78 results about "Stochastic resonance" patented technology

The invention provides an adaptive stochastic resonance weak fault detection method based on kurtosis optimization, and belongs to the technical field of fault detection, and the method comprises the steps: collecting and preprocessing a transient signal on a cable line; constructing a bistable stochastic resonance model; kurtosis of the output signal is used as a fitness function, and potential well parameters of the bistable stochastic resonance model are optimized based on a particle swarm optimization algorithm; and substituting the optimized potential well parameter into the bistable stochastic resonance model, and processing the preprocessed transient signal to obtain an enhanced output signal for fault detection. The method has the beneficial effects that the stochastic resonance system is constructed on the basis of the classical bistable model, the kurtosis of the output signal is used as the fitness function of the particle swarm optimization algorithm, the potential well parameters are adaptively optimized, the potential well parameters are driven to automatically converge to the optimal state, and the robustness of the system is improved. The high-impedance grounding fault traveling wave signal can be extracted and enhanced from a strong noise background, and the detection sensitivity and reliability are improved.

The invention discloses a low-illumination image quality improvement method based on self-adaptive stochastic resonance. The method is specifically implemented according to the following steps: step 1, synchronously acquiring a grayscale image and a color original image corresponding to the same image through two channels; step 2, preprocessing the grayscale image and the color original image acquired in the step 1; 3, self-adaptive stochastic resonance parameter calculation is carried out; step 4, performing dual-channel adaptive stochastic resonance processing on the preprocessed grayscale image and the preprocessed color original image according to adaptive stochastic resonance parameters to obtain an enhanced image; and 5, carrying out brightness component fusion on the enhanced image, and then carrying out image reconstruction and post-processing. According to the invention, the problem of poor image quality after low-illumination image enhancement in the prior art is solved.

The invention discloses a phase modifier bearing fault diagnosis method, which belongs to the technical field of power equipment fault diagnosis, and comprises the following steps of: analyzing independent components, performing blind source separation on a received phase modifier bearing vibration signal, and extracting three types of independent source signals of impact, abrasion and noise; the gradient driving window length is self-adaptive, and the window length of short-time Fourier transform is dynamically adjusted based on the instantaneous frequency gradient of the independent source signal; wavelet packet frequency band energy screening: performing wavelet packet decomposition on the signal after window length adaptive processing, screening a fault characteristic frequency band based on an energy contribution rate, and reconstructing the signal; and enhancing stochastic resonance, and inputting the reconstructed signal into a stochastic resonance system. According to the method, independent component analysis, gradient driving window length self-adaption, wavelet packet frequency band energy screening and stochastic resonance enhanced fourth-order diagnosis chain are constructed, so that multi-stage cooperative processing of phase modifier bearing faults is realized, and the technical problems of time-frequency resolution contradiction, insufficient feature decoupling and weak generalization ability are effectively solved.

The invention discloses an underwater target signal enhancement method based on bistable stochastic resonance and a particle swarm optimization algorithm, and the method comprises the steps: S1, collecting a simulation perception electric signal corresponding to a flow field caused by the disturbance of a propeller of an underwater vehicle through a bionic side line perception sensor based on friction nanometer power generation; s2, preprocessing the simulated sensing electric signal to obtain a preprocessed signal sequence; and S3, constructing a target signal enhancement strategy based on bistable stochastic resonance and a particle swarm optimization algorithm, and performing signal enhancement processing on the preprocessed signal sequence based on the target signal enhancement strategy to obtain a target enhanced signal. According to the invention, the problem that the existing method cannot effectively realize underwater target signal enhancement is solved.

This invention discloses a method for enhancing weak underwater signals using stochastic resonance, belonging to the field of underwater acoustic engineering and signal processing technology. It is used for enhancing weak underwater signals, including constructing and preprocessing an integrated detection and communication signal; constructing a linear potential well wall piecewise tristable stochastic resonance system; inputting the preprocessing results into the linear potential well wall piecewise tristable stochastic resonance system; optimizing the parameters of the linear potential well wall piecewise tristable stochastic resonance system using an improved moss growth optimization algorithm; numerically solving the system's motion equations using the fourth-order Runge-Kutta method to obtain the enhanced output signal; and performing frequency recovery on the enhanced output signal to generate an output signal with a complete linear frequency modulation structure. This invention fundamentally solves the problems of output saturation and difficulty in improving the signal-to-noise ratio in traditional stochastic resonance systems under strong noise environments through the linear potential well wall piecewise tristable structure, significantly improving the system's adaptability to complex underwater noise.

The invention discloses a method and a system for detecting weak signals and harmonic waves based on stochastic resonance of compression enhancement. The system comprises a vacuum system, an imprisoned field, a laser system, a fluorescence acquisition system and an external signal, the vacuum system comprises a vacuum cavity, an ion pump and a getter pump, and a calcium atom furnace and a surface electrode ion trap for trapping calcium ions are placed in the vacuum cavity; the trapping field comprises a radio frequency trapping field and a direct current trapping field, and the radio frequency trapping field is composed of a radio frequency signal source, a power amplifier and a spiral resonant cavity; the direct-current trapping field comprises a direct-current high-voltage power supply, a 6733 board card and an amplification chip. The fluorescence acquisition system comprises an imaging mirror, a CCD (Charge Coupled Device) camera and a photomultiplier; the external signal comprises a driving signal, a compression signal and a to-be-tested signal. According to the method, the compressed signal is used for replacing external random noise, ions are triggered to generate periodic phase change along with the to-be-detected signal, the to-be-detected signal is optimally matched with the bistable system by changing the strength of the compressed signal, and the signal-to-noise ratio and harmonic detection are improved to the maximum extent.

The invention provides a method for extracting features of ultrasonic waves generated by a typical discharge phenomenon in a complex working condition environment, and belongs to the technical field of power equipment.The method comprises the steps that a transient feature vector is extracted by conducting synchronous compression wavelet transform on a signal component, the transient feature vector is input into a waveform propagation enhancement model, and an enhanced feature vector is output; stochastic resonance amplification is carried out on the enhanced feature vector to establish a weak feature vector, the weak feature vector and the transient feature vector are fused to establish an equivalent discharge intensity matrix, a discharge type discrimination index is calculated, the discharge type is determined according to the discrimination index, and a propagation speed parameter of a multi-path delay correction matrix is adaptively adjusted. And on the basis of the updated propagation path analysis result, a time difference of arrival matrix and a maximum likelihood estimation method are utilized to calculate the spatial position coordinates of the discharge source, and the problem of low discharge ultrasonic feature extraction accuracy caused by multi-medium propagation path interference in a complex working condition environment is solved.

The application discloses a fault positioning method and device, electronic equipment, storage medium and program product; the method comprises the following steps: when it is detected that a traveling wave analysis starting condition is met, a fault traveling wave signal set to be analyzed of two measuring points of a line is acquired, the fault traveling wave signal set comprises fault traveling wave signals collected in different sampling periods, and the traveling wave analysis starting condition is determined according to a gain function of a power frequency signal; for each fault traveling wave signal set, a random resonance system potential function is constructed according to the fault traveling wave signal set, system resonance is performed on the fault traveling wave signal according to the random resonance system potential function, and a traveling wave signal to be extracted is obtained; a wave head is extracted from the traveling wave signal to be extracted, and a traveling wave arrival time corresponding to the measuring point is recorded; and fault positioning is performed according to the traveling wave arrival time corresponding to each measuring point and the total length of the line, and a fault position is determined, so that the problems of inaccurate fault traveling wave signal collection and inaccurate wave head extraction are solved.

The application provides a weak non-stationary signal processing method and system, and relates to the technical field of signal processing, and comprises the following steps: obtaining a weak non-stationary signal; estimating the signal frequency range of the weak non-stationary signal, and estimating the noise intensity according to the estimated signal frequency range; constructing a stochastic resonance model based on a Gaussian mixed potential function, and adaptively optimizing the Gaussian mixed potential function according to the estimated noise intensity, so as to obtain an optimal stochastic resonance model, with the maximum spectral amplification factor of the stochastic resonance model as the target; constructing a parameterized demodulation operator to demodulate the weak non-stationary signal, inputting the demodulated signal into the optimal stochastic resonance model for enhancement, and finally obtaining the enhanced non-stationary signal through inverse demodulation; and the application is based on Gaussian mixed potential function stochastic resonance, and solves the bottleneck problem of weak non-stationary signal processing through a closed-loop process of noise intensity estimation, potential function optimization, frequency estimation and signal enhancement.

The application relates to an underwater optical communication receiving signal detection method. The method comprises the following steps: pre-processing a receiving signal disturbed by turbulence by using secondary sampling to obtain a small parameter signal; using a Kalman filtering algorithm to obtain the envelope of the receiving signal disturbed by turbulence by processing the small parameter signal; processing the envelope of the receiving signal disturbed by turbulence according to a signal equalization formula; establishing an adaptive stochastic resonance system model, and taking the processed signal as the input signal of the adaptive stochastic resonance system model; using a multi-strategy fusion particle algorithm to optimize the system parameters in the stochastic resonance system model by taking the system output signal-to-noise ratio as a target function; judging whether the iteration of the multi-strategy fusion particle algorithm is terminated, and if the iteration is terminated, outputting the optimal system parameters corresponding to the maximum value of the target function; and inputting the optimal system parameters into the adaptive stochastic resonance system model. The application can realize adaptive stochastic resonance detection.

The invention relates to a training method of a frequency difference estimation model, belongs to the technical field of radiation source signal positioning, and solves the problem that the prediction precision of an existing frequency difference estimation model on a signal frequency difference is poor. The training method comprises the steps that multiple tests are carried out by changing the positions of radiation source signals, two observation stations are used for receiving signals in each test, the signals, received by the two observation stations, of the radiation source at the same position serve as a set of signals, and the frequency difference of each set of signals is marked; performing signal enhancement processing on each group of signals by using a preset optimal stochastic resonance system, and taking each group of signals after signal enhancement processing and the frequency difference of each group of signals as a training sample to obtain a training set of a frequency difference estimation model; training the frequency difference estimation model based on the training set to obtain a trained frequency difference estimation model; wherein the frequency difference estimation model adopts a convolutional neural network. And the frequency difference prediction precision of the frequency difference estimation model is improved.

The application discloses a method and system for detecting weak signals and harmonics based on compression-enhanced stochastic resonance, comprising a vacuum system, a trapped field, a laser system, a fluorescence collection system and an external signal; the vacuum system comprises a vacuum cavity, an ion pump and a suction pump, a calcium atom furnace and a surface electrode ion trap for trapping calcium ions are arranged in the vacuum cavity; the trapped field comprises a radio frequency trapped field and a direct current trapped field, the radio frequency trapped field is composed of a radio frequency signal source, a power amplifier and a spiral resonant cavity; the direct current trapped field comprises a direct current high-voltage power supply, a 6733 board card and an amplification chip; the fluorescence collection system comprises an imaging mirror, a CCD camera and a photomultiplier tube; the external signal comprises a driving signal, a compression signal and a signal to be detected. The application uses the compression signal to replace the external random noise, triggers the ion to have periodic phase change with the signal to be detected, changes the intensity of the compression signal, makes the signal to be detected and the bistable system reach the best matching, and maximizes the signal-to-noise ratio and the detection of harmonics.

The present invention discloses a rolling bearing weak fault enhanced diagnosis method, device, equipment and storage medium. The method adopts an improved differential search algorithm and uses the minimum mean square envelope entropy as the optimization objective function to perform variational modal decomposition on the rolling bearing acceleration vibration signal to obtain the optimal decomposition level and the optimal quadratic penalty factor of the variational modal decomposition; the rolling bearing acceleration vibration signal, the optimal decomposition level and the optimal quadratic penalty factor are substituted into the variational modal decomposition parameters to obtain the natural modal components of the rolling bearing acceleration vibration signal after decomposition; the optimal component among the natural modal components is selected by correlation kurtosis, and a reconstructed signal is generated based on the optimal component; the reconstructed signal is input into a stochastic resonance model optimized by the improved differential search algorithm, and an enhanced rolling bearing acceleration vibration signal is output. The enhanced rolling bearing acceleration vibration signal is subjected to envelope spectrum analysis. The present invention can better identify the characteristics of weak faults.

The invention relates to a variable-speed bearing fault feature extraction method and device, relates to the technical field of bearing fault diagnosis, and aims to solve the problem that the variable-speed bearing fault feature extraction precision is not high. The method comprises the steps that a particle swarm optimization algorithm is introduced into a multi-scale adaptive morphological filtering operator, key parameters of the adaptive morphological filtering operator are obtained, filtering signals of all scales are obtained through calculation according to the multi-scale adaptive morphological filtering operator and the key parameters, and the filtering signals of all the scales are added to obtain a primary filtering signal; introducing a particle swarm optimization algorithm into an adaptive stochastic resonance algorithm to obtain a system cooperative adjustment parameter of the adaptive stochastic resonance algorithm when the signal-to-noise ratio is maximum, and inputting the primary filtering signal into the adaptive stochastic resonance algorithm to obtain a secondary filtering signal; and calculating the secondary filtering signal by using an angular domain resampling algorithm to obtain an order spectrogram containing bearing fault features. The method has the effect of improving the fault feature extraction precision of the variable-speed bearing.

The application relates to a bistable SR system and a low signal-to-noise ratio multi-frequency sound signal detection device and method. By introducing an optical fiber device, the closed-loop regulation of the optical fiber grating is utilized to realize the random resonance phenomenon based on the bistability, and the low signal-to-noise ratio signal can be effectively extracted under the strong noise background. Secondly, by introducing a carrier signal, the difference frequency signal is obtained by mixing with the external signal, and the multi-frequency signal sensing is realized under the premise of fixing the detection range of the difference frequency signal. In addition, by introducing the array structure of the optical fiber hydrophone, the spatial gain is formed, and the high-sensitivity detection effect can be realized. Finally, the frequency distribution and the relative signal-to-noise ratio intensity size distribution of the obtained target sound signal can be used as the input of the neural network model, the intelligent identification of the low signal-to-noise ratio target sound target is realized, and the detection and early warning of the underwater target can be maximally realized.

The application belongs to the technical field of photovoltaic electrical fault detection, and particularly discloses a research method for detecting features of a direct-current fault arc of a photovoltaic system based on adaptive morphological enhancement. In order to ensure the reliability of the system, the fault arc is accurately detected and the detection features are enhanced. The enhancement effect of morphological on the detection features of the direct-current fault arc is studied, and an adaptive morphological algorithm is proposed. The particle swarm algorithm is combined with signal features to dynamically optimize the structure element, improve the signal signal-to-noise ratio, enhance the detection features, and compared with the stochastic resonance and wavelet analysis algorithm, so that the good feature enhancement effect of the application is verified, and the normal and fault arcs can be effectively distinguished. Finally, the features are decided by combining the support vector machine, and a detection accuracy of 96.23% is obtained. The adaptive morphological algorithm can effectively enhance the detection features of the fault arc, is suitable for adaptive optimization in different experimental scenes, and is helpful for accurately and effectively detecting the direct-current fault arc.

This invention relates to the field of elevator technology, and more particularly to an anomaly detection method for elevator polyurethane buffers based on millimeter-wave radar. The method includes: acquiring echo signals within a preset time window after the polyurethane buffer's compression and rebound by millimeter-wave radar; determining the target distance unit and generating a vibration displacement signal in the radar's line-of-sight direction; identifying intervals where the overall vibration envelope shows a decaying trend and extracting the damping response signal; implementing bistable stochastic resonance enhancement and variational mode decomposition to identify the target modal signal; calculating damping characteristic indices using the target envelope amplitude and stiffness characteristic indices using the target phase to construct a health feature vector; and determining whether the obtained health feature vector lies within the constructed health reference space. Using non-contact millimeter-wave radar to acquire echo signals during the polyurethane buffer's rebound process allows for precise capture of phase changes caused by minute movements or vibrations of the target, resulting in high detection efficiency and safety.

With a simple structure, to make it possible to detect a head biological signal that reflects the state of cerebral blood flow, and to make it possible to assess a sleep stage based on a variation in the cerebral blood flow. A head biological signal detection sensor is supported on the head using a sensor support member such that a connecting yarn sags to make an interval between a pair of facing ground knitted fabrics in a three-dimensional knitted fabric narrower than that under no load. With this configuration, microvibration occurring in yarns and fibers forming the three-dimensional knitted fabric of the head biological signal detection sensor or in other places due to head movement resulting from body motion or the like generates stochastic resonance, which makes it possible to detect a head biological signal.

The present invention discloses a noise reduction method for a CNC machine tool power tool holder based on UUSGSR, which belongs to the technical field of monitoring the power tool holder of a CNC machine tool servo tool holder. The method of the present invention comprises the following steps: performing Hilbert transform and elliptic filtering preprocessing on the vibration signal of the bearing in the monitored tool holder power tool holder; constructing a non-saturated scaled Gaussian potential stochastic resonance model to process the signal; proposing a high-order weighted harmonic noise ratio (HWR) HWHNR As the objective function, reflecting the nonlinear characteristics of the signal, the Gray Wolf Optimization Algorithm is used to optimize the structural parameters and damping factors, resulting in the optimal underdamped, unsaturated, scaled Gaussian potential stochastic resonance model (UUSGSR) containing these model structural parameters and damping factors. This model is then used to perform stochastic resonance processing on the bearing vibration signal within the toolholder power toolholder, reducing the noise contained in the bearing vibration signal and improving the signal-to-noise ratio. In summary, the method disclosed in the present invention can achieve noise reduction for the bearing vibration signal within the toolholder power toolholder.

The application provides a kind of self-adapting random resonance weak fault detection method based on kurtosis optimization, belongs to the technical field of fault detection, including: collecting transient signal on cable line and preprocessing;Constructing bistable random resonance model;With the kurtosis of output signal as fitness function, the potential well parameters of bistable random resonance model are optimized based on particle swarm optimization algorithm;The optimized potential well parameters are substituted into the bistable random resonance model, and the preprocessed transient signal is processed to obtain the enhanced output signal for fault detection.The beneficial effect is: based on the classical bistable model to construct the random resonance system, the kurtosis of output signal is used as the fitness function of particle swarm optimization algorithm, the potential well parameters are adaptively optimized, the potential well parameters are automatically converged to the optimal state, the high impedance grounding fault traveling wave signal can be extracted and enhanced from strong noise background, and the detection sensitivity and reliability are improved.

To provide a sensitivity improvement device which can improve a sensing function of oral cavity and pharynx using a phenomenon called stochastic resonance.SOLUTION: A sensitivity improvement device of the present invention includes an actuator 14 for generating a noise vibration. By applying the noise vibration to a skin of head and neck or a tongue of a biological body, the sensing function of the oral cavity or a pharynx is improved using a phenomenon called stochastic resonance. Further, threshold value setting means 20 is provided for setting a vibration level of the noise vibration. The threshold value setting means 20 applies different vibration levels to a user, causes the user to input the stimulated vibration level at which the noise vibration is sensed and a non-stimulated vibration level at which the noise vibration is not sensed, and sets a threshold value specific to the user based on the input stimulated vibration level and the non-stimulated vibration level.SELECTED DRAWING: Figure 1

The application provides a hyperspectral image enhancement method and equipment based on two-dimensional random resonance, wherein the method comprises the following steps: step 1, obtaining a hyperspectral image data set; step 2, normalizing the values of all sample points of the hyperspectral image data; step 3, inputting the data of each wave band of the normalized hyperspectral image into a two-dimensional random resonance model 2D DSR for processing, processing the signal of the pixel point by comprehensively utilizing the information of adjacent pixel points, and obtaining enhanced data of each pixel point; and step 4, normalizing the enhanced hyperspectral image data to obtain a final hyperspectral image enhancement result data set. The application popularizes the use of DSR to two-dimensional data processing, can fully utilize the spatial position information, and the output of each pixel point is updated four times, the information in the four directions of up, down, left and right is comprehensively utilized, the signal is effectively enhanced, and the information expression capability is improved.

The invention relates to the technical field of hydraulic systems, in particular to a cleanliness detection method and system of a hydraulic system. The method comprises the following steps: acquiring monitoring signals of the hydraulic system, wherein the monitoring signals comprise an initial photoelectric signal of a photoelectric sensor for detecting oil and a mechanical vibration signal at the same position; the monitoring signal is divided into a plurality of analysis windows, the actual pulse width factor of the mechanical vibration signal in each analysis window is calculated, the potential well width parameter is adjusted through the actual pulse width factor to obtain a self-adaptive potential well width parameter, and the self-adaptive potential well width parameter is inversely proportional to the square of the actual pulse width factor; and processing the initial photoelectric signal by using a stochastic resonance system based on the adaptive potential well width parameter, and performing cleanliness detection based on the processed signal. The method has the effect of improving the oil cleanliness detection precision of the hydraulic system.

The invention discloses a compound fault diagnosis system and method for a centrifugal blower, and belongs to the field of signal fault diagnosis of rotating machinery. The method comprises the following steps: collecting a vibration signal through a multi-position acceleration sensor; decomposing the signal by using empirical mode decomposition, and preliminarily screening IMF components; performing noise reduction by using an arc tangent threshold function; carrying out fault feature enhancement by using an optimized bistable stochastic resonance system; performing feature level fusion on the envelope spectrum of the signal after noise reduction processing and the frequency spectrum of the signal after feature enhancement in a frequency domain to obtain a fusion feature; and inputting the fusion features into a width learning system to realize compound fault diagnosis of the centrifugal blower. According to the method, the self-adaptive decomposition capability of empirical mode decomposition, the noise enhancement characteristic of stochastic resonance, the characteristic complementarity of frequency domain fusion and the rapid classification advantage of a width learning system are integrated, and the centrifugal blower composite fault diagnosis work under the complex working condition can be better completed.

The present application provides a device, system and method for detecting weak electromagnetic signals of a sample, comprising a driving module, N Helmholtz coils, a low-temperature container, two SQUID modules, a first signal processing module and a second signal processing module; N is a natural number greater than 1; the driving module provides driving signals for each Helmholtz coil, each SQUID module includes a SQUID galvanometer and a gradiometer; the input end of each SQUID galvanometer is connected to each gradiometer; the input end of the first signal processing module is connected to the output end of each SQUID galvanometer, and outputs a first processing signal to the second signal processing module; the second signal processing module determines whether the processing signal produces random resonance. The present application can realize synchronous detection, that is, the sample of the substance to be tested dissolved in the solvent and the sample containing pure solvent are tested synchronously at the same time, further avoiding the error caused by the time difference caused by separate testing.

This invention discloses an airborne magnetic anomaly detection method based on a stochastic resonance system, comprising the following steps: S1: Acquiring geomagnetic field data. The data is processed using airborne magnetic compensation technology. S2: Establishing a stochastic resonance detection system and calculating a binary hypothesis detection statistic. The binary hypothesis detection statistic is the standard deviation of the output of the stochastic resonance system. S3: Calculating a threshold and detecting the target. The threshold is calculated using the unit averaging method. The presence of a target is determined by comparing the binary hypothesis detection statistic with the threshold. A target is considered to exist when the binary hypothesis detection statistic of any subsystem in the stochastic resonance system is greater than the corresponding threshold; the measurement is considered pure noise only when both subsystem statistics are less than the threshold. This method addresses the problems of low signal-to-noise ratio and the severe influence of noise type on correlation matching detection methods during magnetic anomaly detection in complex environments.

The invention discloses a three-phase motor fault diagnosis method and system based on FPGA heterogeneous control, and relates to the field of motor fault diagnosis, and the method comprises the steps: collecting a current signal, a vibration signal and a temperature signal of a three-phase motor, and carrying out the preprocessing of the current signal, the vibration signal and the temperature signal; inputting the preprocessed current signal into a quantum stochastic resonance unit in the FPGA, injecting controllable quantum noise by dynamically adjusting bistable parameters, enhancing weak fault features, and obtaining an enhanced current signal; and carrying out fast Fourier transform on the enhanced current signal to extract a harmonic characteristic so as to obtain a current harmonic characteristic, and carrying out wavelet packet decomposition on the preprocessed vibration signal to extract frequency band energy. According to the invention, the quantum stochastic resonance unit realized by the FPGA dynamically adjusts bistable parameters and injects controllable noise, and the stochastic resonance effect of the nonlinear unit is utilized to effectively improve the extraction capability of weak fault features in a strong noise environment.

The present application belongs to one-dimensional signal processing technical field, and particularly relates to a Duffing oscillator weak signal enhancement method with automatic parameter optimization. The present application takes a double-stage parallel Duffing random resonance system as a mixed signal processing model, uses a quantum particle swarm algorithm with outstanding performance in optimization solution as a parameter optimization algorithm, converts the double-stage parallel random resonance parameter adjustment problem into a multi-parameter global optimization problem, and performs signal enhancement processing on the signal to be enhanced input into the system according to the best parameters, and outputs the enhanced signal. The present application uses parameter self-adaptive optimization to quickly obtain the best parameters of the double-stage parallel Duffing random resonance system, greatly improves the efficiency and precision of the system best parameter setting, and can quickly and high-quality output the enhanced useful signal.

The application discloses an underwater propeller fault feature enhancement method based on a stochastic resonance system, carries out underwater robot propeller fault test, and collects dynamic signals of the underwater robot; the dynamic signals are input into a bistable stochastic resonance system to obtain a Langevin equation; the fourth-order Runge-Kutta method is used to solve the Langevin equation to obtain a stochastic resonance system output signal; the stochastic resonance system output signal is processed based on a modified Bayesian algorithm to obtain a fault feature sequence, and a maximum value in the fault feature sequence is taken as a fault feature value; structure parameters of the bistable stochastic resonance system are optimized to obtain multiple fault feature values; and a fault feature sequence corresponding to a maximum value in the multiple fault feature values is taken as a fault feature enhancement result. Through optimization of the structure parameters of the stochastic resonance system, multiple fault feature values are obtained, and the maximum value is selected as an enhanced structure, so that the fault feature effect is more obvious.