404 results about "Stochastic resonance" patented technology

The invention discloses a bearing fault detection method based on a manner of controlling stochastic resonance by an external periodic signal. According to the method provided in the invention, after a bearing fault signal is converted by a variable metric method, the converted signal is input in a bistable system; meanwhile, an external single frequency periodic signal is taken as a control signal to act directly on the system; contact barrier height of the bistable system and an escape rate of Kramers are changed by continuously adjusting an amplitude of the control signal. Therefore, stochastic resonance can be generated or increased artificially; a spectral value of an output power spectrum at the position of an input signal frequency can be effectively improved; and thus a characteristic signal of a bearing fault can be detected accurately at last. The detection method provided in the invention enables the effective control of the stochastic resonance to be realized, thereby providing a novel method for early detection of equipment faults.

The invention discloses a self-adapting random resonance weak signal detection method, which achieves the self-adapting detection of weak periodic signal through combining a matching random resonance and an imposed signal induced random resonance. According to the theory of random resonance, firstly, numerical simulation is processed to receive correspondence between the noise intensity and the frequency of the best matching random resonance of a nonlinear bistable system in a range of extremely low frequency and under the condition of small-signal; and a high-frequency weak signal encountered in a project can be processed by using the change of normalized scale. For the detection of a weak signal with extremely low message-to-noise ratio and large noise intensity, the heights of voltage barriers of the bistable system can be changed by using the induced random resonance, thereby helping a weak eigenfrequency signal to fulfill the transition between voltage barriers, so that the bistable system reaches the random resonance state, thereby achieving the self-adapting detection of the weak periodic signal.

The invention discloses a method for extracting weak fault characteristic information of a planetary gear based on improved ensemble average empirical mode decomposition and adaptive stochastic resonance. The method comprises vibration signal acquisition, fault characteristic information effective IMF extraction and weak fault characteristic extraction based on adaptive stochastic resonance. The method can extract the weak fault characteristic information generated due to faults of the planetary gear, and can realize planetary gear fault diagnosis according to the extracted weak fault characteristic information; and the method is an effective planetary gear weak fault characteristic information extraction method.

The invention relates to a self-adaption stochastic resonance weak signal detecting method based on a particle swarm optimization algorithm. The method comprises the following steps of 1) particle swarm initialization; 2) step-changed stochastic resonance; 3) individual fitness evaluation; 4) particle speed and position updating; 5) termination condition judgment and 6) detection result output. The self-adaption stochastic resonance weak signal detecting method has the advantages that the simplicity is realized, the implementation is easy, the application range is wide, the convergence speed is high, high-frequency weak signals at high-noise background can be effectively detected, and a novel method is provided for stochastic resonance parameter self-adaption selection and practical application in engineering.

The invention discloses a weak signal extracting method based on self-adaptive stochastic resonance. Particularly, a frequency of a weak signal can be well adjusted to a frequency range easily generating the self-adaptive stochastic resonance through adjusting a scale changing factor which is secondarily sampled, so that the good performance of the self-adaptive stochastic resonance can be sufficiently utilized, the weak signal can be well extracted at a super-low signal to noise ratio, and the problem that the conventional weak signal processing method represents to be poor, even be invalid, at the super-low signal to noise ratio can be effectively solved; and meanwhile, a signal frequency of the weak signal which is secondarily sampled can be matched with a self-adaptive stochastic resonance system and can generate stochastic resonance through a fed-back automatic adjusting scale conversion factor under the condition of not knowing the frequency of the weak signal, so that the characteristic of the weak signal can be better extracted.

The invention discloses a GNSS carrier loop circuit tracking method based on a stochastic resonance algorithm. According to the invention, the signal to noise ratio of receiving signals is increased by integral accumulation and multiplication feedback treatment on input signals; rough tracking of frequency of the receiving signals is performed by utilizing a second-order frequency locking loop; and precise tracking is performed on the signals subjected to rough tracking by adopting a third-order phase locking loop. Therefore, precise carrier loop circuit tracking on the GNSS signals is realized in a low carrier-to-noise ratio. According to the invention, an advantage of the stochastic resonance algorithm in an aspect of improving the signal to noise ratio and an advantage of anti-interference and robustness of the cooperation of the second order frequency locking loop and the third-order phase locking loop are integrated effectively. The method is simple in hardware implementation and stable in signal tracking, and can realize stable and reliable tracking of highly dynamic satellite signals by a receiver in a severe environment.

The invention relates to a vibration fault diagnosis method of a hydroelectric generating set. The method comprises the steps of: firstly, carrying out denoising processing on collected original vibration signals by utilizing a random resonance technology; secondly, carrying out characteristic vector extraction on the vibration signals after the denoising by utilizing a multi-dimensional permutation entropy technology; thirdly, establishing fault diagnosis models of optimized support vector machine based on an improved particle swarm algorithm; fourthly, inputting extracted characteristic vectors into the models of the models of optimized support vector machine based on the improved particle swarm algorithm for fault diagnosis. The method is applicable to the vibration fault diagnosis of the hydroelectric generating set, the diagnosis result is high in precision, the fault type of the generating set can be relatively accurately diagnosed, the reliable diagnosis result is provided to operation maintenance personnel of the generation set, the maintenance personnel can process faults timely and rapidly, and the safety and the economic performance of the operation of the generating set are ensured.

The invention discloses a method for extracting feature information of weak faults of low-speed heavy-duty equipment, which can be used for extracting signals of the weak faults of the equipment. Themethod comprises the following steps: acquiring vibration signals under the working state of the equipment through an acceleration sensor arranged on a bearing seat of the equipment; carrying out wavelet decomposition on the acquired vibration signals; performing stochastic resonance processing and mutual correlation processing for low-frequency signals obtained by the decomposition; and carryingout Fourier transform on the mutual correlation result to extract the feature of the weak faults of the equipment. The method can effectively extract the feature information of the weak faults of thelow-speed heavy-duty equipment, and provides effective basis for the fault diagnosis of the equipment.

The invention relates to a three-dimensional fault line selection method based on random resonance and a transient current signal. The method comprises the following steps of: (1) after a power distribution network is faulted, recording the zero-sequence current iZn(t) and the fault phase current iXn(t) of each branch line fault, and computing transient zero-sequence current izn(t) and transient fault phase current ixn(t); (2) performing variable-scale bistable treatment on the transient zero-sequence current izn(t) and transient fault phase current ixn(t); (3) computing a cross correlation coefficient matrix M and an integrated correlation coefficient Mn among the transient characteristic zero-sequence current iczn(t) of the branch lines and selecting the line ln with the minimum Mn; (4) performing seven-layer db10 wavelet packet decomposition on the transient characteristic phase current icxn(t) of each branch line and computing simplified energy E*sn and simplified gravity center frequency f*gn; and (5) forming a three-dimensional coordinate system by using the simplified gravity center frequency f*gn as a horizontal ordinate, the simplified energy E*sn as a vertical coordinate, and a polarity parameter value as a longitudinal coordinate. Compared with a method in the prior art, the method is high in reliability and is not liable to suffer from external interference.

Apparatus and method for improving the detection of signals obscured by noise using stochastic resonance noise. The method determines the stochastic resonance noise probability density function in non-linear processing applications that is added to the observed data for optimal detection with no increase in probability of false alarm. The present invention has radar, sonar, signal processing (audio, image and video), communications, geophysical, environmental, and biomedical applications.

Provided is a steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement. First, an electrode is arranged on the head of a user, a detected electroencephalogram signal is sent to a computer, then movement stimulating units are presented in front of the user through a computer screen at the same time, the movement stimulating units are shielded by two-dimensional noise points obeying Gaussian distribution, the noise point update frequency is synchronized with the screen refresh rate, and the noise strength is represented by Gaussian distribution standard deviation. After the movement stimulating units are formed, the user watches any one of the movement stimulating units, a computer acquires a stimulation starting flag bit and a stimulation finishing flag bit, a computer signal is acquired through a test electrode, the significance probabilities of different stimulation targets are calculated, the targets are judged and indicated through the screen, and then the next target identification is conducted. The steady motion visual evoked potential brain computer interface method based on stochastic resonance enhancement can remarkably enhance brain response strength of the user, improve precision and efficiency of an existing brain computer interface, and innovatively improve practical level of the brain computer interface technology.

The invention discloses an accurate detection method for the voltage travelling wave of a transmission line fault based on waveform inversion. According to the method, special voltage travelling wave sensors are mounted on grounding wires of capacitive equipment, as capacitive voltage transformers (CVT), of transformer stations at the two ends of a power transmission line; the special voltage travelling wave sensors are utilized to detect a primary voltage traveling wave signal after the transmission line fault and output a secondary voltage traveling wave signal; and a high-frequency transfer function model of the special voltage travelling wave sensors is used to establish a high-frequency inversion function model of the special voltage travelling wave sensors, according to the high-frequency inversion function model, a random resonance-deconvolution inversion algorithm is designed for waveform inversion, the primary voltage traveling wave signal is output and inversed, and accurate inversion from the second voltage traveling wave signal to the primary voltage traveling wave signal is realized. According to the invention, the problem of waveform distortion generated when the primary voltage traveling wave signals is transformed by the special voltage travelling wave sensors, and the primary voltage traveling wave signal can be detected accurately and effectively.

Nonlinear target detection for the detection of extremely weak signals when buried in very high levels of noise is performed with improvement of the quality of the signals received in the data by the amplification of the signal to noise (S/N) ratio. The S/N ratio increases dramatically after the originally measured signal is passed through a nonlinear stochastic resonance filter.

A navigation-body weak radiation noise signal detection method under a marine environment relates to navigation-body radiation noise signal detection. Euler domain random resonance and Euler domain mixing random resonance models are established. A signal submerged by a strong background noise is processed in a stable state system of an Euler space. The signal is mapped to a period index form. A stable state model possesses a multi-resonant characteristic. Limitations of an approximation heat insulation theorem of a traditional random resonance theory and residence time are broken through, a radiation noise signal characteristic can be highlighted and a signal to noise ratio is increased. An Euler domain non-linear random resonance and mixing random resonance system is used to detect a period signal and a pulse signal radiated by an underwater navigation body. Underwater weak signal detection performance is improved and an output signal to noise ratio is effectively increased. A target signal is detected in a distance range which is greater than 500m. Under a co-existence condition of a low signal to noise ratio of minus 5dB and various kinds of other water sound interferences, existence of a target is detected. Sparse characteristic extraction and degradation treatment of a data amount are performed, and a calculating speed is increased.

The invention relates to a method for increasing detection probability of frequency spectrum perception in the technical field of wireless communication. The method comprises the following steps: sampling received signals, and accumulating by using a square-law device to obtain a signal energy Tx within the detection frequency band; when Tx is less than or equal to the threshold gamma 1, the user transmits information which indicates that the master user signal does not exist to a merging center; when Tx is greater than or equal to the threshold gamma 2, the user transmits information which indicates that the master user signal exists to the merging center; or else, carrying out random resonance, and processing the state variable output sequence of the random resonance system by using the previous method until the cognitive user participating in the cooperative frequency spectrum perception transmits the detection result information which indicates that the master user information exists or does not exist to the merging center; and the merging center carrying out merging to obtain the final detection result information of the master user signal. The invention effectively increases the detection probability, has the advantages of low computation complexity, good robustness and favorable feasibility and practicality, is not susceptible to environment, and overcomes the problem of noise walls.

The invention discloses a weak fault parallel-connected random resonance detection method of a low-speed heave-load device for detecting weak fault signals of a device. The method comprises extracting a device vibration signal through a vibration sensor mounted on a position to be detected, amplifying and filtering the signals, and converting analogue signals into digital signals through an analog-digital converter, then processing by a parallel-connected random resonance method for output. The fault type is determined by extracting the weak characteristics of fault signals from the waveform of the output signal and the spectrogram thereof. The method combines random resonance method and self-correlation processing method, can effectively extract early weak fault characteristic signals in the low-speed heavy-load device so as to find fault hidden dangers ahead of time.

The invention relates to the field of early fault diagnosis of rotating machineries, and discloses a self-adaptive stochastic resonance early fault diagnosis method based on a grey wolf optimization algorithm. The self-adaptive stochastic resonance early fault diagnosis method improves the weak signal detection ability of a stochastic resonance method, and realizes precise diagnosis of machinery early faults. The self-adaptive stochastic resonance early fault diagnosis method introduces the grey wolf optimization algorithm into a bistable state stochastic resonance method, optimizes stochastic resonance structure parameters, selects the optimal structure parameter adaptively according to input signal features, realizes the optimal stochastic resonance output, and further achieves precise extraction of weak faults and accurate fault recognition. The self-adaptive stochastic resonance early fault diagnosis method is suitable for early fault diagnosis of rotating machinery.

A quantum interface control system for manipulating, evaluating, modulating, and responding to characteristic changes in a matrix of two or more stochastic generators when influenced by other external stochastic generators, such as mental or thought field energy is provided. The QICS device includes two or more quantum source generators, a quantum pattern processor, and a feedback controller. In operation the QICS provides an interface between quantum form-information created by a mental or thought energy, and one or more corresponding physical states as expressed through temporal constraints defined by perception, movement, and communication. The QICS recognizes and utilizes the phenomenon that quantum generators capable of producing quantum form-information, such as mental energy or other stochastic resonators inherently respond to quantum form-information of one another and additionally imprint quantum form-information onto each other.

The invention discloses a GNSS carrier loop circuit tracking method based on a stochastic resonance algorithm. According to the invention, the signal to noise ratio of receiving signals is increased by integral accumulation and multiplication feedback treatment on input signals; rough tracking of frequency of the receiving signals is performed by utilizing a second-order frequency locking loop; and precise tracking is performed on the signals subjected to rough tracking by adopting a third-order phase locking loop. Therefore, precise carrier loop circuit tracking on the GNSS signals is realized in a low carrier-to-noise ratio. According to the invention, an advantage of the stochastic resonance algorithm in an aspect of improving the signal to noise ratio and an advantage of anti-interference and robustness of the cooperation of the second order frequency locking loop and the third-order phase locking loopare integrated effectively. The method is simple in hardware implementation and stable in signal tracking, and can realize stable and reliable tracking of highly dynamic satellite signals by a receiver in a severe environment.

The invention relates to a bistable optimal stochastic resonance single-frequency weak signal detection method based on variable frequency in the technical field of signal processing. The method comprises the following steps of: multiplying a single-frequency received signal r(t) and a local signal cos([omega]st+2[pi][delta]f*t) to perform frequency conversion; performing weighted summation of the received signal r(t) cos([omega]st+2[pi][delta]f*t) after frequency conversion and a locally generated zero-mean unit power resonance white Gaussian noise nSR(t); inputting the weighted sum signal [k1r(t)cos([omega]st+2[pi][delta]f*t)+k2nSR(t)] into a bistable stochastic resonance system to obtain an output signal-to-noise ratio SNRo of the bistable stochastic resonance system at a frequency [delta]f; performing maximum likelihood optimization on a weighting coefficient to obtain an optimal weighting coefficient; bringing the optimal weighting coefficient into the bistable stochastic resonance system to obtain a state variable output sequence of the system, and inputting the output sequence into an energy detector to obtain the energy of an output signal; and judging that a signal to be detected exists when the energy of the output signal is greater than the set energy threshold,. The invention has advantages of low calculation complexity, good robustness, high detection accuracy and strong feasibility and practicability.

The invention discloses a low-concentration gas detection method based on double threshold stochastic resonance. The method includes the steps: measuring resistance values of a gas-sensitive sensor under different concentrations of reference gas; inputting the measured resistance values as signals, carrying out normalization processing on the signals, and then carrying out curve fitting to obtain a periodic function by Fourier series; carrying out discretization processing on the periodic function to obtain an input function; inputting the input function and added gaussian white noise into a double threshold stochastic resonance system, carrying out value assignment by using a double threshold detector, taking a value-assignment signal as an output signal, and calculating a cross-correlation coefficient of the double threshold stochastic resonance system; searching an optimal double threshold to make the cross-correlation coefficient maximal, and building a linear regression equation with the maximal cross-correlation coefficient and a corresponding gas concentration; and selecting to-be-measured low-concentration gas, acquiring a corresponding maximal cross-correlation coefficient of the to-be-measured low-concentration gas, introducing into the linear regression equation, and calculating out the concentration of the to-be-measured low-concentration gas.

The invention discloses an enhanced stochastic resonance system and a diagnostic method for a machinery fault based on the enhanced stochastic resonance system. According to the invention, a plurality of stochastic resonance systems are organically combined together so as to enhance weak signals, wherein the output of this level of the stochastic resonance system is used as the input of the next level of the stochastic resonance system, and then, fault information of mechanical equipment is extracted with an enhanced stochastic resonance technology so as to effectively diagnose the fault of the mechanical equipment. This method solves the problem that the weak signals are hard to extracte under a background of strong noises, amplifies the weak fault information submersed by the noises and plays an important role in diagnosis for early fault of the mechanical equipment.