10 results about "Hilbert envelope" patented technology

The invention discloses a rolling bearing fault time-varying instantaneous feature enhancement and extraction method. The method comprises the following steps: acquiring an original bearing fault signal; decomposing an original bearing fault signal based on an adaptive noise complete set empirical mode decomposition method to obtain a series of intrinsic mode function components; component properties are distinguished based on a correlation coefficient hopping criterion, and denoising is carried out; mapping the bearing fault signal after noise reduction to a time-frequency chirp rate space based on a local maximum frequency chirp rate synchronous compression chirp transformation method; and extracting a time-frequency chirp rate coefficient reflecting fault time-varying instantaneous characteristics from the extruded time-frequency chirp rate space, reconstructing a bearing fault impact component, performing Hilbert envelope analysis on the bearing fault impact component, and identifying the characteristic frequency of the bearing fault. According to the method, the fault time-varying instantaneous characteristics of the rolling bearing can be clearly and accurately extracted, and the method has a good engineering application prospect.

The invention belongs to the technical field of fault detection, and discloses a digital twin-assisted bearing weak fault diagnosis method, which comprises the following steps of: 1, establishing a dynamical model of three faults of a rolling bearing; and 2, setting parameters of the kinetic model according to an actual test bed, and updating according to actually acquired vibration data to obtain a simulated vibration signal. And step 3, performing sparse feature optimization by taking the simulated vibration signal as a sample, and obtaining each column of the weight matrix as a filter. And 4, filtering the original signal by using the optimized filter to obtain a filtered signal, and observing the fault characteristics of the filtered signal through a Hilbert envelope spectrum. According to the method, the influence of sample noise on the result in the sparse optimization process is reduced, and noise interference can be more effectively suppressed, so that fault features are enhanced.

The invention relates to a transformer fault detection method based on an antenna enhanced radio frequency identification sensor. The method comprises the steps of performing modal decomposition according to an initial vibration signal of a to-be-detected transformer to obtain a plurality of similar characteristic modes corresponding to the initial vibration signal, and performing Hilbert transform processing according to the similar characteristic modes to determine a Hilbert envelope spectrum of the initial vibration signal, and inputting the Hilbert envelope spectrum into a pre-trained sparse noise reduction auto-encoder SSDA model to obtain features of an initial vibration signal, performing fault classification on the to-be-detected transformer according to the features of the initial vibration signal, and determining a current fault type of the to-be-detected transformer. By adopting the method, whether the fault generated by the transformer to be detected is the known fault or not is not limited, and the accuracy and timeliness of transformer fault detection based on the antenna enhanced radio frequency identification sensor can be improved.

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 invention provides a power transmission tower bolt tightness detection method, a medium and equipment, and relates to the technical field of power transmission lines. Extracting characteristic parameters from the collected bolt vibration signal data by using a dynamic weighted average Hilbert envelope coefficient algorithm; constructing a Gaussian mixture model for bolt looseness detection, and training the Gaussian mixture model by using the feature parameters based on an entropy expectation maximization algorithm; bolt vibration signals collected in real time are obtained, and real-time characteristic parameters are extracted from bolt vibration signal data collected in real time through the dynamic weighted average Hilbert envelope coefficient; and inputting the real-time characteristic parameters into the trained Gaussian mixture model, outputting to obtain clustered characteristic model data, and judging whether the bolt is loosened or not and the loosening degree according to the aggregation degree of the characteristic model data. According to the method, the bolt fastening degree can be rapidly and effectively detected, and the working efficiency can be improved.

The invention discloses a vibration source distance estimation method and device based on a scorpion vibration source distance determination mechanism, comprising the steps of: using a single three-axis sensor to collect vibration data of a human body walking at different distances, preprocessing the vibration data, and obtaining vibration data corresponding to a single vibration event at different distances; performing Hilbert-Huang transform on the preprocessed vibration data to obtain a marginal spectrum and a Hilbert envelope curve of three-channel vibration data, extracting frequency domain features according to the marginal spectrum, extracting time domain features according to the Hilbert envelope curve, performing feature fusion, and constructing a distance feature database; and using a random forest regression algorithm to calculate a vibration source distance estimation value of a test sample according to the distance feature database. The invention can improve the accuracy of the existing vibration source positioning algorithm for vibration source distance estimation.

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.