58 results about "Energy operator" patented technology

The invention discloses a method and device for detecting voltage fluctuation and flicker based on an energy operator and spectrum correction. The method comprises the steps of: extracting an envelope signal of a voltage flicker signal by utilizing a Teager-Kaiser energy operator, and speeding up an operational speed to overcome influences from change of parameters such as a signal frequency, a waveform, an amplitude value and a sampled data length during the extraction of the flicker envelope signal and realize rapid and real-time detection of the flicker signal; and performing improved FFT (Fast Fourier Transform) spectrum correction analysis on the voltage flicker signal by adopting a Kaiser window function with a freely selectable proportion of a width of a main lobe to a height of a side lobe, and exactly obtaining the frequency and the amplitude component of the voltage flicker signal when the frequency, the waveform and the amplitude value of the flicker signal are changed excessively. The device based on the method comprises a signal conditioning unit, a data processing unit and a data storage and display unit all of which are connected in order. The detecting method is convenient for rapid and in-time detection process of the signal; and the device has a simple structure and is easy to realize.

The invention discloses a fault diagnosis method for a rolling bearing based on LCD-MF (Local Characteristic Scale Decomposition)-(Multifractal). The fault diagnosis method comprises the following steps: firstly, obtaining a plurality of intrinsic scale components (ISC) by using LCD, and selecting useful ISCs to respectively calculate Teager energy operators (TEO) to obtain instantaneous amplitude and instantaneous frequency corresponding to each ISC; secondly, analyzing the instantaneous amplitude of each ISC by using multifractal detrended fluctuation analysis (MFDFA), and extracting a generalized Hurst index of each ISC as the multifractal characteristic of each ISC; thirdly, performing dimension reduction by using principal component analysis (PCA) and taking a result obtained by the PCA as a fault feature vector; acquiring a vibration signal of the rolling bearing during work under variable working conditions in real time, processing the acquired vibration signal according to the steps to obtain a corresponding fault feature vector M, and identifying the fault feature vector to realize fault detection and fault positioning of the rolling bearing.

The invention discloses an aero-engine intershaft bearing early weak fault diagnosis method, which belongs to the technical field of aero-engine fault detection. The method includes the following steps: the fault signal of the intershaft bearing is collected; an AR model is used to remove a deterministic signal generated by periodic rotation of the engine rotor; the processed fault signal is subjected to inverse MED filtering to enhance the shock component of the fault signal; a CEEMD method is used to decompose the processed fault signal, an effective modal component is screened according toa kurtosis value index and an autocorrelation function, and the intershaft bearing fault signal is subjected to noise reduction and reconstruction; and through a Teager energy operator, the output energy of the noise reduction signal is obtained, the envelope spectrum is calculated, and through analyzing the frequency component with a frequency component in the envelope spectrum, engine intershaftbearing early weak fault diagnosis is realized. The fault signal can be subjected to preprocessing, screening, reconstruction and demodulation, a fault characteristic signal reflecting the bearing fault is acquired, and the early weak fault of the intershaft bearing can be accurately diagnosed.

The invention discloses a voice activity detection method in complex background noise. The method sequentially comprises the following steps of: (1) performing TEO (Teager Energy Operator) operation on data; (2) pre-weighting input data x(n); (3) performing band-pass filtering; (4) framing and windowing; (5) calculating an evolution value of autocorrelation of each frame and a standard variance thereof; (6) calculating Stati of 20 frames at the initial stage, and a mean (Stati) and a standard variance std (Stati) thereof, comparing the std (Stati) with a preset threshold to judge whether voice is available; (7) calculating subsequent data; (8) calculating Stati of continuous FrameN frames, and performing secondary determination according to the mean (Stati) and the standard variance std (Stati) thereof; (9) considering that the speech interval Speechmin is equal to 100-200ms and duration Silencemin is equal to 500-1,000ms, judging that voice occurs under the condition that Statusfinalis equal to 0 when continuous Ns (the value is related to the FrameN) atatus is equal to 1; and judging that the voice is ended under the condition that Statusfinal is equal to 1 when continuous NE (the value is also related to the FrameN) atatus is equal to 0, and finally judging actual end points of the voice.

The invention relates to the technical field of energy internet planning, in particular to a game theory-based regional energy internet planning mathematical model and planning method, which comprisesthe following steps of: establishing a model of a distributed power supply, an energy storage system and a combined cooling heating and power system which are suitable for game theory planning and analysis; using A public energy operator, a service provider of distributed energy and a user as game participants, planning construction capacity, using pricing strategies and energy using behaviors asrespective decision variables, using the full life cycle income of equipment as earnings, and establishing different game models for regional energy internet planning; Establishing a specific game model under multiple modes according to the decision strategy of each participant; And carrying out solving through an iterative optimization algorithm, comparing and analyzing Nash equilibrium solutions under various game modes, and finally selecting the Nash equilibrium solution under the benefit maximization of all parties as a planning scheme of the regional energy internet.

The invention discloses a composite fault diagnosis method and system of a gear case. The composite fault diagnosis method comprises firstly measuring and storing vibration signals of the gear case; then adopting 1 / 4 sampling translation dual tree complex wavelet transform for resolving; and finally extracting a plurality of fault characteristics by adopting an energy operator demodulation method in a plurality of sub-band signals obtained by resolving, and further identifying a composite fault mode. The composite fault diagnosis method is fused with a complementary characteristic using dual tree complex wavelets and energy operator demodulation, and the obtained gear case is high in recognition capability of composite fault characteristic extraction. By means of speed capability of parallel realization of the dual tree complex wavelet transform and the energy operator demodulation method, partial polling and on-line monitoring of the gear case in a working condition can be completely applied, sudden accidents can be avoided, and the composite fault diagnosis method and system can be applicable to gear case portions in different models.

The invention relates to an MRS (magnetic resonance sounding) FID (frequency identity) signal noise inhibition method. The method comprises steps as follows: a signal detected by an MRS system is subjected to spectral analysis, the detected signal is decomposed into a homonymous component X and a quadrature component Y with a normalization quadrature detection technology, and a low-frequency FID signal is obtained through hardware filtering processing; peak noises of the components X and Y in the FID signal are rejected from acquired data respectively with a non-linear energy operator algorithm; preliminary signal and noise separation is performed on the components X and Y respectively on the basis of a PCA (principal component analysis) method; the components X and Y processed with the PCA method are further decomposed on the basis of an EMD (empirical mode decomposition) method, and a signal trend term is extracted; the components X and Y processed with the EMD method are superimposed and averaged, and an e index curve is obtained. The problems including high probability of loss of signal components with a conventional filtering means and the like are completely solved, and various complicated noises included in the MRS FID signal are effectively inhibited.

The invention relates to a rolling bearing fault feature extraction method based on CEEMD and FastICA and belongs to the technical field of fault diagnosis and signal processing and analysis. The method comprises the following steps that: vibration signals are decomposed into IMF components with different frequencies through the CEEMD algorithm, corresponding IMF components are selected accordingto kurtosis criteria so as to be reconstructed into observation signals, and the residual IMF components are reconstructed into virtual noise channel signals; unmixing and denoising processing is performed on the observation signals and the virtual noise channel signals through the FastICA algorithm; demodulation processing is performed on the denoised signals through the Teager energy operator; and FFT (fast Fourier transformation) is performed on the demodulated signals, the frequency spectrum characteristics of the transformed signals are analyzed, the fault characteristic frequencies of the signals are extracted, and a fault diagnosis result is obtained. With the method adopted, the problem of fault information loss during a denoising process and the problem that noises cannot be completely removed due to modal aliasing can be solved; fault fundamental frequencies and frequency multiplication information can be extracted clearly and accurately; and the fault diagnosis result can beobtained.

The invention discloses a seismic fluid prediction method based on VMD and TK energy operators. The method comprises the following steps: S1, performing variational mode decomposition on a seismic signal to obtain a set of modal components that reflect a natural mode of vibration inside the seismic signal and have an instantaneous frequency with real physical meaning; S2, getting TK energy spectrums of each modal component by using the TK energy operators to obtain more accurate energy distribution on a signal time frequency; and S3, performing effective and accurate seismic fluid prediction by searching fluid characteristics of ''enhanced low frequency energy and attenuated high frequency energy'' of oil and gas contained in the reservoir. The seismic fluid prediction method based on VMDand TK energy operators combines the variational mode decomposition with the TK energy operators, which has stronger identification for the fluid-containing characteristics of the reservoir, and can effectively improve the accuracy of seismic fluid prediction.

A method for automatically identifying and extracting K complex waves in sleep brain waves comprises the following steps of performing wavelet decomposition and reconstruction on brain wave signals; performing Teager energy operator calculation on reconstructed data and obtaining absolute values; smoothing and performing 0 / 1 coarse graining on an obtained Teager energy curve; performing threshold detection on the data which is performed on coarse graining; performing morphology detection on reconstructed signals satisfying the threshold values, enabling the signals at positions which satisfying a morphology condition to be the K complex waves and recording starting and final positions and wave crest and wave trough values and positions. The method for automatically identifying and extracting the K complex waves in the sleep brain waves has the advantages of analyzing the signals which is performed on the wavelet decomposition and the reconstruction through the Teager energy operator, extracting an absolute value sequence of the Teager energy operator and performing smoothness and coarse graining processes, being easy to achieve and high in anti-noise capacity, accurately confirming K complex wave positions and the wave crest and trough values and positions and establishing foundation for identification of a non-rem second period in sleep stage and research of the K complex waves.

An antifriction bearing fault diagnosis method based on an energy operator demodulated depth belief network (DBN) and a particle swarm optimized support vector machine (PSO-SVM); the method comprisesthe following steps: using an energy operator demodulation method to obtain an instantaneous Teager oscillogram and solving a time frequency characteristic statistics parameter thereof; using the DBNto extract secondary characteristics of the time frequency characteristic statistics; finally inputting the extracted characteristic parameter into the PSO-SVM for fault classification. The antifriction bearing fault diagnosis method is higher in accuracy, can greatly shorten the algorithm training time, thus improving the fault diagnosis accuracy and efficiency.

The invention discloses a method for extracting power grid voltage flicker envelope parameters. According to the method, power grid voltage flicker waveforms are sampled at a constant sampling frequency, a flicker envelope amplitude modulated wave component is obtained through energy calculation of an improved Teager energy operator, a cosine window is added to conduct Chirp-Z conversion, the flicker envelope amplitude modulated wave frequency and a correction factor of an amplitude modulated wave amplitude value are extracted by improving the Chirp-Z conversion, and the flicker envelope amplitude modulated wave amplitude value is corrected through the correction factor and accurately obtained; through the combination of the improvement on the energy operator and the improvement on the Chirp-Z conversion, the energy operator is improved, the flicker envelope demodulation calculation process by the energy operator is improved, the noise resistance is improved; by improving the Chirp-Z conversion, the flicker frequency analysis range is flexible and adjustable, and frequency spectrum leakage generated under the condition of asynchronous sampling and errors caused by a picket fence effect are effectively avoided; the correction factor of the amplitude modulated wave amplitude value reduces the errors caused by the energy operator, and the power grid voltage flicker envelope parameters can be accurately measured in real time.

The invention relates to a respiration information processing method. The method includes the following steps: S101, preprocessing a respiration signal, selecting signal frequency band and acquiring a bandwidth-limited signal; S103, subjecting the bandwidth-limited signal to Teager energy operator transformation to acquire instantaneous amplitude and instantaneous frequency of the respiration signal; S105, respectively performing time domain and frequency conversion on the instantaneous amplitude and the instantaneous frequency to acquire instantaneous respiration intensity and instantaneous respiration rate; S107, performing filter processing on the instantaneous respiration intensity and the instantaneous respiration rate respectively to obtain average respiration intensity and average respiration rate. In addition, the invention provides a respiration information processing system. The respiration information processing method has good movement disturbance resisting capability, and the respiration rate and the respiration intensity can be acquired simultaneously, and can be calculated online in real time.

The invention discloses a working condition on-line identification and early warning method of a low frequency oscillation leading model of an electric power system. A non-stationary signal is decomposed and handled by an ensemble empirical mode. By the adoption of a filter, a cross correlation coefficient and a signal energy weight coefficient, leading model components are screened out, a cross correlation function of the leading model components is obtained by a natural excitation technique, the cross correlation function is used as the leading model to identify signals, variable amplitudes and frequencies are identified by the adoption of a teager energy operator, phase positions are identified by the adoption of a time domain peak peak-value method, damping ratio is identified by the adoption of an energy analysis method and is applied to the working condition on-line early warning of low frequency oscillation leading model. The working condition on-line identification and early warning method has the advantages that the dynamic real-time leading model information of a system can be identified quickly and precisely and man-made excitation is not needed, and anti-noise performance is strong, and has an important meaning for maintaining a safe and stable on-line identification and early warning of the electric power system.

An energy facility control system transforms the manner in which energy facilities create, modify, share, and store information during all phases of their lifecycles, from licensing through construction and decommissioning. The energy facility control system provides energy operators and their suppliers with collaborative toolsets to help improve accuracy, reliability and efficiency during every stage of an energy facility's lifecycle.

The invention belongs to the technical field of target tracking, and discloses a Doppler through-wall radar locating method based on an energy operator. The Doppler through-wall radar locating methodincludes an energy operator frequency estimating algorithm and a locating and tracking algorithm. The energy operator frequency estimating algorithm is used to extract a target component of interest in the received echo and estimate the instantaneous frequency feature value of the target component. The locating and tracking algorithm is used to track and locate the target component in real time according to a target frequency. High precision can be ensured and multiple targets can be simultaneously located without constructing a large transceiver antenna array. The cost of the system can be reduced, the size of radar can be reduced, and the power of the battery can be saved. Moreover, complicated multi-channel data fusion can be avoided, and the complexity of signal processing can be reduced. The method has a wide application prospect in the field of real-time detection of Doppler through-wall radar.

The invention discloses a fault indicator based fault positioning method employing a travelling wave-impedance method and used for a double end power distribution network with branches. Combination ofa double-end travelling wave method and a single-end impedance method based on GPS synchronization time check is utilized and a fault indicator is used in a cooperative manner. If a fault occurs andthe fault indicator on a branch does not send an alarm, it is indicated that the fault is in a main line. The double-end travelling wave method is utilized for positioning. If the fault indicator sends an alarm, it is indicated that the fault is in the branch. The single-end impedance method is utilized for positioning, and the position of the fault on the main line or the branch can be judged accurately. Wave head recognition of the double-end travelling wave method adopts a novel algorithm which is an MRSVD decomposition algorithm added with teager energy operators, and the travelling wave head arrival time point can be determined accurately. The method solves a problem that the double-end travelling wave method cannot determine branch faults and ensures the high precision of measurement. The novel wave head recognition algorithm overcomes shortcomings of a traditional wavelet method and a Hilbert-Huang Transform method. The method provided by the invention has high practical value.

The invention discloses a method for extracting a maximally stable extremal region with scale invariance. The method includes the steps that firstly, an initial maximally stable extremal region is detected in an original image through a maximally stable extremal region algorithm; then a scale pyramid of the initial maximally stable extremal region is built through M-scale wavelet transform, characteristic points with the scale invariance are determined in the scale pyramid according to energy operators of an M-scale wavelet transform coefficient, extremal regions corresponding to the characteristic points are obtained from all layers of images of the scale pyramid of the maximally stable extremal region, and the maximally stable extremal region with the scale invariance is extracted through the stability indexes of the extremal region in a multi-scale space; finally, the maximally stable extremal region with the scale invariance is adjusted to be in an oval shape, and the final maximally stable extremal region with the scale invariance is obtained. According to the method for extracting the maximally stable extremal region with the scale invariance, the scale invariance and the maximally stable extremal region are combined, the maximally stable extremal region is extracted, and full affine invariance is achieved.

The invention discloses a gear fault diagnosis method based on ESMF and energy operator demodulation. The gear fault diagnosis method comprises steps of adopting an extreme point symmetric mode decomposition method to realize adaptive decomposition on a vibration signal of a gear fault, wherein first two IMF components comprise rich information of the gear fault in a decomposition result, envelope line over fitting and under fitting phenomenon are overcome, an end effect is weak and operation time is less; performing energy operator demodulation on the first two IMF components to obtain an envelope signal, wherein the demodulation effect is good and calculation is less; and successively searching frequency corresponding to the gear fault from amplitude spectrums and envelope amplitude spectrums of the first IMF component and the second IMF component so as to realize gear fault diagnosis.

A method for separating and identifying composite fault features of a wind power transmission chain includes the following steps of: obtaining a vibration signal by using an acceleration sensor; decomposing the acquired vibration signal in a whole frequency range by using multi-wavelet packet transform; by using a permutation entropy as an evaluation index, selecting single signals satisfying requirements to perform reconstruction to complete signal noise reduction and composite fault separation; and processing the reconstructed signals by using an energy operator demodulation method to identify fault information.

The invention relates to a voltage flicker parameter detection method based on a combined window function. The voltage flicker parameter detection method comprises the following steps: S201, by utilizing a Teager energy operator function improved on the basis of interval K point sampling, and at the aim of an established modulation model for rectangular wave voltage flicker signals, extracting andobtaining a component v(n) of a voltage flicker envelope signal; S202, performing windowing correction on the obtained component v(n) of the voltage flicker envelope signal by adopting an establishedK-RV mutual convolution window function so as to obtain a voltage flicker envelope signal component y(n) subjected to windowing correction; S203, performing spectral analysis on the voltage flicker envelope signal component y(n) by utilizing three spectral line interpolation FFT, so as to obtain an amplitude correction function and a frequency correction function of the voltage flicker envelope signal. According to the method disclosed by the invention, the detection accuracy of voltage flicker can be greatly improved, a determining criterion is provided for classifying flicker modulation waveforms, and studies on voltage flicker modulation waveform complexity of amplitude-modulated waves are expanded.

The invention belongs to the field of mechanical equipment fault diagnosis. The invention discloses a rolling bearing fault diagnosis method based on the Walsh transformation and Teager energy operator. The specific contents are as follows: first, an acceleration sensor is fixed on a bearing seat under test, the acceleration sensor is connected with a data acquisition instrument to obtain a bearing vibration signal, then a bearing fault diagnosis is performed, and subjecting the signal to Walsh transform denoising; fault features are extracted by Teager energy operator demodulation; and the bearing fault type is positioned and identified by comparing with a theoretical calculating value. The invention takes the vibration signal as the research object, combines the Walsh transform denoisingand the Teager energy operator demodulation, and proposes the novel bearing fault diagnosis method. The bearing fault diagnosis method in the invention can effectively diagnose the fault type of a rolling bearing and has the advantages of high diagnostic accuracy.

The invention relates to the field of the electric system, and especially relates to an electric system low-frequency oscillation mode distinguishing method based on multi-element empirical mode decomposition. The method comprises the following steps: performing pre-decomposition processing on a low-frequency oscillation signal in an electric system by using an improvement algorithm based on multi-element empirical mode decomposition, and computing a relative energy value of each eigenmode function component by utilizing quick response capacity of the Teager energy operator, and taking the energy as the judgment evidence to screen out a leading oscillation mode capable of reflecting the real oscillation condition of the system, and rejecting a virtual noise part; and finally computing an oscillation mode parameter corresponding to the leading oscillation mode through a forecast error method, namely frequency and damping ratio, thereby accomplishing the distinguishing of the leading oscillation mode of the electric system. Through the method disclosed by the invention, the leading oscillation mode of the electric system based on the wide area measurement information can be quickly,accurately and efficiently distinguished.

The invention claims for protecting an improved wavelet threshold function denoising method based on a Teager energy operator, relating to the field of voice signal denoising. According to the method,for a wavelet coefficient after wavelet decomposition, the calculation for the Teager energy operator is carried out, the differences between noises and the signal wavelet coefficient are increased,and thus the self-adaption selection for the threshold is facilitated; by improving a soft and hard threshold denoising function, a novel threshold function is provided, the function can overcome thePseudo-Gibbs effect caused by the discontinuity of the hard threshold function, and also can overcome the constant deviation problem occurring after soft threshold function denoising. With the methodprovided by the invention, the signal to noise ratio and the mean square error are greatly improved.

The invention discloses an iterative Teager energy operator (TEO) demodulation method and an iterative TEO demodulation system. The demodulation method comprises the following steps of: acquiring an original signal; performing energy operator demodulation to obtain a signal energy function and a signal differential energy function of the signal, and performing energy separation to obtain an amplitude envelope and instantaneous frequency; performing low-pass filtration to obtain an instantaneous amplitude envelope and the instantaneous frequency of the signal; subtracting the calculated instantaneous amplitude envelope from the amplitude envelope to obtain a residual signal component; and comparing the energy of the residual signal component with the energy of the original signal to obtain an energy difference, continuing the energy operator demodulation if the energy difference is greater than a preset value, otherwise finishing the energy operator demodulation. The iterative TEO demodulation can be implemented through a hardware platform consisting of a digital circuit and an analogue circuit or through a software algorithm on the basis of combination of an energy demodulation algorithm, zero-phase low-pass filtration and energy judgment, and the finally obtained demodulation signal component can be stored and displayed by a selected personal computer (PC) according to the type of the signal or can be displayed in real time by an analogue oscilloscope.

The invention relates to a rolling bearing fault feature extraction method based on the IITD and the AMCKD, belonging to the technical field of fault diagnosis and signal processing analysis. The rolling bearing fault feature extraction method based on the IITD and the AMCKD comprises the following steps: performing an IITD method decomposition operation on an original acquired fault vibration signal first to obtain a series of inherent rotational components; using a kurtosis index to select a PR component with more fault information features for reconstruction; then optimizing the length of areconstruction signal filter in the AMCKD algorithm by using a variable step size network search method; then performing noise reduction processing on the reconstructed signal by using the AMCKD algorithm; performing demodulation processing on the de-noised signal by using a Teager-Kaiser energy operator; and finally, analyzing the spectral features of the signal after an FFT transformation, so that the fault feature frequency information can be extracted. The rolling bearing fault feature extraction method based on the IITD and the AMCKD can effectively extract the fundamental frequency andfrequency doubling feature information of the rolling bearing fault, and has a better fault diagnosis effect.

The invention belongs to the technical field of electrical engineering, and particularly relates to the Hilbert-Hung and the MEMD. The method comprises the steps of: uploading measurement informationof a transmission circuit and a bus node in an actual power grid to a data concentration unit by adopting the real-time data collection function of a PWU device, and extracting key information for mode identification. The method provided by the invention can achieve rapid, accurate and efficient identification for the dominant oscillation mode of the electric power system based on the actually measured information of the PWU, the Teager energy operator criterion is introduced to screen out the key IMF components containing the dominant oscillation mode so as to avoid the influence of noise components in PMU actual measurement data on an identification result; and moreover, the oscillation frequency and the damping ratio change curve are observed by adopting the features of the Hilbert-Hungto track the instant oscillation frequency and the instant damping ratio of the dominant oscillation mode to obtain the mean value of the instant oscillation parameters and estimate the oscillation parameters of the dominant oscillation mode.

The invention discloses a low-sampling-rate traveling wave fault locating method based on VMD (variational mode decomposition) and an SDEO (symmetric difference energy operator), and the method combines the VMD and the SDEO, and can achieve the precise detection of an instantaneous energy abrupt change moment of a wave head at a lower sampling rate through the high-stability decomposition mode andthe excellent tracking signal singularity of an energy operator, thereby achieving the locating of an occurrence point of a fault. The method makes the most of the arrival moment of the head of the traveling wave and line length so as to eliminate the impact on fault range finding, thereby improving the position precision. Moreover, the method can save the equipment cost, and is suitable for actual engineering.

The invention discloses an APIT-MEMD (Adaptive-projection intrinsically transformed multivariate empirical mode decomposition)-based electric power system low-frequency oscillation mode identificationmethod. The APIT-MEMD-based electric power system low-frequency oscillation mode identification method includes the steps: decomposing a multi-component wide-area actual measurement signal s(t) by means of APIT-MEMD, and extracting a set of IMFs (intrinsic mode functions) representing different oscillation frequencies; introducing a Teager energy operator to calculate the energy value of the IMFcomponents, sorting the energy values in the same measurement channel according to the magnitude of the energy values, and selecting the IMF component strongly correlated with the dominant oscillationmode; and estimating the instantaneous oscillation frequency and the instantaneous damping ratio of the dominant oscillation mode corresponding to the strongly correlated IMF component by Hilbert-Huang transform, and averaging the instantaneous oscillation frequency and the instantaneous damping ratio respectively, thereby realizing the identification of the dominant oscillation mode of the powersystem. The APIT-MEMD-based electric power system low-frequency oscillation mode identification method realizes the identification of the low-frequency oscillation mode of the power system based on the actually measured data of the PMU (Phasor Measurement Unit), and improves the identification precision.

The invention discloses a signal processing method and system for blast furnace lining impact echo detection. The material of a detected object is subjected to wave velocity calibration through a timedifference ultrasonic method, calibration wave velocity information is obtained, empirical modal decomposition is conducted on impact echo signals, and an intrinsic model component is obtained; on the basis of the intrinsic model component, the impact echo signals are filtered to obtain filtered signals and energy operators based on the filtered signals; and the filtered signals are classified, and on the basis of a classification result and the wave velocity information, thickness information of the innermost layer of a blast furnace lining is obtained. The technical problem that in the prior art, elastic waves generate the aliasing effect during propagation in a non-uniform layered medium and accordingly the deviation of an erosion state detection result of the blast furnace lining is large is solved. By denoising the collected impact echo signals and further utilizing the energy operators to classify the filtered signals, echoes of various types can be effectively distinguished, sothat corresponding features of a boundary surface are accurately extracted, and finally the thickness information of the innermost layer of the blast furnace lining is accurately extracted.