-frequency clustering technology, the duration and frequency bandwidth of discontinuous components of a signal can be accurately estimated, and the accurate estimation of the instantaneous frequency and group delay of the signal is realized.
The signal time-frequency characteristic extraction method based on iterative data provided by at least one embodiment of the present disclosure may be implemented in a manner of software, hardware, firmware or any combination thereof, and loaded and executed by a processor in a device such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, a network server, etc., so as to improve time-frequency resolution, meet requirements of time resolution and frequency resolution, accurately estimate duration and frequency bandwidth of discontinuous components of a signal, and accurately estimate instantaneous frequency and group delay of the signal.
Next, a signal time-frequency feature extraction method based on iterative data according to at least one embodiment of the present disclosure will be described with reference to fig. 1, where the signal time-frequency feature extraction method includes steps S1 to S5.
S1, acquiring a state monitoring signal of equipment.
In some embodiments, the state monitoring signal may be a mechanical vibration signal of the device, so that state monitoring and fault diagnosis of the mechanical device are achieved by extracting relevant features in the vibration signal, and safe and stable operation of the device is ensured.
S2, preprocessing the state monitoring signal to obtain time-frequency distribution after removing noise time-frequency coefficients lower than a threshold value.
In some embodiments, step S2, as shown in fig. 2, includes:
s21, performing short-time Fourier transform on the state monitoring signals to obtain time-frequency distribution of the state monitoring signals;
s22, carrying out normalization processing on the time-frequency coefficients of the time-frequency distribution;
s23, carrying out threshold denoising operation on the normalized time-frequency distribution, and removing the time-frequency coefficient lower than the threshold noise to obtain the time-frequency distribution with the time-frequency coefficient lower than the threshold noise removed.
Specifically, the state monitoring signal is denoted as s (t), and is determined at the time window parameter w (n) Performing short-time Fourier transform to obtain time-frequency distribution of the obtained time-frequency distribution asAnd carrying out normalization processing on the time frequency coefficient, wherein a specific normalization processing formula is as follows:
in the method, in the process of the invention,representation pair->Normalized time-frequency distribution, +.>Representing extremum taking operation; w (w) (n) ＝Round[N(2n)]Time window parameters for the nth iteration; />Representing a rounding operation; n is the number of samples of the signal s (t).
Further, the formula of the threshold denoising operation is as follows:
wherein H is (n) ＝mean(TFDF (n-1) >0)-μ (n-1) ×2std(TFDF (n-1) >0)；TFDF (n-1) Performing step S1.3 for the n-1 th time to obtain a fused time-frequency distribution, TFDF (n-1) >0 represents a time-frequency coefficient greater than 0 in the fused time-frequency distribution,μ (n-1) TFDF after performing step S2 for the n-1 th time (n-1) Obtained penalty parameter, μ (0) ＝0；mean[g]And std [ g ]]Respectively mean and standard deviation operations.
S3, sequentially carrying out time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution, obtaining high-resolution time-frequency distribution of the components, removing dispersed noise time-frequency values of the high-resolution time-frequency distribution, and determining the quantity of the time-frequency distribution of the components.
In some embodiments, as shown in fig. 3, step S3 includes S31-S32, specifically,
s31, performing time-frequency fusion operation on the denoised time-frequency distribution to obtain the time-frequency distribution represented by the high-resolution time-frequency.
In some embodiments, the formula for the time-frequency fusion operation is as follows:
in TFDF (n) Representing the fused time-frequency distribution;
s32, performing time-frequency clustering operation on the fused time-frequency distribution, reaching high-resolution time-frequency distribution of the components, removing scattered noise time-frequency values of the components, and determining the quantity of the time-frequency distribution of the components.
The number of the component time-frequency distribution is recorded as K, and the formula of the time-frequency clustering operation is as follows:
wherein the method comprises the steps ofRepresenting a set of K component time-frequency distributions, +.>Representing a time-frequency clustering operation based on connected regions.
Further, the principle of the time-frequency clustering operation of the connected region is as follows:
wherein, C represents class labels of time-frequency coefficients, d represents the distance between coordinates of non-zero 0-frequency coefficients, and k represents serial numbers of connected areas obtained by clustering.
S4, updating punishment parameters according to the current time-frequency distribution, and judging whether the updated punishment parameters exceed a threshold value;
if not, the method loops to the steps S2-S4 to perform preprocessing, time-frequency fusion and iteration of time-frequency clustering operation on the time-frequency distribution;
and S5, if so, determining that the time-frequency distribution result tends to be stable, and identifying the time-frequency distribution of the signals by using a ridge line classification extraction method to obtain the signal component type and estimating the instantaneous frequency or group delay of the signal component type.
In some embodiments, the penalty parameter is noted μ (n) The specific formula for updating the penalty parameters is as follows:
in TFDF (n) And representing the time-frequency distribution after fusion clustering.
In some embodiments, the threshold value is denoted epsilon, and the value of the threshold epsilon ranges from 0.8 to 1; when the threshold is 0.8, if the updated penalty parameter exceeds 0.8, the determined time-frequency distribution result is a stable result, and when the threshold is 1, the determined time-frequency distribution result is the most stable result.
In some embodiments, as shown in FIG. 4, step S5, including S51-S55, is as follows:
s51, extracting a ridge line of a k component time-frequency distribution along the time direction and the frequency direction from the signal time-frequency distribution.
In some embodiments, K is a sequence number in a time-frequency distribution of the signal, and the whole time-frequency distribution of the signal has K component time-frequency distribution ridge lines, which are sequentially extracted for analysis.
S52, calculating the average distance indexes of the ridge lines of the time-frequency distribution of the extracted kth component in the time direction and the frequency direction respectively.
In some embodiments, the formula for calculating the average distance index in the time direction and the frequency direction is:
wherein ADI f Representing the average distance in the time direction of adjacent ridge points having the same frequency coordinates; ADI (ADI) t Representing the average distance in the frequency direction of adjacent ridge points having the same time coordinates; g and E respectively represent the sampling number of time and frequency; p represents the number of ridge points with the same coordinates in a certain direction, p=1, l, P is the number of ridge points; v (V) e (p) represents the time sampling number corresponding to the p-th ridge point at the frequency coordinate e, V e (0)＝V e (1)-1；V g (p) represents the frequency sampling number corresponding to the p-th ridge point at the time coordinate g, V g (0)＝V g (1)-1。
S53, classifying the time-frequency distribution according to the average distance index in the time direction and the average distance index in the frequency direction, and determining the signal component type.
In some embodiments, as shown in fig. 5 and 7, step S53 includes:
s531, comparing the average distance index in the time direction and the frequency direction with a preset threshold;
s532, when the average distance index is larger than a preset threshold value, determining the average distance index in the kth time direction as a frequency modulation component, and determining a dispersion component by the average distance index in the kth frequency direction;
s533, classifying the components by using the ridge slope and a preset constant when the average distance index is equal to a preset threshold.
In some embodiments, the preset threshold is 1, i.e., ADI f ADI (advanced development) device t To be compared with a preset threshold value 1 to perform component classification.
In some embodiments, as shown in fig. 6 and 7, step S533 includes:
s5331, comparing the slope of the ridge line with a preset constant;
s5332, determining a kth component as a frequency modulation component when the slope of the ridge line is smaller than or equal to a preset constant;
s5333, determining the kth component as a dispersion component when the slope of the ridge line is larger than a preset constant.
Specifically, the preset constant is set to 1, so that the slope of the ridge line is compared and classified with the constant 1, so that the component type is determined.
S54, determining the instantaneous frequency or group delay of the signal component according to the type of the component.
In some embodiments, the signal component types include frequency modulation components and dispersion components, and the specific content of step S54 is:
when the signal component type is a frequency modulation component, the ridge line extracted along the time direction is the instantaneous frequency of the component;
when the signal component type is a dispersion component, the ridge line extracted in the frequency direction is the group delay of the component.
S55, judging whether k is the last component in the time-frequency distribution;
if not, the process loops to steps S51-S54 until all components of the time-frequency distribution determine their instantaneous frequency or group delay.
According to the signal time-frequency characteristic extraction method based on the iterative data, self-adaptive time-frequency fusion denoising can be achieved in an iterative mode, self-adaptation and noise robustness are improved, time-frequency resolution of time-frequency analysis is improved through time-frequency data fusion, requirements of time resolution and frequency resolution are met, high-quality time-frequency distribution of each component is segmented through a time-frequency clustering technology, duration and frequency bandwidth of discontinuous components of a signal can be estimated accurately, and accurate estimation of instantaneous frequency and group delay of the signal is achieved.
Embodiment two:
as shown in FIG. 8, a simulation signal is represented by a time frequency, the signal-to-noise ratio is 4dB, and the sampling frequency is 1000Hz; after the signal time-frequency characteristic extraction method based on the iterative data provided by the embodiment of the invention is processed, the signal time-frequency characteristic shown in figure 9 is obtained. The results show that the invention can accurately extract the instantaneous frequency, group delay and duration and frequency bandwidth of the vibration signals in a noise environment.
Embodiment III:
the signal time-frequency characteristic extraction method based on the iterative data provided by the embodiment of the invention is used for characteristic extraction for estimating an actual vibration signal. FIG. 10 is a time-frequency representation of rail vehicle axlebox vibration acceleration. Fig. 11 shows time-frequency characteristics of the axle box vibration signal extracted by the method provided by the invention. The result shows that the invention can extract various wheel rail damage component characteristics in the axle box vibration signals.
Embodiment four:
the present disclosure further provides a signal time-frequency feature extraction device based on iterative data fusion clustering, where each module in the device corresponds to a step in the above method, and the obtained effect and the solved problem of the module have been described and are not described herein. The signal time-frequency characteristic extraction device based on iterative data fusion clustering, as shown in fig. 12, comprises:
the acquisition module 11 is used for monitoring signals of the state of the equipment;
a data preprocessing module 12, configured to preprocess the state monitoring signal to obtain a time-frequency distribution after removing a noise time-frequency coefficient lower than a threshold value;
the data fusion clustering module 13 is used for sequentially carrying out time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution, obtaining high-resolution time-frequency distribution of the components, removing dispersed noise time-frequency values of the high-resolution time-frequency distribution, and determining the quantity of the time-frequency distribution of the components;
the calculating module 14 is configured to update the penalty parameter according to the current time-frequency distribution, and determine whether the updated penalty parameter exceeds a threshold;
and an analysis and identification module 15, configured to identify the time-frequency distribution of the signal by using a ridge line classification and extraction method, obtain the signal component type, and estimate the instantaneous frequency or group delay thereof.
In some embodiments, the output end of the acquisition module 11 is connected with the input end of the data preprocessing module 12, the output end of the data preprocessing module 12 is connected with the input end of the data fusion clustering module 13, the output end of the data fusion clustering module 13 is connected with the input end of the calculation module 14, and the output end of the calculation module 14 is connected with the input end of the analysis and identification module 15, so that the steps of the signal time-frequency characteristic extraction method of the embodiment are performed, so that the duration and the frequency bandwidth of the discontinuous component of the signal can be accurately estimated, and the accurate estimation of the instantaneous frequency and the group delay of the signal can be realized.
In some embodiments, the apparatus may be part of software, or may be implemented in combination with corresponding hardware, which is not described herein.
Fifth embodiment:
at least some embodiments of the present disclosure also provide an electronic device, as shown in fig. 13, including a memory 21 and a processor 22, where the memory 21 stores a computer program that, when executed by the processor, performs the steps of the signal time-frequency feature extraction method as provided in any one of the embodiments of the present disclosure.
In some embodiments, the processor 22 is configured to perform all or part of the steps in a signal time-frequency feature extraction method as in any of the embodiments of the present disclosure. The memory 21 is used to store various types of data, which may include, for example, instructions of any application or method in the electronic device, as well as application-related data.
The processor 22 may be an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), a digital signal processor (Digital Signal Processor, abbreviated as DSP), a digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), a programmable logic device (Programmable Logic Device, abbreviated as PLD), a field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), a controller, a microcontroller, a microprocessor, or other electronic component implementation for performing the signal time-frequency feature extraction method in the above embodiment.
The Memory 21 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk or optical disk.
Example six:
at least some embodiments of the present disclosure further provide a computer readable storage medium, as shown in fig. 14, on which a computer program 31 is stored, where the computer program 31 implements the steps of the signal time-frequency feature extraction method provided by any one of the embodiments of the present disclosure when executed by a processor.
In some embodiments, the storage medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
Embodiment seven:
embodiments of the present invention also provide a computer program product comprising computer programs/instructions which, when executed by a processor, implement the steps of a signal time-frequency feature extraction method as provided by any of the embodiments of the present disclosure.
The various embodiments in this disclosure are described in a progressive manner, and identical and similar parts of the various embodiments are all referred to each other, and each embodiment is mainly described as different from other embodiments.
The scope of the present disclosure is not limited to the above-described embodiments, and it is apparent that various modifications and variations can be made to the present disclosure by those skilled in the art without departing from the scope and spirit of the disclosure. Such modifications and variations are intended to be included herein within the scope of the following claims and their equivalents.
Claims (10)
1. A signal time-frequency characteristic extraction method based on iterative data is characterized in that: the method comprises the following steps:
s1, acquiring a state monitoring signal of equipment;
s2, preprocessing the state monitoring signal to obtain time-frequency distribution after removing noise time-frequency coefficients lower than a threshold value;
s3, sequentially carrying out time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution to obtain high-resolution time-frequency distribution of the components, removing dispersed noise time-frequency values of the high-resolution time-frequency distribution of the components, and determining the quantity of the time-frequency distribution of the components;
s4, updating punishment parameters according to the current time-frequency distribution, and judging whether the updated punishment parameters exceed a threshold value;
if not, the method loops to the steps S2-S4 to perform preprocessing, time-frequency fusion and iteration of time-frequency clustering operation on the time-frequency distribution;
and S5, if so, determining that the time-frequency distribution result tends to be stable, and identifying the time-frequency distribution of the signals by using a ridge line classification extraction method to obtain the signal component type and estimating the instantaneous frequency or group delay of the signal component type.
2. The method for extracting time-frequency characteristics of a signal based on iterative data according to claim 1, wherein: the preprocessing the state monitoring signal to obtain time-frequency distribution after removing noise time-frequency coefficients lower than a threshold value comprises the following steps:
performing short-time Fourier transform on the state monitoring signals to obtain time-frequency distribution of the state monitoring signals;
normalizing the time-frequency coefficient of the time-frequency distribution;
and carrying out threshold denoising operation on the normalized time-frequency distribution, and removing the time-frequency coefficient lower than the threshold noise to obtain the time-frequency distribution with the time-frequency coefficient lower than the threshold noise removed.
3. The method for extracting time-frequency characteristics of a signal based on iterative data according to claim 1, wherein: the time-frequency distribution after denoising is sequentially subjected to time-frequency fusion and time-frequency clustering to obtain high-resolution time-frequency distribution of components, remove scattered noise time-frequency values of the high-resolution time-frequency distribution, and determine the quantity of the time-frequency distribution of the components, and the method comprises the following steps:
performing time-frequency fusion operation on the denoised time-frequency distribution to obtain time-frequency distribution represented by high-resolution time-frequency;
and performing time-frequency clustering operation on the fused time-frequency distribution, reaching the high-resolution time-frequency distribution of the components, removing the scattered noise time-frequency values of the components, and determining the quantity of the time-frequency distribution of the components.
4. The method for extracting time-frequency characteristics of a signal based on iterative data according to claim 1, wherein: the method for identifying the time-frequency distribution of the signals by using the ridge line classification extraction method, obtaining the types of the signal components and estimating the instantaneous frequency or group delay of the signal components comprises the following steps:
s51, extracting a ridge line of a k component time-frequency distribution along a time direction and a frequency direction from the signal time-frequency distribution respectively;
s52, calculating the average distance indexes of the ridge lines of the time-frequency distribution of the extracted kth component in the time direction and the frequency direction respectively;
s53, classifying the time-frequency distribution according to the average distance index in the time direction and the average distance index in the frequency direction, and determining the signal component type;
s54, determining the instantaneous frequency or group delay of the signal component according to the type of the component.
5. The method for extracting time-frequency characteristics of a signal based on iterative data according to claim 4, wherein: the signal component type comprises a frequency modulation component and a dispersion component; said determining the instantaneous frequency or group delay of the signal component based on the signal component type, comprising:
when the signal component type is a frequency modulation component, the ridge line extracted along the time direction is the instantaneous frequency of the component; when the signal component type is a dispersion component, the ridge line extracted in the frequency direction is the group delay of the component.
6. The method for extracting time-frequency characteristics of a signal based on iterative data according to claim 4, wherein: the method for classifying and extracting the ridge line is used for identifying the time-frequency distribution of the signal to obtain the type of the signal component and estimating the instantaneous frequency or group delay of the signal component, and the method further comprises the following steps:
judging whether k is the last component in the time-frequency distribution;
if not, the process loops to steps S51-S54 until all components of the time-frequency distribution determine their instantaneous frequency or group delay.
7. The method for extracting time-frequency characteristics of a signal based on iterative data according to claim 4, wherein: the classifying the time-frequency distribution according to the average distance index in the time direction and the average distance index in the frequency direction, and determining the signal component type comprises the following steps:
comparing the average distance index in the time direction and the frequency direction with a preset threshold value;
when the average distance index is larger than a preset threshold value, determining the average distance index in the kth time direction as a frequency modulation component, and determining a dispersion component by the average distance index in the kth frequency direction;
classifying the score using a ridge slope and a preset constant when the average distance index is equal to a preset threshold; the method comprises the following steps:
comparing the slope of the ridge line with a preset constant;
when the slope of the ridge line is smaller than or equal to a preset constant, the kth component is determined to be a frequency modulation component;
when the slope of the ridge line is greater than a preset constant, the kth component is determined to be a dispersion component.
8. A signal time-frequency feature extraction device, comprising:
the acquisition module is used for monitoring signals of the state of the equipment;
the data preprocessing module is used for preprocessing the state monitoring signals to obtain time-frequency distribution after removing noise time-frequency coefficients lower than a threshold value;
the data fusion clustering module is used for sequentially carrying out time-frequency fusion and time-frequency clustering on the denoised time-frequency distribution, obtaining high-resolution time-frequency distribution of the components, removing dispersed noise time-frequency values of the high-resolution time-frequency distribution, and determining the quantity of the time-frequency distribution of the components;
the computing module is used for updating the punishment parameters according to the current time-frequency distribution and judging whether the updated punishment parameters exceed a threshold value or not;
and the analysis and identification module is used for identifying the time-frequency distribution of the signals by using a ridge line classification and extraction method, obtaining the types of the signal components and estimating the instantaneous frequency or group delay of the signal components.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the signal time-frequency feature extraction method of any one of claims 1 to 7 when the computer program is executed by the processor.
10. A computer readable storage medium, characterized in that the computer program when executed by a processor implements the steps of the signal time-frequency feature extraction method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311535352.2A CN117763329A (en) | 2023-11-16 | 2023-11-16 | Signal time-frequency characteristic extraction method and device based on iteration data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311535352.2A CN117763329A (en) | 2023-11-16 | 2023-11-16 | Signal time-frequency characteristic extraction method and device based on iteration data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117763329A true CN117763329A (en) | 2024-03-26 |
Family
ID=90309539
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311535352.2A Pending CN117763329A (en) | 2023-11-16 | 2023-11-16 | Signal time-frequency characteristic extraction method and device based on iteration data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117763329A (en) |
-
2023
- 2023-11-16 CN CN202311535352.2A patent/CN117763329A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Intrinsic chirp component decomposition by using Fourier series representation | |
| Frei et al. | Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals | |
| US6993458B1 (en) | Method and apparatus for preprocessing technique for forecasting in capacity management, software rejuvenation and dynamic resource allocation applications | |
| CN110717472B (en) | Fault diagnosis method and system based on improved wavelet threshold denoising | |
| CN108009122B (en) | Improved HHT method | |
| CN111291918B (en) | Rotating machine degradation trend prediction method based on stationary subspace exogenous vector autoregression | |
| CN113326744B (en) | Method and system for detecting on-orbit state abnormity of spacecraft | |
| Azami et al. | Automatic signal segmentation using the fractal dimension and weighted moving average filter | |
| CN117290788B (en) | Power distribution network fault identification method and system based on improved wavelet transformation algorithm | |
| CN113221382A (en) | Method, system and equipment for predicting residual life of industrial equipment | |
| CN114077852A (en) | Intelligent denoising method for strong noise spectrum signal | |
| Li et al. | A Reliable Strategy for Improving Automatic First‐Arrival Picking of High‐Noise Three‐Component Microseismic Data | |
| CN112697483A (en) | LMD multi-scale fluctuation analysis state monitoring method and device | |
| CN112697482A (en) | VMD multi-scale fluctuation analysis state monitoring method and device | |
| Keković et al. | Detection of change points in time series with moving average filters and wavelet transform: Application to EEG signals | |
| CN117763329A (en) | Signal time-frequency characteristic extraction method and device based on iteration data | |
| CN115797318A (en) | Spectral data preprocessing method and device, computer equipment and storage medium | |
| Pan et al. | Symplectic transformation based variational Bayesian learning and its applications to gear fault diagnosis | |
| CN111142134A (en) | Coordinate time series processing method and device | |
| de Sá et al. | Coherence estimate between a random and a periodic signal: Bias, variance, analytical critical values, and normalizing transforms | |
| CN115169747A (en) | Method and device for predicting non-stationary time sequence of power load and related equipment | |
| CN114692693A (en) | Distributed optical fiber signal identification method, device and storage medium based on fractal theory | |
| CN114565794A (en) | Bearing fault classification method, device, equipment and storage medium | |
| CN112697472A (en) | WD multi-scale fluctuation analysis state monitoring method and device | |
| CN113553898A (en) | Method for diagnosing loosening fault of escalator footing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |