126 results about "Vibration signal analysis" patented technology

The invention discloses a wind turbine generator bearing fault diagnosis method under the variable speed. According to the method, a rotation angle change curve is drafted according to the bearing rotation speed; equal angle division for the rotation angle change curve is carried out, and an equal angle re-sampling time sequence is determined; interpolation for a bearing vibration signal is carried out according to the time sequence, a random Gauss white noise sequence is added to an angle domain vibration signal, and the signal added with the white noise is processed by utilizing an empirical mode decomposition (EMD) algorithm to acquire multiple sets of IMF; a kurtosis value of each IMF component is calculated; the IMF with the largest kurtosis value is selected and taken as a sensitive IMF; Hilbert envelope demodulation for the sensitive IMF is carried out to obtain an envelope signal, the envelope signal is processed by utilizing Fourier transform to obtain order envelope spectrum of the sensitive IMF, the fault characteristic frequency is extracted, and fault diagnosis on the wind turbine generator bearing is realized. The method is advantaged in that influence of rotation speed change on vibration signal analysis can be eliminated, and accuracy and validity of fault diagnosis can be improved.

A hydroelectric generating set vibration signal noise reduction method based on mode autocorrelation analysis of SVD and VMD comprises the steps of constructing a Hankel matrix of a set vibration signal and performing singular value decomposition (SVD), selecting an effective singular value based on a mean value filtering strategy for reconstructing the signal, and realizing pre-filtering; performing decomposition through variational mode decomposition (VMD) for obtaining a series of mode functions, calculating an autocorrelation function of each mode component, selecting effective mode components according to an energy set of the autocorrelation function, and obtaining a signal after noise reduction through adding all effective mode components. According to the hydroelectric generating set vibration signal noise reduction method provided by the invention, a noise reduction experiment is carried out through simulation analysis and actual measurement of a vibration signal; and a result represents a fact that the method has relatively high noise reduction performance and can effectively improve hydroelectric generating set vibration signal analysis precision.

The invention discloses a rolling bearing fault diagnosis method based on variational mode decomposition, which is used to analyze a rolling bearing fault based on a vibration signal. First, fault information enhancement processing is performed on a signal collected by a sensor using a predictive filtering method; then, variational mode decomposition is performed on the filtered signal to get four modes; next, a mode most relevant to a rolling bearing fault is selected according to a fault information index; and finally, an envelope autocorrelation spectrum analysis of the filtered signal is made, and fault characteristic frequency matching is performed to get fault information.

The invention relates to an early fault identification method of a rolling bearing under a variable rotating speed working condition. The method comprises the following steps that firstly, time-frequency analysis is carried out on a vibration signal of the rolling bearing to obtain a time-frequency spectrum; secondly, a peak value search method is adopted to extract an instantaneous frequency conversion in the time-frequency spectrum and data fitting is carried out; then high-pass filtering is carried out on an original vibration signal, and equal angle sampling is carried out on the originalvibration signal according to the instantaneous frequency conversion after data fitting to obtain an angle domain signal; finally, noise reduction processing is carried out on the original vibration signal and envelope spectrum analysis is carried out on the noise-reduced signal to identify faults of the rolling bearing. The method does not rely on a rotation speed sensor or human experience, andself-adaptively searches optimal parameters of the MCKD, moreover, the method is convenient and reliable, and is particularly suitable for vibration signal analysis of the rolling bearing with low signal-to-noise ratio under variable rotation speed working conditions.

The invention provides a vibration monitoring system of a reactor and reactor internals, solving the problems of reliability, compatibility, stability and maintenance convenience and comprising an accelerometer, a charge amplifier, signal conditioning equipment, vibration signal analyzing equipment, a display terminal, vibration monitor software and the like. In the signal conditioning equipment,a signal conditioning module carries out programmable amplification and low-pass filter on vibration signals, and a multi-channel selection module receives control signals of a self-check control module of the vibration signal analyzing equipment so as to realize system programmable self check; the vibration signal analyzing equipment comprises a PXI bus crate; the vibration monitor software calculates vibration characteristic frequency and amplitude of each channel as well as a CPSD value, a coherence value and a phase value of each channel pair and monitors vibration acceleration and amplitude of a pressure vessel of the reactor, a first order modal frequency and amplitude and a second order modal frequency and amplitude of a reactor fuel assembly, frequency and amplitude of a core barrel beam mode and a second order shell mode as well as swinging frequency and amplitude of the pressure vessel, and the like.

The invention discloses a train rolling bearing fault and impurity vibration distinguishing method. The method includes: utilizing spectral kurtosis to perform filtering processing on an original bearing vibration signal, and adopting an empirical mode decomposition technique to perform step-by-step decomposition on the preprocessed vibration signal to obtain intrinsic mode functions; subjecting the intrinsic mode functions to frequency spectrum and statistic characteristic analysis, extracting a feature vector composed of frequency spectrum center-of-gravity and characteristic frequency energy ratio as input, establishing a support vector machine based classifier model by training, and distinguishing whether a rolling bearing with abnormal noise is faulted or contains impurities in real time. The method is used for distinguishing the fault or impurity-containing state of the train rolling bearing based on vibration signal analysis, the method effectively improves accuracy in bearing fault identification, the problem of overhigh false alarm rate in the bearing vibration diagnosis method caused by impurities is solved, and good technical support is provided for engineering application of the vibration diagnosis method.

The present invention relates to a high-voltage circuit breaker mechanical failure diagnosis method based on vibration signal analysis. The high-voltage circuit breaker mechanical failure diagnosis method based on the vibration signal analysis comprises the steps of measuring the time sequences and the time difference sequences of the occurrence moments of vibration events during the opening and closing processes of a circuit breaker under a normal working condition experiment and a typical fault experiment, and creating a time sequence feature library and a time difference sequence feature library; measuring a time sequence and a time difference sequence of the occurrence moments of the vibration events of an actually operated circuit breaker; comparing the time difference sequence of the occurrence moments of the vibration events during the opening and closing processes of the actually operated circuit breaker with the time difference sequences in the time difference sequence feature library to determine the types of faults; comparing a time sequence of the fault types under the faults of varying degrees in the time sequence feature library with the measured time sequence to determine the degrees of the faults. The method more visually solves the problems that the judgement of the conventional circuit breaker diagnosis method based on the vibration is too simple, and the accuracy is low, and can determine the degrees of the faults.

The invention discloses an asynchronous motor rotor broken bar fault judgment method with electric signals and vibration signals integrated, which belongs to the field of motor fault judgment. The method comprises the following steps: an acquisition instrument acquires voltage and current data through a current clamp and a voltage clamp, current spectrum analysis is carried out, if the current signals are normal, no broken bar fault happens to the motor rotor, and if the current signals are abnormal and when one side band appears at each position with a twice slip frequency difference from a power frequency, the next step is carried out; voltage spectrum analysis is carried out, if the voltage signals are normal, no broken bar fault happens to the motor rotor, if the voltage signals are abnormal and when one side band appears at each position with a twice slip frequency difference from the power frequency, vibration signal analysis is carried out, and whether broken bar fault happens to the motor is judged. In view of the problem that error judgment exists in a motor current signal analysis method in the asynchronous motor rotor broken bar fault in the prior art, a mode of sequentially judging the stator current, the stator voltage and the vibration signals is adopted, the rotor broken bar fault is detected, and the error judgment rate is reduced.

The invention relates to a solid rocket engine fault diagnosis method, and particularly relates to application of vibration signal analysis and a constant fault alarm detection algorithm in solid rocket engine fault diagnosis. The fault diagnosis method is based on a vibration signal analysis technology, and the constant fault alarm detection algorithm is applied to the feature extraction process to perform state identification on specific frequency points on the basis of a frequency fault diagnosis method so that sudden frequency signals submerged in clutter energy spectrum can be effectively detected and accurate frequency information can be obtained. The solid rocket engine fault diagnosis method has important meaning on engine fault decision and provides reliable reference and support for engine design personnel and test personnel.

The invention relates to a bearing fault diagnosis method under a strong noise variable speed condition based on an energy weight method. The method comprises the steps: extracting a vibration signalorder through employing a time-frequency ridge feature point linear interpolation and masking algorithm method according to a time-frequency representation graph based on Gabor transformation; performing instantaneous frequency estimation and secondary fitting on the vibration signal by using a local extremum search algorithm and the extracted order; carrying out equal-angle resampling on the vibration signal by utilizing a key phase time scale method according to the fitted instantaneous frequency; performing Hilbert-Huang transformation of CEEMDAN on the resampled isometric domain signal toobtain an order-frequency spectrum of the signal; extracting an impact energy occurrence position in the order-frequency spectrum, and then carrying out binaryzation on the order-frequency spectrum; acquiring an energy weight order sequence capable of reflecting impacts through multi-scale binary spectrum analysis, and carrying out power spectrum analysis on the energy weight order sequence to obtain fault-related impact components. The influence of strong noise and variable rotating speed on vibration signal analysis can be eliminated, and the accuracy of rolling bearing fault diagnosis is improved.

A device for recognizing bearing damage of a bearing (3), on which an object (4) which rotates at a rotational frequency is mounted, having at least one oscillation sensor (2) for converting an oscillation signal output by the bearing (3) into an electrical signal and having a calculation unit (8) for performing a first frequency transformation for multiple time windows of the oscillation signal to generate multiple time window spectra associated with the particular time windows and for performing a second frequency transformation for multiple frequency bands of the time window spectrograms to generate a multiband modulation spectrum, which, for modulation frequencies which are a function of the rotational frequency of the rotating object (4) because of bearing damage of the bearing (3), have signal amplitudes, the level thereof disclosing an extent of the bearing damage.

The invention relates to an intelligent detection system of special metallurgical vehicle wheel set bearings. The system comprises a rotating platform, a hydraulic pump, a touch display, a personal computer, a computer cabinet, a signal switchboard, a signal acquisition card, an 8-path relay output module, a vibration sensor, a rotating speed sensor, a hydraulic sensor, connecting wires for each part, a signal communication module, a driving control module, a data acquisition and processing module and a data warning and report generating module. A wheel set is placed on the rotating platform, the computer controls the wheel set to be lifted to a designated position, vibration acceleration sensors are placed at horizontal positions of bearing at both sides of the wheel set, and belt pulleys are driven by a motor to rub with the wheel set. When the rotating speed keeps constant, the wheels stay in the uniform rotation state, vibration signals are started to be collected, test data is stored after collection, and the wheel set is put back to the original position. The software can analyze whether the wheel set has bearing faults through the rotating speed, the current bearing model and the vibration signal. With the detection system, the accuracy rate and the real-time performance are effectively increased, the detection time is reduced, and the detection cost is reduced.

The invention relates to a high-pressure diaphragm pump check valve fault diagnosis method based on vibration signal analysis, and belongs to the field of mechanical fault diagnosis and signal processing. The high-pressure diaphragm pump check valve fault diagnosis method based on the vibration signal analysis comprises the following steps that firstly, VMD decomposition is carried out on a vibration signal of a check valve of a high-pressure diaphragm pump, and the number K of decomposition modes is determined through a center frequency to obtain K IMF components with physical significance; the MPE of the IMF components is then calculated to form a multi-scale complexity measure feature vector; and finally, a high-dimensional feature matrix is input into a classifier established by a support vector machine optimized based on a genetic algorithm to identify a working state of the check valve of the high-pressure diaphragm pump. According to the high-pressure diaphragm pump check valvefault diagnosis method based on the vibration signal analysis, the vibration signal of the check valve is denoised and decomposed into the IMF components without mode-mixing through a VMD algorithm, the multi-scale arrangement entropy of each IMF component is calculated to collect fault characteristic information distributed on multiple scales, the correct rate of fault identification of the checkvalve is improved, and higher practicability and engineering significance are achieved.

The invention discloses a fiber-optic gyroscope vibration signal analysis method based on improved LMD, and belongs to the technical field of inertial navigation. The method includes the steps that time-frequency domain decomposition is performed on a vibration signal through the improved LMD method, and a series of harmonic signals corresponding to different frequency bands in the original signal are obtained; finally, construction is performed by eliminating light source fluctuation noise and gyroscopic drift caused by vibration, and an output signal with vibration noise removed is obtained. According to the improved LMD method, cubic spline interpolation is adopted for replacing a moving average method, meanwhile a mirror extension method is adopted for relieving possible end effect problems, and OC judgment with a higher convergence speed is introduced into decomposition ending conditions. A kernel principal component analysis method is used for analyzing component signals, an energy ratio method is used for separating and reflecting effective signals and noise generated under vibration influences, and finally effectiveness analysis of the fiber-optic gyroscope vibration signal is completed.

The invention discloses a method for state monitoring and fault diagnosis of an intermittent large-scale low-speed and heavy-load production device, wherein the state monitoring and the fault diagnosis can not be effectively solved by utilizing the vibration signal analysis method, and the method is the technical method for carrying out the state monitoring and the fault analysis and diagnosis on the intermittent large-scale low-speed and heavy-load device through physical and chemical index detection, spectral analysis and ferrographic analysis of lubricating oil liquid (grease) used by the device. The invention provides an early wear fault diagnosis and maintenance scheme of the low-speed and heavy-load device, which can improve the accuracy of lubrication state monitoring of the low-speed and heavy-load device and the using management level of the lubricating oil (grease), save maintenance cost, prolong the service life of the device and create greater production value.

The invention discloses a vibration signal analysis method for a main bearing of a wind power transmission system based on improved HHT and fuzzy entropy. The method comprises the steps of collectinga vibration signal, performing EMD decomposition on the vibration signal, screening a real IMF component by a relative entropy theory, and performing Hilbert spectrum analysis to obtain marginal spectrum through synthesis. According to the method, the fault characteristics of the main bearing of the wind power transmission system can be stably and effectively extracted, and the interference of thenoise to a characteristic frequency can be effectively filtered. A false eigenmode function component generated by empirical mode decomposition is eliminated with proposed improved Hilbert-Huang transform, the marginal spectrum synthesized by the real component is made, and the true frequency distribution of the signal is reflected. Combined with the fuzzy entropy theory, an eigenmode function which best reflects fault bearing characteristics is screened out.

The invention discloses a bearing fault intelligent diagnosis method based on compressed sensing and a correlation vector machine. According to the method, fault diagnosis is achieved through vibration signal analysis. The method comprises the steps of firstly selecting a Gaussian random matrix as a measurement matrix based on a compressed sensing theory to realize compressed sampling of signals,secondly constructing an over-complete redundant dictionary to perform sparse representation on the signals, then utilizing an orthogonal matching pursuit algorithm to realize signal reconstruction, and selecting a time domain index sensitive to fault features as a feature vector for the reconstructed signals; and finally, selecting a Gaussian function as a kernel function, dividing a training sample and a test sample by utilizing a feature vector, importing the training sample into an intelligent recognizer of a relevance vector machine model constructed by a relevance vector machine, and comparing a test result with an actual fault type and degree to obtain the effectiveness of the diagnosis model. According to the invention, the problems of difficult transmission and processing of massdata and low signal-to-noise ratio of bearing vibration signals can be solved, and qualitative and quantitative identification of bearing faults can be realized more accurately.

The invention relates to a method for extracting fault features of a gantry crane lifting mechanism under the variable speed. The method comprises the steps of firstly collecting an original vibrationsignal of the gantry crane lifting mechanism under the variable speed, performing low-pass filtering and downsampling on the original vibration signal according to the speed of the lifting mechanism,then analyzing the undersampled vibration signal by using a time-frequency analysis method to obtain a time-frequency cloud map, extracting the instantaneous rotating frequency according to a frequency ridge extraction method, obtaining an estimated instantaneous rotating frequency curve through polynomial fitting, performing uniform angle sampling on the original vibration signal according to the estimated instantaneous rotating frequency curve, carrying out order ratio analysis by using the uniform angle sampled vibration signal to obtain an order ratio spectrum, and judging whether the gantry crane lifting mechanism breaks down or not according to the order ratio spectrum. The method solves a problem that it is difficult to extract fault features for the existing gantry crane lifting mechanism under the variable speed.

The invention provides a fault diagnosis system and method for a fuel delivery pump. The fault diagnosis system mainly comprises four piezoelectric acceleration transducers, five temperature sensors, a signal conditioner, a temperature measurement module, a data acquisition card and an industrial control computer. The four piezoelectric acceleration transducers are connected with the signal conditioner respectively. The signal conditioner, the data acquisition card and the industrial control computer are connected in sequence. The five temperature sensors are connected with the temperature measurement module respectively. The temperature measurement module, the data acquisition card and the industrial control computer are connected in sequence. The fault diagnosis method comprises the steps of (1) vibration signal acquisition, (2) signal conditioning, (3) data acquisition and (4) signal analysis. The fault diagnosis system can combine the virtual instrument technology, a traditional temperature signal analysis method, a traditional vibration signal analysis method and a modern vibration signal analysis method, thereby effectively improving efficiency and accuracy of fault diagnosis of the fuel delivery pump.

The invention discloses a vibration signal grading judgment recognition method. On account of defects of lack of combination and using of multiple analysis methods and aiming at improving vibration signal analysis precision, the invention provides the vibration signal multistage recognition method achieving improving identification precision by adopting different signal analysis indexes and analysis methods on different identification stage layers and combining amplitude analysis, energy analysis and frequency analysis into one. According to the method, a computing center executes the steps of de-noising processing, signal I level identification of an amplitude index, signal II level identification based on FFT transformation and signal III level identification based on STFT transformation after subtracting an amplitude signal with an initial time, and finally judges whether target vibration occurs or not. The invention further discloses a rockfall energy scale computing method and a rockfall risk early warning method. The vibration signal grading judgment recognition method, the rockfall energy scale computing method and the rockfall risk early warning method have the advantages of being reliable in theory, scientific and simple in computing process, simple in implement apparatus, convenient to install, high in measuring result precision, and especially suitable for the demand of the engineering field.

The invention relates to a fault diagnosis method for a gear of a drive system based on a vibration signal analysis. The fault diagnosis method comprises the following steps: (1) carrying out n orders of differential derivation on an original vibration signal x(t), wherein the formula is represented as the description; (2) carrying out first-order EMD (Empirical Mode Decomposition) on xn(t) to obtain a first IMF (Intrinsic Mode Functions), wherein the formula is represented as the description and rn1(t) is a residual component after the first-order EMD; (3) carrying out primary integration on IMFn1(t), wherein the formula is represented as the description; (4) carrying out the first-order EMD on b(n-1)1t to obtain a formula, wherein the formula is represented as the description and n is equal to n-1 if n-1 is more than 0; continually carrying out the steps (3) and (4); (5) if n-1 is equal to 0, carrying out DEMD (Differential Empirical Mode Decomposition) on the original vibration signal x(t) to obtain a first IMF1; and (6) enabling x2(t) to be equal to x(t)-IMF1 and carrying out the steps (1) to (5) to obtain a second IMF2; circulating the steps to obtain IMF1-IMF(i-1) after the x(t) is subjected to n orders of the DEMD, wherein xi(t) is the residual component.