33results about How to "Enhanced fault signature" patented technology

A self-diagnosis method for rolling bearing cage faults, fast filtering of vibration signals through high-precision fast filter banks, and then selecting the optimal signal according to the spectral kurtosis of the mean square envelope autocorrelation signal of each filtered signal and calculating Its envelope spectrum, and then automatically select the first M-order actual fault characteristic frequency according to the theoretical fault characteristic frequency of the cage, and then automatically find out the threshold that meets the probability requirement according to the statistical characteristics of the spectrum, and finally calculate the cyclostationary global test index and fault occurrence rate, The self-diagnosis of the fault of the rolling bearing cage is realized, the invention provides a method for automatically diagnosing the fault of the rolling bearing cage, and provides an effective self-diagnosis tool for the intelligent bearing to analyze the fault of the rolling bearing cage.

The invention discloses a method for strengthening weak faults of rolling bearings based on matrix recovery, which belongs to the technical field of fault diagnosis of rotating machinery. By constructing the fault information matrix, the collected one-dimensional vibration signal is expressed in the form of a two-dimensional fault information matrix, so as to meet the input requirements of the matrix recovery theory, and use the matrix recovery algorithm to restore the shock characteristics from the two-dimensional fault information matrix. Based on the low-rank matrix, the cumulative average algorithm is used to restore the vibration signal without noise interference from the low-rank matrix. At the same time, considering the inevitable tail truncation phenomenon when constructing the fault information matrix, the positive sequence and reverse sequence fault information matrices are respectively constructed for the positive sequence and reverse sequence vibration signals, and the above three steps are carried out for the two fault information matrices respectively, and The denoising information obtained through the above two fault information matrices is synthesized to obtain a final denoising signal. The method is applicable to the analysis of vibration signals of rotating machinery in the field of fault diagnosis of rotating machinery.

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 discloses an improved keyless phase fault feature order extraction method, comprising the following steps: 1) Obtaining the state information of the equipment through a vibration acceleration sensor, and preprocessing the obtained state information by using a time-frequency analysis method to obtain the instantaneous frequency ; 2) Carry out conventional integral operation on the estimated instantaneous frequency to obtain a roughly estimated instantaneous phase, and then use the Romberg integral rule to correct the roughly estimated instantaneous phase, and finally obtain an accurate estimated instantaneous phase; 3) According to the time domain and The mapping relationship in the angle domain uses the accurately estimated instantaneous phase information to resample the original signal in the angle domain; 4) uses the flexible angle domain synchronous averaging method to perform noise reduction processing on the angle domain resampled signal, and then the angle domain after noise reduction The fault characteristic order of the equipment is extracted by performing order spectrum analysis on the domain resampled signal. This method can accurately extract the fault characteristic order of the equipment under the condition of keyless phase change speed.

The invention relates to the technical field of bearing fault diagnosis, and discloses an early diagnosis method for bearing faults based on adaptive frequency-shift variable-scale vibration resonance technology. In the method, the original vibration signal of the bearing is firstly extracted, and then the bearing vibration signal is processed by a signal processing function. Generate the superposition signal, and then use the quantum genetic algorithm to adjust the parameters of the bistable potential function for the purpose of improving the signal-to-noise ratio to optimize the superposition signal, so as to obtain a better output waveform. Beneficial effects: the method can use the collected signal to quickly optimize the parameters of the steady-state function through the quantum genetic algorithm, improve the signal-to-noise ratio of the output signal, thereby using the noise to enhance the characteristic signal of the bearing fault, and effectively detect the bearing fault, and the detection effect is better than the traditional one. The maximum correlation spectral kurtosis deconvolution method.

The invention relates to a distribution network single-phase grounding protection method based on active and passive joint detection of weak faults, and belongs to the technical field of electric power system relay protection. When the distribution network is running abnormally, use the bus zero-sequence voltage wavelet transform mutation energy in the low frequency band to judge whether the system has a single-phase ground fault, and start protection; if a fault occurs, the bus zero-sequence voltage when the neutral point is not regulated The zero-sequence current is sampled, decomposed by 6-layer wavelet, and the characteristic frequency band is determined by using the energy and maximum principle; the fault passive detection criterion is constructed after the wavelet coefficients under the characteristic frequency band are cross-overlapped and differentially transformed. According to whether the first non-zero value of the passive detection discriminant exceeds the threshold, it is judged whether a single-phase ground fault occurs in the feeder. The present invention utilizes whether the zero-sequence voltage can be continuously detected multiple times within a fixed time limit, can preliminarily judge the nature of the fault, and avoid repeated tripping to reduce power supply reliability.

The invention provides a rolling bearing slip rate evaluation method based on vibration information demodulation analysis, which uses a vibration sensor to collect high-frequency vibration signals, and simultaneously uses a key phase sensor to synchronously collect key phase signals, and obtains the packet of the original signal through Hilbert transform. The envelope demodulation signal, and then use the key phase information to perform linear equiangular interpolation on the envelope demodulation signal, obtain the envelope signal of the narrow-band impact by setting the narrow-band filter, and calculate its phase, use the phase calculation of the narrow-band impact envelope signal, etc. Using the equivalent reference shaft phase and speed, the envelope order spectrum analysis is performed again using the equivalent reference shaft phase, and the slip rate of the bearing is calculated using the equivalent reference shaft speed. The present invention can effectively suppress the order migration caused by slipping And fuzzy phenomenon, enhance the fault characteristics, and can estimate the bearing slip rate, and the evaluation accuracy is high.

The invention relates to a diagnosis method for impact type failure between a rolling bearing and a gear based on an optimal self-adaptive wavelet filter, which comprises the following steps: firstly establishing an exponentially damped sinusoidal impact type failure signal model, using a mode self-adaptive wavelet generation algorithm publicly disclosed by Hector Mesa to generate a self-adaptive wavelet filter which is matched with the signal model, then utilizing a fast FIR (finite impulse response) filtering algorithm to execute the wavelet filter to obtain the filtering result, then calculating the kurtosis value of the filtering result, and using an evolution differential algorithm to repeat the steps so as to finally obtain an optimized envelope spectrum. By adopting the method, impact failure characteristics in vibration signals can be precisely extracted, and a clearer envelope spectrum can be given out, thereby ensuring that failure symptoms can be displayed more clearly.

The application provides a rolling bearing fault diagnosis method, device, medium and computer equipment, including: intercepting the vibration signal of the rolling bearing to obtain an initial signal; creating an initial Hankel matrix based on the initial signal, and using a singular value decomposition algorithm to perform an initial Hankel matrix Reconstruct to obtain the reconstructed signal; use the 1.5-dimensional symmetric differential analytical energy operator demodulation algorithm to demodulate the reconstructed signal to obtain the demodulated signal; determine the 1.5-dimensional energy spectrum of the fault characteristic signal based on the demodulated signal; based on the 1.5-dimensional The energy spectrum is used to diagnose the fault of the bearing; in this way, the original vibration signal of the rolling bearing can be reconstructed by using the adaptive singular value decomposition algorithm, the background noise in the vibration signal can be removed, and the fault characteristics can be obtained; The sub-demodulation algorithm processes the fault characteristics, suppresses the residual noise, improves the fault characteristics, and ensures the accuracy of the fault diagnosis results.

The invention discloses a DC distribution network grounding fault identification method and system for detecting signals injected by MMC. With the help of an additional control strategy, the detection signal is injected into the feeder after the fault occurs, and according to the difference in the response of the faulty feeder and the healthy feeder to the detection signal Fault line identified. The active injection detection signal effectively enhances the fault characteristics, and the MMC-based injection method combined with the fault identification method avoids the installation of additional devices, and has the characteristics of high sensitivity and no need for double-terminal communication.