363 results about "Time frequency distribution" patented technology

A method and apparatus for determining the mean arterial blood pressure (MAP) of a subject during tonometric conditions. In one embodiment, the apparatus comprises one or more pressure and ultrasound transducers placed over the radial artery of a human subject's wrist, the latter transmitting and receiving acoustic energy so as to permit the measurement of blood velocity during periods of variable compression of the artery. During compression, the ultrasound velocity waveforms are recorded and processed using time-frequency analysis. The time at which the mean time-frequency distribution is maximal corresponds to the time at which the transmural pressure equals zero, and the mean pressure read by the transducer equals the mean pressure within the artery. In another aspect of the invention, the ultrasound transducer is used to position the transducer over the artery such that the accuracy of the measurement is maximized. In yet another aspect of the invention, a wrist brace useful for measuring blood pressure using the aforementioned apparatus is disclosed.

The invention relates to a power distribution network fault circuit selection method based on transient zero-sequence current time-frequency characteristic vectors, and belongs to the technical field of power system relay protection. When a power distribution network runs into a single-phase earth fault via an arc suppression coil grounding system, a transient zero-sequence current component detected by a measuring end is a nonlinear non-stationary signal formed by different frequency components. By taking the fault component as a study object, time-frequency characteristics of a fault transient current of the fault component are analyzed by utilizing the wavelet packet theory, time-frequency distribution regularities among all feeder lines under different fault conditions are described according to similarity of the time-frequency characteristics, and consequently line selection criteria based on transient zero-sequence current time-frequency characteristics can be obtained. The method is simple in principle, only utilizes short-time window zero-sequence current data of 5ms after the fault, can identify faulty feeders under the conditions of small fault angle and high resistance ground fault, has excellent timeliness and robustness, is free from influence of an arc fault or a resistance ground fault, requires a low sampling rate for hardware, and is highly practical.

The invention provides a passive positioning method of a single-vector hydrophone. The passive positioning method includes: (1) performing short-time Fourier transformation on acoustic pressure signals to obtain time frequency distribution of noise signals; (2) extracting time history of strong line spectrum signals, evaluating the recent passing time of a target according to the time history of a line spectrum frequency, and evaluating the moving speed of the target according to Doppler frequency offset of the line spectrum; (3) finding the direction of the target corresponding to the line spectrum according to the acoustic pressure signals and horizontal vibration velocity signals received by the single-vector hydrophone, and obtaining the direction time history of the target during the point measuring process; and (4) evaluating motion tracks of the target according to the direction time history and motion speed of the target. The passive positioning method of the single-vector hydrophone has the advantages of being capable of positioning the target only by continuously measuring the acoustic pressure and the horizontal vibration speed information without knowing the hydrology parameters of the ocean, and is suitable for deep oceans and shallow oceans.

An apparatus for extracting an ambient signal from an input audio signal comprises a gain-value determinator configured to determine a sequence of time-varying ambient signal gain values for a given frequency band of the time-frequency distribution of the input audio signal in dependence on the input audio signal. The apparatus comprises a weighter configured to weight one of the sub-band signals representing the given frequency band of the time-frequency-domain representation with the time-varying gain values, to obtain a weighted sub-band signal. The gain-value determinator is configured to obtain one or more quantitative feature-values describing one or more features of the input audio signal and to provide the gain-value as a function of the one or more quantitative feature values such that the gain values are quantitatively dependent on the quantitative values. The gain value determinator is configured to determine the gain values such that ambience components are emphasized over non-ambience components in the weighted sub-band signal.

The invention relates to a fluid conveying pipe leakage acoustic emission time-frequency positioning method. The fluid conveying pipe leakage acoustic emission time-frequency positioning method comprises the following steps of picking up acoustic emission signals through an acoustic sensor and a vibration sensor which are arranged at two ends of a pipe leakage point respectively and performing cross-correlation analysis on the acoustic emission signals which are picked up; performing time-frequency analysis on cross-correlation functions of the two channels of acoustic emission signals through smooth pseudo Wigner-Ville time-frequency distribution; extracting the time and frequency information corresponding to time-frequency spectrum peak values of the cross-correlation functions of the acoustic emission signals during pipe leakage; serving the time information corresponding to the peak values as the time delay of two observation signals and determining the transmission speed of the leakage acoustic emission signals along a pipe through table look-up on a frequency dispersion curve according to the frequency information of the peak values; determining the pipe leakage position through the time delay and the timely determined acoustic speed. The fluid conveying pipe leakage acoustic emission time-frequency positioning method can be used for performing accurate positioning on the leakage point under the conditions that the leakage acoustic emission frequency dispersion of the fluid conveying pipe is serious and the acoustic speed is difficult to be determined and meanwhile the correlation functions of the single frequency leakage signals are extracted for the time delay estimation and accordingly the degree of correlation of the leakage signals is enhanced and the leakage positioning error is further reduced.

The invention provides an ultrasonic guided wave detection device for quality evaluation of a composite laminated plate. In the device, ultrasonic guided wave is taken as a detection means for accurately and conveniently evaluating quality of the composite laminated plate. The detection means is achieved by the following steps: (a) performing time-frequency analysis on guided wave signals of the composite laminated plate, comparing a time-frequency distribution diagram of the signals with a theoretical time-frequency distribution curve of the guided wave, and providing a mode separation method; (b) obtaining accurate time delay information in a time-frequency domain based on good characteristics of a Gabor wavelet, combining optimal sensor placement and multi-path positioning, broadening the range of defect positioning, introducing cluster analysis and then improving accuracy and reliability of two-dimensional positioning; and (c) improving rationality and accuracy for extracting signal characteristics, and effectively separating various mode components from ultrasonic guided wave signals by adopting an improved HTT method, and taking instantaneous components as a characteristic parameter according to linear regression analysis; and (d) constructing a detection device of the ultrasonic guided wave signals, improving detection steps, designing software architecture and programming for processing the guided wave signals based on the time-frequency analysis.

The invention provides a CEEMD (Complementary Empirical Mode Decomposition)-STFT (Short-Time Fourier Transform) time-frequency information entropy and multi-SVM (Support Vector Machine) based fault diagnosis method for a centrifugal pump. The method comprises the following steps: 1, preprocessing the fault data of the centrifugal pump; 2, extracting fault features; 3, performing dimensionality reduction for the fault features; 4, automatically recognizing a fault mode through a multi-SVM classifier. Vibration signals of the centrifugal pump have the characteristics of being non-stable and low in repeatability and reproducibility, so that some traditional time domain or frequency domain based analysis methods cannot timely reflect the running conditions of a system. The CEEMD is a self-adaptive signal decomposition method and can decompose the signals into a series of intrinsic mode functions; the STFT is a time-frequency analysis method and can analyze non-stable signals; the time-frequency information entropy is a metric of the signal time-frequency distribution complexity and can reflect the fault information hidden in the signals. According to the method, the CEEMD, the STFT and the information entropy method are combined; the method is applied to the actual diagnosis experiment, and the data analysis result shows that the method is high in diagnosis accuracy.

The invention provides a gear failure keyless phase angle domain average computing order analysis method. The method comprises the steps that low-pass phase-protection filtering is conducted on collected accelerated speed signals, and time-frequency distribution of the signals is calculated through smooth pseudo Wigner-Ville distribution; the instantaneous frequency of a gear case rotary shaft is estimated according to a Viterbi optimal path search algorithm, key phase signals are used for estimating a model to conduct point-by-point integration on the instantaneous frequency, and an estimated key phase signal is obtained, calculation order analysis is conducted on the vibration accelerated speed signals through equal angle resampling and the angular domain average technology, and the order spectrum based on instantaneous frequency estimation is obtained. The order spectrogram can effectively reflect the feature information of gear case failures. The gear failure keyless phase angle domain average computing order analysis method integrates the advantages of the smooth pseudo Wigner-Ville distribution, the Viterbi optimal path search algorithm, the angular domain average technology and the calculation order analysis, and can effectively conduct fault diagnosis on a gear case working on the working station of variable speeds.

The invention discloses a two-dimensional ISRA imaging method of an object with micro rotation in the air. The method comprises the following steps that: (1), a radar matriculates an ISAR echo; (2), translation compensation is carried out; (3), time frequency distribution map is drafted; (4), micro doppler distance units are determined; (5), echo separation of a distance unit; (6), it is determined whether all distance unit have been traversed; (7), a distance-doppler method is used to carry out imaging on a rigid body echo; and (8), imaging is carried out on a rotary part echo. According to the invention, a low frequency modulation rate matched filtering method is employed to carry out echo separation, so that an adaptive chirplet decomposition imaging method's disadvantages including large calculated amount, high time consumption and insufficience of real-time property are overcome; therefore, the method has advantages of simple realization, high efficient and high real-time property. According to the invention, an I-Radon conversion is employed to carry out imaging on a rotary part; therefore, defects of an EHT algorithm are overcome, wherein the defects include high image sidelobe of a rotary object, low precision of estimation position and inaccuracy of object identification; and the method has advantages of good image focusing, high position estimation precision and accurate object identification.

The invention discloses a time-frequency spectrum curve extracting method based on edge detection. The method includes the steps that firstly, a k-order origin moment of signal time-frequency distribution is calculated, then, a matrix containing time-frequency spectrum edge information is acquired according to an edge detection method, a time-frequency spectrum curve is acquired by judging the matrix and combining the edge information, parameter space is established according to an existing curve model so that parameters of the curve are estimated, and the time-frequency spectrum curve is extracted. Compared with a tradition detection method, the time-frequency spectrum curve extracting method has the advantages that the structural attributes of signal time-frequency distribution are fully used, the curve extraction accuracy is improved, and especially under the conditions that the signal to noise ratio is low and multiple time-frequency spectrum bands exist, the time-frequency spectrum curve can still be extracted through the method while the performance of a traditional extraction method is reduced and even the traditional extraction method fails. The performance of the method is better for time-frequency spectrum curve extraction.

The invention discloses a multi-scale convolutional neural network based radio signal modulation identification method. The multi-scale convolutional neural network based radio signal modulation identification method comprises the steps of (1) generating a processed radio modulation signal; (2) generating a two-dimensional time-frequency diagram and performing Fourier transform on an instantaneouscorrelation function of the signal to obtain a Wigner-Ville time-frequency distribution diagram of the signals; (3) performing pre-processing on the time-frequency distribution diagram to generate atraining sample set and a test sample set; (4) building a multi-scale convolutional neural network module and training the model; and (5) testing the test set by utilizing the trained network model, calculating the correction rate, obtaining an identification accuracy rate and assessing the network performance. The multi-scale convolutional neural network based radio signal modulation identification method has the advantages of strong universality, no need for manual characteristic extraction and a plenty of priori knowledge, low complexity and accurate and stable classification results, and can be used in the field of signal classification identification technologies.

The invention discloses a synchronous compression transformation order analysis method for rolling bearing fault diagnosis. According to the method, firstly, SST is utilized to analyze a variable-speed vibration signal of a bearing to obtain time-frequency distribution of the vibration signal; secondly, an instantaneous frequency ridge of frequency conversion is extracted and subjected to high-order polynomial fitting, a fitted instantaneous frequency curve is obtained, and phase discrimination time marks are solved according to the fitted instantaneous frequency curve; thirdly, equal-angle resampling is performed on the original vibration signal to obtain an angular domain signal; and fourthly, Hilbert envelope demodulation is performed on the angular domain signal, an envelope order spectrum of the angular domain signal is solved, whether the rolling bearing has a fault or not and the fault type are judged by analyzing the envelope order spectrum, a diagnosis result is given, maintenance suggestions are proposed, and therefore normal running of a metro vehicle is guaranteed. Through the synchronous compression transformation order analysis method, instantaneous frequency conversion information contained in the vibration signal can be precisely extracted, order tracking is realized, a specific hardware device is not needed, the installing process is greatly simplified, and thefault monitoring cost is lowered.

The invention relates to a time-frequency-transformation-based blind extraction method of fetal electrocardiography, which is based on the characteristic that the time domain of an original signal is relatively sparse. The method comprises the following steps: extracting a plurality of paths of mother-fetal mixed electrocardiosignals from different parts of mother's abdomen, and preprocessing thecollected mixed signal, wherein the preprocessing comprises the steps of correcting the baseline shift of the signals, filtering off 50Hz power frequency interference, filtering off high-frequency noise interference such as myoelectricity and the like; selecting two paths of the mother-fetus mixed electrocardiosignals according to the maximal signal-to-noise ratio and separately searching mother-fetal electrocardio wave group positions of the two paths of mixed signals with an R-wave positioning technology to obtain time intervals in which the mother-fetus electrocardio in the two paths of mixed signals are relatively sparse, converting the obtained relatively sparse time intervals into a time frequency domain by using a fuzzy function, calculating signal items and cross items in each time frequency distribution, constructing a comparison function by using generalized Rayleigh's quotient, and finally separating fetal electrocardiosignals from the two paths of mother-fetal mixed electrocardiosignals. The method provided by the invention is tested on the basis of actual collected data, and can well separate fetal electrocardiosignals.

The invention belongs to the field of radar radiation source signal recognition technology and particularly relates to an intra-pulse modulation recognition method based on time frequency image features. The method comprises the steps that (1) radar signals are subjected to Wigner-Vill distribution time frequency transformation to obtain a time frequency distribution image reflecting signal energy along with time and frequency; (2) the time frequency image obtained in the step (1) is subjected to image preprocessing; (3) the image obtained after preprocessing is subjected to interpolation to scatter an original track; (4) the track scattered into single points in the step (3) is analogized into a target location in target tracking, and joint probabilistic data association is introduced to recognize tracks of different radar signals; and (5) a central moment is utilized to extract shape features, etc. of the time frequency image. According to the method, the radar signals are converted into the time frequency image by means of time frequency distribution transformation, and modulation modes of the radar signals which arrive at the same moment under the condition that frequencies are overlapped are recognized respectively through digital image processing.

The invention provides an unmanned aerial vehicle (UAV) classification method and device based on dual frequency radar signal time-frequency distribution. The method comprises steps that S1, short-time Fourier transform is utilized to process time domain data of each UAV acquired by a dual band radar system, and time-frequency maps of two bands of each UAV are acquired; S2, characteristic extraction of the time frequency maps of two bands of each UAV is carried out through a principal component analysis algorithm; and S3, for each UAV, the characteristics of the two bands of the UAV are fusedto acquire fusion characteristics, each fusion characteristic is taken as a sample to input to a support vector machine to classify the UAV. The method is advantaged in that the dual band radar systemis utilized to emit electromagnetic waves in different bands to the UAV, characteristic extraction of the micro Doppler information of UAV echo is carried out, then dual band characteristic fusion analysis is carried out, so different UAV categories are acquired, and UAV classification accuracy is improved.

The invention provided an abnormal sound diagnosis device. Time-frequency analysis of waveform data of the sound or vibration of an object device which is taken in by a waveform data acquisition unit is made by a time-frequency analyzing unit, the time-frequency distribution of the time axis and the frequency axis is determined, a plurality of regions specified by coordinate values of the time axis and the frequency axis of the time-frequency distribution are generated, regions containing variation components different from a stable state of the time-frequency distribution are extracted by a region extracting unit, and abnormality judgment and outputting are performed by a judging unit based on the time-frequency distribution contained in the extracted regions.

The invention discloses a rotary machine instantaneous rotation speed estimation method based on vibration signal synchronous compression transformation. The rotary machine instantaneous rotation speed estimation method comprises the following steps of 1 obtaining a vibration signal in the rotary machine operation process; 2 conducting frequency shift treatment on the measured vibration signal, 3 conducting synchronous compression continuous wavelet transformation on the vibration signal subjected to the frequency shift treatment to obtain the time-frequency distribution of the vibration signal subjected to the frequency shift treatment, 4 utilizing a Viterbi algorithm to extract first-order instantaneous frequency components of the vibration signal subjected to the frequency shift treatment from the obtain time-frequency distribution, and 5 utilizing the extracted first-order instantaneous frequency to recover calculation so as to obtain the rotary machine instantaneous rotation speed. The rotary machine instantaneous rotation speed estimation method adopts a frequency shift algorithm and the synchronous compression continuous wavelet transformation to process the signal, achieves accurate estimation of the instantaneous frequency of the vibration signal, utilizes the Viterbi algorithm to achieve accurate extraction of the instantaneous frequency and can accurately extract the instantaneous rotation speed of a rotary machine which cannot directly measure the instantaneous rotation speed through the vibration signal.

The invention relates to a transformer internal discharge fault type identifying method and a transformer internal discharge fault type identifying device. The method comprises the steps of receiving transformer partial discharge ultrahigh-frequency signals collected by an external antenna sensor, extracting the characteristic parameters of the partial discharge ultrahigh-frequency signals, and inputting the characteristic parameters to a pre-trained classifier to get a transformer partial discharge fault type identification result. The time-frequency distribution map of the partial discharge ultrahigh-frequency signals is drawn and grayed, the initial characteristic parameters of time-frequency distribution of the partial discharge ultrahigh-frequency signals are extracted based on a gray level co-occurrence matrix, and a principal component analysis is made of the initial characteristic parameters to extract the characteristic parameters of the partial discharge ultrahigh-frequency signals. Characteristic parameters can be accurately extracted from the collected transformer partial discharge ultrahigh-frequency signals. The characteristic parameters are input to the pre-trained classifier, and the transformer internal discharge fault type can be identified quickly by the classifier. A basis is provided for transformer maintenance plan arrangement, and safe and stable operation of the power transformers is effectively guaranteed.

The invention discloses a rotary machinery fault feature extracting method based on local mean decomposition (LMD) and local time-frequency entropy. According to the technical scheme, the rotary machinery fault feature extracting method comprises the following steps of: (1) measuring rotary mechanical equipment by using an acceleration transducer, and acquiring vibration acceleration signals; (2) performing LMD on the vibration acceleration signals to obtain a plurality of pulse frequency (PF) components, and determining the instant amplitude and the instant frequency of each component; (3) making a time-frequency spectrum, dividing a time-frequency plane and calculating the local time-frequency entropy; and (4) extracting fault features by utilizing a local time-frequency entropy value as feature quantity and combining experiments. An analyzing process of a rotary machinery fault diagnosis system based on LMD is realized, difference of vibration signals of the equipment on time-frequency distribution and energy distribution in different states is researched, a local time-frequency entropy theory can be used for diagnosing fault of machinery, the local time-frequency entropy of the vibration signals in the different states is calculated after LMD conversion of the vibration signals, and the local time-frequency entropy value is used as the feature quantity to judge whether the equipment fails or not.

The invention relates to a full-wave magnetic resonance signal random noise abatement method combining EMD and TFPF algorithms. The method is a 'blind' filtering method in which filtering range designis not needed. The method comprises the steps of firstly utilizing decomposition characteristics of the EMD algorithm to decompose full-wave magnetic resonance signals into different intrinsic mode functions, then using the TFPF algorithm to code signal dominant mode functions into instantaneous frequencies of unit amplitude analytic signals, and utilizing the characteristic that time-frequency distribution of the analytic signals is concentrated along instantaneous frequency to inhibit random noise. The full-wave magnetic resonance signal random noise abatement method combining the EMD and TFPF algorithms has the advantages that few filtering constraint conditions are needed, the operation is simple, there is no need to design a filtering range in a time-frequency domain, and the methodhas high adaptability to full-wave magnetic resonance signals with a low signal-noise rate; the detection efficiency is remarkably improved, a good denoising effect can be obtained only through once measuring, random noise is effectively reduced, signal components are not lost at the same time, the signal-to-noise ratio can be remarkably increased, and the accuracy of later inversion is improved.

The invention discloses an indoor human body posture recognition method based on weighted combined distance time frequency transformation, and the method comprises the steps: firstly carrying out the MTI filtering of indoor human body posture data recorded by a UWB (ultra wide band) radar, extracting the data comprising the body posture, and selecting an effective distance gate; secondly obtaining a combined distance time frequency distribution map through the weighted combined distance time frequency transformation, and extracting the feature parameters of a body envelope in the map; thirdly determining the mapping relation between different postures and the feature parameters through a machine learning algorithm; finally determining the class of body posture according to the obtained mapping relation. The method is effective and feasible, is reliable in performance, and can accurately recognize different indoor body postures.

The invention relates to a method for identifying a time-varying structure modal frequency based on a time frequency distribution map. The method comprises the following steps of: 1, acquiring structural dynamic response signals of an identified structure and setting sampling time and sampling frequency; 2, performing time frequency transformation on each response signal to obtain a time frequency distribution coefficient and drawing the time frequency distribution map; 3, writing the time frequency distribution coefficient into a corresponding energy distribution form and rearranging the coefficient as a column vector; 4, determining a time frequency distribution region corresponding to the response containing each-order time-varying modal frequency for identification according to the time frequency distribution map of each response; 5, extracting parts with the highest energy time frequency distribution corresponding to the each-order time-varying modal frequency from the time frequency distribution map by using proper time frequency window functions respectively; 6, estimating the each-order time-varying modal frequency by using a weighting nonlinear least square method; and 7, performing error analysis on the identification result. The method has the advantages of clear physical significance, simple and convenient use, high applicability and high anti-interference capability.

The invention discloses an adaptive kernel function and instantaneous frequency estimation-based fault arc detection method of a photovoltaic system. The adaptive kernel function and instantaneous frequency estimation-based fault arc detection method comprises the steps of acquiring a signal xt by a time window with TNCT length, performing non-linear frequency modulation wavelet transform to obtain a iteration time-frequency diagram of the xt, and constructing characteristic quantity, used for judging moment when frequency spectrum energy is increased, according to a frequency component in theselected iteration time-frequency diagram; and obtaining a corresponding matrix distribution form of the xt in the time-frequency domain by adaptive optimal kernel time-frequency distribution after an abrupt changing point where the energy is increased is found out, performing square sum on the matrix according to time dimension to obtain a line vector, selecting a plurality of frequency bands, performing frequency-dimensional integral operation on each frequency band to obtain a plurality of characteristic values input into a trained naive bayesian model, and judging a state of the photovoltaic system in a current time frame. By a plurality of effective time-frequency characteristics, a fault arc in the photovoltaic system is accurately identified, meanwhile, no misoperation under various types arc working conditions also can be ensured, and the photovoltaic system safely and stably run.

The invention discloses an estimation method of a multi-component non-stationary signal instantaneous frequency based on focusing S-transform, and mainly solves the problems that the multi-component non-stationary signal instantaneous frequency with high precision cannot be realized because the energy aggregation is not high and the time frequency resolution ratio cannot be flexible to adjust and cannot realize high precision. The estimation method comprises the realizing steps: 1, inputting a to-be-analyzed non-stationary signal; 2, optimizing a control parameter of a window function in the focusing S-transform according to an aggregation criterion; 3, calculating the optimized focusing S-transform and acquiring a signal time frequency distribution energy diagram; 4, carrying out binarization processing on the time frequency distribution energy diagram of the non-stationary signal; and 5, obtaining the instantaneous frequency estimation on a signal according to a binaryzation time frequency diagram after the binarization processing. According to the method provided by the invention, the self-adaption adjustment of the control parameter of the window function can be realized by improving the S-transform window function, and the time frequency distribution energy aggregation and the instantaneous frequency estimation accuracy can be improved, therefore the method can be used for radar interception, communication countermeasure, voice identification, and medical brain wave signal analysis.

The invention provides a characteristic extraction method of radar in-pulse modulation signals. The characteristic extraction method of radar in-pulse modulation signals comprises the following steps: decomposing the time frequency of the received radar in-pulse modulation signals to obtain a corresponding time-frequency distribution image; and then, analyzing principal components of the time-frequency distribution image after two-dimensional wavelet discomposition to obtain characteristic parameters of each class of radar in-pulse modulation signals so as to construct a corresponding classifier. The extracted signal characteristic parameters have favorable robustness and stronger anti-noise capability, and the generated classifier can be utilized to obtain higher signal identification rate, therefore, the characteristic extraction method can be effectively applied to the identification of the radar in-pulse modulation signals.

The invention discloses a method for positioning an underwater sound pulse signal on the basis of frequency estimation, which comprises the following steps of: a step 10 of receiving the underwater sound pulse signal by utilizing a single hydrophone and carrying out estimation of time-frequency distribution with a self-adaption radial gaussian kernel function on a frequency of the underwater sound pulse signal; and a step 20 of implementing positioning on a target by utilizing sound field matching, wherein the step 10 comprises the following steps of: firstly, receiving the underwater sound pulse signal by utilizing the single hydrophone and determining a fuzzy function on a corresponding two-dimensional frequency-deviation-time-lag region for the underwater sound pulse signal; then setting a total dot number of a discretized frequency deviation and a total dot number of a discretized time lag; converting the fuzzy function in a rectangular coordinate system into a fuzzy function in a polar coordinate system; measuring an optimal spread function by adopting an iterative algorithm to obtain time-frequency distribution of the received signal; and determining a central frequency and corresponding instantaneous time of the received signal. According to the positioning method, parameter estimation on the unknown underwater sound pulse signal of a transmitted signal can be implemented by utilizing the signal hydrophone and the transmitted signal is accurately positioned.

The invention relates to an order tracking method based on nonlinear frequency modulation wavelet transformation. The order tracking method comprises the steps of: firstly, acquiring a vibration signal of a measured rolling bearing through an acceleration or velocity sensor mounted thereon, adopting nonlinear frequency modulation wavelet transformation for carrying out time-frequency analysis on the vibration signal to obtain joint time-frequency distribution of the signal, searching a maximum spectral peak at each moment in the joint distribution to obtain a corresponding instantaneous frequency value, fitting the instantaneous frequency values by utilizing a instantaneous frequency value, and regarding an obtained value as an estimated value of instantaneous frequency; and determining a center and bandwidth of resonant frequency of the vibration signal by utilizing spectral kurtosis, adopting a band-pass filtering method to extract an inherent resonant component, carrying out envelope demodulation on the inherent resonant component, and finally performing uniform angle resampling on the envelope signal by utilizing instantaneous frequency to obtain an order spectrum of the rolling bearing and complete order tracking of the signal.