33 results about "Multiscale entropy" patented technology

The invention provides a bearing fault diagnosis method and system device based on improved empirical wavelet transform. The method comprises: step one, collecting different fault bearing signals as analysis signals and converting a time domain waveform into a frequency domain waveform; step two, drawing an upper envelope of a frequency spectrum and transforming a frequency peak with a tight support into a flat top; step three, screening flat tops in the frequency domain based on criteria, removing meaningless flat tops, and keeping a main frequency; step four, using a minimum value between adjacent flat tops as the boundary of spectrum segmentation; step five, establishing wavelet filters respectively for segmented frequency spectrums and decomposing the signals into N mode components; step six, calculating similarity values between mode components and the original signals by using a cross-correlation coefficient and selecting a component with the highest similarity value; and step seven, taking a fault sample, calculating an IMF component with the largest correlation coefficient of the sample, calculating a multi-scale entropy of the IMF component, and drawing the multi-scale entropy curve of the sample to realize fault classification.

Major depression can affect multiple physiologic systems. Analysis of signals that reflect integrated function may be useful in probing dynamical changes in this syndrome. Complex variability can be used as a marker of healthy, adaptive control mechanisms and dynamical complexity decreases with aging and disease. The heart rate (HR) dynamics in non-medicated, young to middle-aged males during an acute major depressive episode exhibit lower complexity compared with healthy counterparts. By analyzing HR time series, a neuroautonomically regulated signal, during sleep, using the multiscale entropy method, a measure of complexity of HR dynamics can be determined. The complexity of the HR dynamics is significantly lower for depressed than for non-depressed subjects for the entire night and combined sleep stages 1 and 2, providing an indication of depression. These complexity signals, individually, or in combination with the complexity of other physiologic signals, can be used to define novel dynamical biomarkers of depression.

We disclose a system, an apparatus, and a method for high frequency temperature monitoring and analysis. According to a disclosed embodiment the system comprises: (a) a wireless temperature acquisition and logging device especially designed for multi-day, high-frequency, and high-resolution temperature sampling; and (b) an analysis system implemented in a digital computer with one or more processors in order to analyze and characterize said temperature using a plurality of methods including complexity analysis techniques such as Lempel-Ziv complexity, Approximate Entropy, Sample Entropy, Multiscale Entropy, and Detrended Fluctuation Analysis; and other statistical time-series analysis techniques. According to one embodiment the temperature monitoring system is designed to capture the dynamic aspects of temperature in order to enable researchers and clinicians to study temperature regulation, thermal physiology, and clinical thermometry.

In a subject undergoing therapeutic intervention, efficacy of the therapeutic intervention is assessed based on a series of physiologic data associated with the subject. The series of physiologic data is analyzed to produce a measure of complexity. The complexity measure is then compared to a control. The efficacy of the therapeutic intervention is assessed based on the comparison of the complexity measure to the control. The control may be, for example, a complexity measure taken prior to initiation of the therapeutic intervention, a complexity measure taken from a different subject, or a predetermined threshold value. The measure of complexity is generated using, for example, a multiscale entropy measurement (MSE), a time asymmetry measurement, and/or an information-based similarity measurement. An increase in complexity indicates a positive effect of the therapeutic intervention, while a decrease in complexity indicates a negative effect of the therapeutic intervention.

The invention discloses a sound signal noise reduction method and system of foreign matter in an electric energy meter on the basis of short-time energy, multi-scale entropy and EMD. The method includes the steps that noise data generated by shaking of the foreign matter in the electric energy meter is collected; foreign matter sound signal data is extracted through short-time energy positioning;the extracted foreign matter sound signal data is subjected to empirical mode decomposition (EMD), and the multi-scale entropy of each stage of intrinsic mode function (IMF) component obtained throughEMD is calculated; the multi-scale entropy of the IMF components is subjected to noise reduction and filtering to obtain sound signals, obtained after noise reduction and filtering, of the foreign matter in the electric energy meter. The method has the advantages that short-time energy positioning can be used for data extraction to increase the processing speed; the sound signals of the foreign matter in the electric energy meter are processed through multi-scale entropy and empirical mode decomposition, which is a self-adaptive process, and the defect that traditional spectrum subtraction noise reduction estimation is not accurate is overcome; meanwhile, introduction of new noise is avoided to the maximum degree, and the foreign matter in the electric energy meter can be accurately detected.

The invention provides a rolling bearing fault diagnosis method based on blind signal separation and a support vector machine, belongs to the technical field of intelligent fault diagnosis of rotatingmachinery, and aims to improve the precision, efficiency and robustness of rolling bearing fault diagnosis, and the implementation steps are as follows: obtaining a training sample set and a test sample set; obtaining a multi-dimensional feature vector set corresponding to the training sample set; obtaining a support vector machine set; performing iterative training on the support vector machineset; defining an observation matrix and a separation matrix; performing blind signal separation on the observation matrix; and obtaining a fault diagnosis result of the rolling bearing. According to the invention, the self-adaptive selection nonlinear function and the iteration step length are introduced into the iteration of the separation matrix, the multi-dimensional feature vector composed ofthe amplitude domain parameter, the frequency domain index and the multi-scale entropy is adopted when the feature vector of the vibration signal is extracted, and the diagnosis precision, efficiencyand robustness are effectively improved in combination with the support vector machine.

The invention discloses an industrial system fault detection method based on Euclidean distance multiscale fuzzy sample entropy. According to the method provided by the invention, the complexity of atime sequence can be described from a plurality of time scales; meanwhile, compared with an existing multiscale entropy method and an existing composite multiscale entropy (FME) method, the method hasthe advantages that the stability and the accuracy of the calculation of multi-scale fuzzy sample entropy (FME) are remarkably improved. The method can be used for judging and detecting the fault type of an industrial system and analyzing the complexity of the time sequence.

The invention discloses a quantitative determination method and a quantitative determination device for flow patterns of horizontal gas-liquid two-phase flows. The quantitative determination method comprises the steps of: acquiring two-phase flow liquid film thickness data in various flow patterns; carrying out signal processing analysis on the two-phase flow liquid film thickness data in various flow patterns according to a multiscale entropy (MSE) concept, so as to extract characteristics of different flow patterns; acquiring MSE arrangement distribution characteristic curves according to the extracted characteristics of different flow patterns, and calculating rates of MSE of different flow pattern curves of MSE arrangement distribution diagrams; and establishing a flow pattern database according to the two-phase flow liquid film thickness data in various flow patterns, the MSE arrangement distribution characteristic curves and the rates of MSE, so as to obtain a flow pattern identification quantitative determination form, and achieving the purpose of determination by means of the flow pattern identification quantitative determination form. The quantitative determination method can process two-phase flow liquid film thickness signals by adopting the MSE algorithm, achieve the purpose of flow pattern identification through extracting characteristic quantities, improve the applicability and reliability of determination, and improve the accuracy of determination.

The invention discloses a biological tissue terahertz imaging method, system and equipment based on multiscale entropy. According to the method, the information of whole terahertz time-domain spectroscopy signals is utilized, and the entropy value of each point on the detected biological tissue sample on different time scales is obtained; the entropy value on a certain scale is selected, or the entropy values on multiple scales are added together, so that a terahertz image of the detected biological tissue sample is reconstructed. By means of the method, the limitations that a traditional biological tissue terahertz imaging method only uses the spectroscopy information of a certain time or frequency point are overcome, and imaging contrast among different samples is improved.

The invention discloses a method for analyzing reservoir heterogeneity based on multi-scale entropy, and the method comprises the steps: collecting logging signals of a to-be-logged reservoir in different well sections, and obtaining logging curves of the to-be-logged reservoir in different well sections; performing normalization processing and coarse graining processing on the logging curve, and calculating corresponding sample entropy values of reservoir heterogeneity represented by the logging curve under different time scales; drawing a function diagram according to the relationship between the time scale and the corresponding sample entropy value, and constructing a multi-scale entropy curve of the reservoir of different well sections according to the function diagram; and comparing and analyzing the multi-scale entropy curves of different well sections to evaluate the reservoir heterogeneity strength of different well sections. A plurality of pieces of different information is obtained from logging signals according to different scales in a normalization and coarse graining processing mode, the information amount of to-be-processed data is increased in a multi-azimuth and multi-angle mode, meanwhile, a multi-scale entropy curve is constructed, all-directional comparison is achieved, and the accuracy and reliability of information analysis are improved.

The present invention provides a construction method for automatic sleep staging and use thereof. The construction method for automatic sleep staging comprises: acquiring a plurality of sets of PSG signals and manual sleep information of PSG signals; pre-analyzing to decompose the original time series in the PSG signals into a set of pseudo-intrinsic mode functions (pseudo-IMFs); assembling the pseudo-IMFs to obtain m sets of time series; analyzing by multiscale entropy (MSE), to calculate the entropy values of the m sets of time series on n coarse-graining timescales, thus obtaining an entropy matrix with m*n elements; establishing a correlation coefficient matrix between the levels of consciousness and the elements in the entropy matrix, and finding the coarse-graining timescale and filtering timescale corresponding to the most significantly positively correlated element or the most significantly negatively correlated element in the correlation coefficient matrix; and calculating the entropy value on the coarse-graining timescale and filtering timescale corresponding to the most significantly positively correlated element or the most significantly negatively correlated element, and assessing the sleep state according to the entropy value.

The invention provides a fault diagnosis optimization method, system and device for a numerical control machine tool under multiple working conditions. Through arrangement optimization of numerical control machine tool sensors, effectiveness of data acquisition and utilization is improved, an improved multi-scale entropy algorithm is utilized to extract characteristic information of different states represented by the numerical control machine tools of different time scales, deep-level characteristic information is mined, and differentiation of characteristics among different states is improved; on the basis, ITML-K mean value clustering is used for identifying the working condition of the numerical control machine tool so that a problem of a poor identification effect of a traditional clustering method under the condition of multi-working-condition boundary fuzziness is solved; and finally, an entropy-based regularization function is utilized to solve an overfitting problem occurring when a data-driven numerical control machine tool fault diagnosis model is constructed so that the generalization and accuracy of the numerical control machine tool fault diagnosis model are improved, and the optimization of the numerical control machine tool fault diagnosis model is realized. The method, the system and the device have important help for improving the operation safety and reliability of the numerical control machine tool and improving the fault diagnosis rate of the numerical control machine tool.