5 results about "Hilbert envelope" patented technology

The application belongs to the technical field of fault diagnosis, and particularly relates to a twin data multi-modal fusion transfer diagnosis method for gear fault, which comprises the following steps: obtaining three mode components of Hilbert envelope spectrum, autocorrelation time domain waveform and autocorrelation envelope spectrum of gear measured and simulated signals, and respectively constructing three source domain subsets and three target domain subsets; performing JMMD mapping alignment on the source domain subsets and the target domain subsets of the same mode; training three independent DBSCAN classifiers by using the JMMD mapping features of the three source domain subsets respectively, and performing pseudo-label labeling on the JMMD mapping features of the target domain same mode components; updating the target domain sample labels by using a Sugeno fuzzy integral decision fusion method; repeating the JMMD mapping and the Sugeno fuzzy integral decision fusion until the maximum iteration number is reached, and obtaining a fault recognition result; and the application is supported by simulation data, and can realize accurate recognition of gear fault without the guidance of measured label data, and has a good application prospect in the field of gear fault diagnosis.

This invention relates to a method for diagnosing composite faults in rolling bearings based on fast eigenmode decomposition (EMD) in the field of rolling bearing fault diagnosis technology. The method includes the following steps: S1, Signal initialization and period estimation: Segmented narrowband filtering of the rolling bearing vibration acceleration signal is performed using a Hanning window; S2, Adaptive filter design: The original signal is filtered using the MOMEDA method to obtain multiple modal signals; S3, Correlation kurtosis calculation and mode selection: The correlation kurtosis value of each filtered signal in each frequency band is calculated, and a cross-correlation matrix between modes is constructed based on cross-correlation theory, retaining a preset number of optimal modes; S4, Fault diagnosis analysis: Hilbert envelope demodulation is performed on the retained optimal mode signals. This invention solves the mode breakage problem that may occur in existing technologies while saving significant computational costs and improving computational efficiency, demonstrating good practicality and engineering application value.

The invention provides a flotation machine fault diagnosis method and system based on a periodic impact index. The method comprises the following steps of vibration signal preprocessing and Hilbert envelope demodulation, envelope spectrum local maximum value searching and sorting, periodic impact detection based on a frequency multiplication relation, periodic impact index calculation and fault judgment. According to the method, key information is extracted through vibration signal preprocessing and Hilbert envelope demodulation, periodic impact is detected based on a frequency multiplication relation through envelope spectrum local maximum search sorting, and finally a PII index is calculated to judge a fault. The method can accurately identify periodic impact components, does not need a large number of fault samples, can quantitatively evaluate the severity of faults, is high in anti-interference capability, can carry out real-time online diagnosis, effectively improves the fault diagnosis accuracy and engineering practicability of the flotation machine, reduces non-planned shutdown, and reduces the maintenance cost.

The present invention provides a method for detecting the mechanical wear status of a Roots-type compressor, comprising obtaining an original vibration signal transmitted from the Roots-type compressor; segmenting the original vibration signal into multiple short signals based on a preset adaptive window segmentation method; decomposing the multiple short signals based on a preset symplectic geometric mode decomposition method to obtain initial symplectic geometric components of each short signal; discarding noise components from the initial symplectic geometric components of each short signal based on a preset component selection criterion of cyclic kurtosis entropy, and further reconstructing the signal to obtain a de-noised signal; plotting the Hilbert envelope spectrum of the de-noised signal, and performing characteristic frequency analysis on the Hilbert envelope spectrum to detect the mechanical wear status of the Roots-type compressor. The present invention can not only extract multiple fault features, but also weak fault features, thereby improving the accuracy, reliability, and stability of mechanical wear status detection.

The present application relates to a kind of mechanical fault diagnosis systems based on minimum entropy deconvolution and stochastic resonance, comprising: the vibration data of mechanical rotating component is collected as original signal;After frequency scaling, the original signal is input into bistable stochastic resonance system, and correlation kurtosis is used as index function, system parameters are adaptively adjusted, so that the desired signal is output, the numerical solution of nonlinear system output is obtained using 4-order Runge-Kutta algorithm, the impact component in original signal is amplified using stochastic resonance phenomenon, and the signal after noise reduction is obtained;The signal after noise reduction is filtered again using minimum entropy deconvolution, and minimum entropy deconvolution filter signal is obtained;The signal after minimum entropy deconvolution filtering is subjected to hilbert envelope spectrum analysis, and the fault of mechanical rotating component is diagnosed.The present application can autonomously extract the frequency of fault signal by designing corresponding index under the condition that the fault frequency of unknown signal is unknown, and accurate fault diagnosis and positioning are realized.