134 results about "Power spectral analysis" patented technology

The invention discloses a water leakage detector, a water leakage detecting system and a water leakage detecting method. The water leakage detector comprises an acquisition device and a processing device, wherein the acquisition device is used for acquiring sound signals for preprocessing; and the processing device is used for performing Fourier transformation of the sound signals, performing frequency analysis and calculation, extracting features of the sound signals, performing power spectrum estimation for the sound signals to analyze frequency components of the signals, and judging whether leakage exists or not according to peak values and bandwidths of a power spectrum. The water leakage detector and the water leakage detecting system are simple and accurate in detection, and a water leakage fault can be recognized according to a pattern recognition method and by means of Fourier functional transformation and power spectrum analysis, so that maintainers can remove the fault as soon as possible and replace water pipes, waste is avoided, water supply quality is guaranteed, and economic loss is reduced. Besides, the problem of waste due to difficulty in water supply leakage detection is avoided, and the detecting method is accurate and convenient in control.

The invention provides a vibration error compensation method of a fiber optic gyro strap-down inertia measurement system. A vibration experiment is conducted on the fiber optic gyro strap-down inertia measurement system to collect the output data of an inertia measurement element accelerometer and a fiber optic gyro of the fiber optic gyro strap-down inertia measurement system; the installation error of the fiber optic gyro and the accelerometer is considered and is compensated to the output of the gyro and the accelerometer; and power spectral analysis is conducted on the output of the accelerometer after the compensation of installation error to obtain the vibration characteristics of a vibration signal; and an Elman neural network method is used to conduct non-linear compensation to the vibration error output of the fiber optic gyro strap-down inertia measurement system. In the situation of environmental vibration, the method uses a reasonable neural network model to compensate the vibration error and can effectively reduce the effect of the environmental vibration on the precision of the system and maintain favorable precision.

A lithology while drilling and reservoir characteristics recognizing method comprises the following steps: (1), establishing data analysis software and hardware equipment; (2), mounting vibration signal measuring equipment; (3), transmitting vibration acoustic wave signals by a vibration transducer to an analog filter; (4), filtering useless frequency bands in the signals and transmitting useful frequency bands to a high fidelity amplifier by the analog filter; (5), amplifying, strengthening, and transmitting the signals to a digital filter by the high fidelity amplifier; (6), transmitting power spectral density of the signals to a wavelet transformation unit by a power spectrum analysis unit, (7), performing wavelet decomposition, reconstruction and noise elimination on the signals by the wavelet transformation unit; and (8), unfolding restored true signals in spectrographs and energy spectrum charts by a Fourier transformation unit, and determining multiple kinds of lithology and reservoir fluid characteristics by recognizing each group of spectrum lines. The method detects and filters the vibration acoustic wave signals, extracts stratum information, and obtains the lithology and the reservoir fluid characteristics through noise elimination and spectral analysis.

The invention discloses a test system and method for mode and vibration of a self-supporting tower and a large-span power transmission tower. The test system is characterized by comprising a sensor, wherein the sensor is connected with a signal conditioner; the signal conditioner is connected with a digital signal collecting box; a digital signal collector is connected with a computer; the digital signal collector is provided with eight channels; and an auto-power spectrum analysis module, a cross-power spectrum analysis module and an environmental motivation mode analysis module are arranged in the computer. On the basis of an environmental motivation method, a method for fixing a reference point and moving a measured point is utilized to test mode parameters and vibration of a soaring power transmission tower. Besides, corresponding hardware equipment and software programs are utilized to ensure that the environmental motivation method can be used for performing off-line mode test and analysis for a power transmission tower model.

The invention provides a blade imbalance on-line fault diagnosis method of a doubly-fed wind generator. The method comprises the steps that the rotating speed of the doubly-fed wind generator and the power signal P output by the generator are synchronously sampled; the input pneumatic torque T of a fan hub is estimated according to the rotational inertia J of the fan hub and the generator output power and the rotating speed which are synchronous sampled in the sampling period; order analysis is conducted to obtain a resampled pneumatic torque Ta, and the frequency fuzzy phenomenon caused by rotating speed variation is removed; power spectrum analysis is conducted on the signals after order analysis and reconstitution, and feature extraction is conducted; a frequency doubling power spectrum amplitude Q(1) and a frequency tripling power spectrum amplitude Q(3) are compared, and if Q(1) is larger than Q(3), it is judged that an imbalance fault happen. According to the blade imbalance on-line fault diagnosis method of the doubly-fed wind generator, the blade imbalance fault of the doubly-fed wind generator is judged according to the synchronously-sampled signals of the rotating speed, the voltages and the currents of the doubly-fed wind generator, and safe and normal working of a wind generating set is guaranteed. A used sensor is simple, convenient to mount and very suitable for field service.

The invention relates to a frequency chosen of singular-spectrum analysis-based magnetic resonance sounding signal extraction method. According to the method, a nuclear magnetic resonance sounding water detector is used to collect MRS signals of a Larmor frequency-known region; a broadband band-pass filter is adopted to suppress partial noises, and thereafter, the position of MRS signals corresponding to the Larmor frequency are found on a power spectrum based on power spectrum analysis; and frequency chosen of singular-spectrum analysis is performed, so that the MRS signals are extracted. Thefrequency chosen of singular-spectrum analysis includes the following four steps that: embedding is performed; RSVD (Regularized Singular Value Decomposition) is performed; and corresponding singularvalues are selected according to the amplitudes of the MRS signals so as to perform matrix reconstruction; and diagonal averaging is performed. With the method of the invention adopted, random noises, spike noises and power frequency harmonic interference can be effectively filtered from noise-containing MRS signals; and the effective extraction of the MRS signals under a complex strong noise interference condition can be realized. Compared with a traditional MRS signal de-noising method, the method has the advantages of high operation speed, high signal-to-noise ratio, high practicality andthe like.

The invention provides a pipeline leakage diagnosis method, which includes the following steps: mounting dynamic pressure transmitters or acoustic wave leakage monitors at head and tail stations of the pipeline respectively, so as to continuously monitor a dynamic pressure signal or an acoustic wave signal in the pipeline in real time; denoising the data of the corresponding head and tail stations and calculating the signal average, so as to turn the signals into positive and negative signals; establishing two equal length moving windows arranged at a preset interval for the data, and enabling the moving windows to move with preset step lengths, so as to cut out the two equal length time domain signals, performing power spectrum analysis on the two signals respectively to compare the two signals and calculate the corresponding characteristic indexes; detecting the threshold value of sensitivity according to the set corresponding leakage, and setting to be 1 or zero clearing the leakage diagnosis mark of the upper and lower streams; and performing the leakage diagnosis according to the leakage diagnosis mark of the upper and lower streams. The pipeline leakage diagnosis method can effectively reduce mistaken reporting and completely avoid missing reporting.

The invention discloses a method for determining a load spectrum of an auxiliary frame of a mixing transport truck. The method includes the steps that firstly, according to finite element analysis and a fatigue damage position of the auxiliary frame of the mixing transport truck in the actual use, a detecting position is determined, and static and road tests are carried out on the load spectrum; secondly, by combining speed and acceleration signals, power spectrum analysis and filtering processing are carried out on time domain data, singular points and the null shift are detected out and removed, and complete and reliable test data are obtained; thirdly, according to the actual working conditions of the mixing transport truck, combined work conditions of 'rapid feeding, full load transportation and rapid discharging' are adopted, and the load spectrum containing a set of feeding work condition data, one set of road test data (containing three road conditions) and one set of discharging work condition data is determined by combining the actual road test data. The method is simple, accurate and practical when used for testing and compiling the load spectrum of the auxiliary frame of the mixing transport truck, and provides a way and a data foundation for forecasting the fatigue life.

The invention discloses a fuzzy-intelligence-based rail traffic car suspension system fault analysis method. The method comprises the steps that 1, a rail traffic car suspension system model is constructed, and dynamic characteristic analysis is performed on the model; 2, according to the dynamic characteristic analysis result of the rail traffic car suspension system model, an acceleration sensor is arranged; 3, multiple data time domain and frequency domain characteristics collected by the acceleration sensor are extracted, and distance characteristics are extracted through power spectrum analysis; 4, dimension reduction processing is performed on an original characteristic sample in the step 3, and a fault characteristic sample is obtained; 5, on the basis of the fault characteristic sample, fuzzy intelligence is utilized for performing fault classification on the car suspension system. According to the scheme, the defect that time frequency domain characteristic indexes describe signal changes from a certain aspect of a time domain or a frequency domain is overcome, meanwhile, meanwhile, the defects that time frequency domain characteristic indexes are easily added and average calculating operation submerge difference characteristics are obtained are overcome, and the characteristic sample quality is improved.

The present invention relates to a cascade empirical mode decomposition-based gear fault diagnosis method. The cascade empirical mode decomposition-based gear fault diagnosis method comprises the steps of using a vibration sensor to monitor the gear fault working condition, and acquiring a gear fault working condition vibration signal; carrying out the fault signal local characteristic extraction on the acquired vibration signal via a multilevel empirical mode decomposition method, and obtaining an intrinsic mode function; carrying out the power spectrum analysis on the obtained intrinsic mode function, extracting the mixed single frequency information of an intrinsic mode function component IMF, and determining whether the mixed single frequency information generates a mode aliasing state; adding an auxiliary signal in an obtained aliasing mode signal IMFj to obtain a new mixed signal; initializing the obtained mixed signal, circulating until all decomposed intrinsic mode functions have no mode aliasing phenomenon; carrying out the power spectrum analysis on the finally obtained intrinsic mode function having no mode aliasing phenomenon to obtain a fault characteristic frequency. The cascade empirical mode decomposition-based gear fault diagnosis method of the present invention can diagnose the gear faults timely and accurately, and avoids the problem that the fault characteristics are difficult to identify.

Provided is a method for evaluating an R value and power spectrums of electroencephalogram signals causing discomfort when people watch three-dimensional (3D) images. The method comprises arranging an experimental environment and experimental conditions, arranging experimental sequence of a subject, explaining the experiment process, arranging an electroencephalogram electrical electrode, collecting electroencephalogram signals of the subject before watching images for 2 minutes, and performing a first questionnaire survey; collecting electroencephalogram signals when the subject watches the whole movie and performing a second questionnaire survey after the movie is over; collecting electroencephalogram signals for 5 minutes when the subject rests for 10-20 minutes after watching the movie and performing a third questionnaire survey; preprocessing the collected electroencephalogram signals; analyzing the power spectrums in a plurality of wave bands according to the frequency range obtained by preprocessing the electroencephalogram signals; and analyzing the R value and performing statistics t-inspection under 2D experimental conditions and 3D experimental conditions. The method can perform comparison of 2D images and the 3D images, analyzes centralized brain regions of the electroencephalogram signals from the energy angle, provides an experimental basis for follow-up researches, and then provides thoughts for technical standards for evaluating the 3D images.

The invention discloses a bituminous pavement surface structure 2D test evaluating system and method. The system comprises an image acquiring module, an image processing module, a surface structure line acquiring module, a surface structure line processing module, and an analyzing and computing module. The method comprises the following steps of: 1, making a bituminous mixture test piece; 2, acquiring a profile scanning image of the bituminous mixture test piece; 3, preprocessing the profile scanning image to obtain a profile scanning grey-scale map; 4, acquiring the surface structure line of the profile scanning grey-scale map; 5, performing bad point deletion, anti-confusion filtering, window function addition, and DFT on the surface structure line to obtain the constant-bandwidth narrow spectrum of the surface structure line; and 6, analyzing the power spectrum of the constant-bandwidth narrow spectrum of the surface structure line and computing an evaluation index octave index L<TX, I j> for the bituminous pavement surface structure. The system and the method are high in testing precision, simple in operation, low in cost, and easy to popularize.

The invention discloses a power load forecasting optimization method based on continuous power spectrum analysis. The continuous power spectrum analysis method is used to extract the hidden significant periodic sequence in the power load time series and separate to obtain the residual sequence. The optimization method is based on particle swarm optimization. The BP neural network predicts the significant periodic sequence and obtains the prediction results of each significant periodic sequence; the RBF neural network optimized by the particle swarm optimization algorithm is used to predict the first-order difference sequence of the residual sequence, and then the residual sequence is obtained by differential inverse operation Finally, the average value of the average power load time series is added to the prediction results of each significant period sequence and the prediction result of the residual sequence to obtain the final prediction result. Aiming at the periodic characteristics of electric load data, the present invention establishes a forecasting model, which can greatly improve the accuracy of short-term electric load forecasting.

A visualization method for screening electronic devices is provided. In accordance with the disclosed method, a probe is applied to a grid of multiple points on the circuit, and an output produced by the circuit in response to the stimulus waveform is monitored for each of multiple grid points where the probe is applied. A power spectrum analysis (PSA) produces a power spectrum amplitude, in each of one or more frequency bins, on the monitored output for each of the multiple grid points. The PSA provides a respective pixel value for each of the multiple grid points. An image is displayed, in which image portions representing the multiple grid points are displayed with the respective pixel values.

Non-intrusive assessment of fatigue in drivers using eye tracking. In a simulated driving experiment, vigilance was assessed by power spectral analysis of multichannel electroencephalogram (EEG) signals, recorded simultaneously, and binary labels of alert and drowsy (baseline) were generated for each epoch of the eye tracking data. A classifier and a non-linear support vector machine were employed for vigilance assessment. Evaluation results revealed a high accuracy of 88% for the RF classifier, which significantly outperformed the SVM with 81% accuracy (p<0.001). In a simulated driving experiment, the simultaneously recorded multichannel electroencephalogram (EEG) signals were used as the baseline. A random forest (RF) and a non-linear support vector machine (SVM) were employed for binary classification of the state of vigilance. Different lengths of eye tracking epoch were selected for feature extraction, and the performance of each classifier was investigated for every epoch length. Results revealed a high accuracy for the RF classifier in the range of 88.37%-91.18% across all epoch lengths, outperforming the SVM with 77.12%-82.62% accuracy. A feature analysis approach was presented and top eye tracking features for drowsiness detection were identified. A high correspondence was identified between the extracted eye tracking features and EEG as a physiological measure of vigilance and verified the potential of these features along with a proper classification technique, such as the RF, for non-intrusive long-term assessment of drowsiness in drivers.

The invention discloses a fault diagnosis method for an AC/DC charging equipment power device based on wavelet packet analysis, and the method comprises the steps: analyzing a charging equipment powermodule, and determining a fault class; obtaining output signals of the charging equipment power module under the normal condition and various fault conditions; carrying out the high-low frequency decomposition of the output signals, carrying out the multilayer wavelet packet decomposition of the high-low frequency signals obtained through decomposition, and carrying out the reconstruction of a wavelet packet decomposition coefficient; calculating the extracted signal energy at all frequency bands, carrying out the normalized calculation of the signal energy, and finally determining a centralized frequency band range of the signal energy; carrying out the power spectrum analysis of the reconstructed wavelet packet decomposition coefficient, determining the feature frequency and power spectrum value of a frequency band signal, comparing the comparison with the fault analysis, and determining the type of a fault; and carrying out the fault type coding of the type of the fault. The methodimproves the time-frequency resolution of a signal, achieves the precise fault positioning through the spectrum features, and improves the precision of the fault diagnosis of the AC/DC charging equipment power device.

The invention relates to the technical field of biometric features identification, in particular to a pulse wave identification device and method based on power spectrum analysis. The device comprises a fingertip pulse detector, a signal processing unit, a Bluetooth transmitter module, a Bluetooth receiver module and an upper computer. The fingertip pulse wave detector comprises a pulse wave sensor and a preamplifier, wherein the pulse wave sensor is used for acquiring fingertip pulse wave. The output end of the pulse wave sensor is connected with the input end of the preamplifier. The input end of the signal processing unit is connected with the output end of the preamplifier. The signal processing unit is wirelessly communicated with the upper computer through the Bluetooth transmitter module and the Bluetooth receiver module. According to the method, pulse wave serves as a feature extraction classification object, and different objects are identified on the basis of pulse signal processing; identities of people of different features are distinguished by maximum entropy power spectrum estimation, and the pulse wave identification device and a fingerprint acquisition system can be combined to develop a comprehensive identification system.

The invention relates to a composite fault feature extraction method for a gear system, and a fault test device, and belongs to the field of mechanical equipment fault diagnosis. A gear system vibration signal f in a time domain is acquired through an acceleration sensor arranged on the surface of a gear box body, and an adaptive optimization-based variable mode decomposition method is used to decompose the acquired gear system vibration signal f; and envelope power spectrum analysis is performed on all sub-signals obtained after decomposition, all the sub-signals are converted from the time domain to a frequency domain, and a frequency distribution condition of all the sub-signals on an envelope power spectrum is obtained, so that the extraction of fault features is realized. The defect that an existing VMD method is unlikely to determine optimal parameters is overcome; the composite fault features of the gear system can be more effectively extracted; the economic cost and the time cost of a test can be greatly reduced; and an alternative test condition is provided for fault diagnosis of the gear system under the condition that the industrial field actual measurement is not conveniently carried out.

The invention belongs to the technical field of test and measurement. A turning fork resonance frequency quick measuring method based on a virtual instrument is characterized in that the tuning fork starts oscillation with colored-noise signal excitation, collects signals of an output end of the tuning fork, and then carries out filtration and spectral analysis on the collected signals. The resonance frequency of the tuning fork can be quickly measured by spectral analysis. A measurement system comprises a general-purpose computer, intelligent measuring software, and a special sound card. Theintelligent measuring software generates the colored-noise signals with appointed bandwidth and value of amplitude by driving the special sound card; the colored-noise signals are used as the excitation signals of the input end of the tuning fork; and then the signals of the output end of the tuning fork is obtained by the collection of the special sound card. By the power analysis, the frequencywhich a spectrum peak corresponds to is the resonance frequency of the tuning fork. The method is based on the general purpose computer, uses the sound card as the data acquisition card, and utilizesa LabVIEW virtual instrument platform technology to compile the intelligent measuring software.