104 results about "Stochastic resonance" patented technology

The invention discloses a deep neural network model-based backfill compaction degree quality evaluation method, which comprises the following steps of: acquiring various physical characteristics of soil in a compaction process in real time through a multi-source sensor, and performing data labeling and time-space adaptive normalization processing; based on the position information of the multi-source sensor and the multi-source sensing data, adopting an improved empirical mode decomposition and stochastic resonance enhancement method, and fusing same-order mode components of the multi-source sensor to obtain an intrinsic mode function related to the compactness; in combination with graph convolution operation, stochastic resonance gating, multi-scale time sequence attention, a mixed loss function, a dynamic course learning strategy and the like, training the deep neural network model; and based on the trained model, carrying out backfill compaction degree quality evaluation on the to-be-detected area. According to the method, by collecting multi-source data in real time and combining advanced technologies such as space-time adaptive normalization, empirical mode decomposition and dynamic adaptive graph convolution, efficient and stable backfill compaction degree evaluation is achieved.

The invention relates to a rolling bearing early fault early warning and diagnosis method, which comprises the steps of extracting an envelope component from a bearing vibration signal, constructing a time-delay feedback stochastic resonance optimal model by taking an improved signal-to-noise ratio INSR as an optimization objective function, obtaining an output signal, obtaining a first reconstruction signal through CEEMDAN adaptive decomposition and IMF component screening, and obtaining a second reconstruction signal through the CEEMDAN adaptive decomposition and IMF component screening. Calculating the signal-to-noise ratio ISNR of the vibration signal at the fault characteristic frequency, comparing the signal-to-noise ratio ISNR with a preset initial threshold value, judging whether an early warning is given out or not, and if the early warning is given out, processing the vibration signal by using a multi-wavelet adjacent coefficient adaptive threshold value method to obtain a denoised signal; processing the denoised signal by combining a fast spectral kurtosis method and an ensemble empirical mode decomposition method to obtain a second reconstructed signal; and processing by using improved fast spectrum correlation to obtain a corresponding enhanced envelope spectrum, and comparing the enhanced envelope spectrum with a fault characteristic frequency for identification. Compared with the prior art, accurate early warning and diagnosis can be carried out on early weak faults of the rolling bearing.

The invention discloses a magnetic field measurement method and system based on a multi-sensor fusion technology, and relates to the technical field of sensor fusion and magnetic field measurement, and the method comprises the steps: deploying a multi-sensor data array, carrying out the adaptive initialization of a bistable SR parameter range, defining an SR system differential equation, and carrying out the iterative optimization through employing an MPA population. Carrying out Hilbert transform edge detection on enhanced signal component data, calculating an array inclination angle, carrying out abbe error and bidirectional projection error compensation, and carrying out metasurface grid coordinate quantization mapping; the collected and cross-scale magnetic field data set is preprocessed and packaged into data cells, quality evaluation and weight distribution are carried out on the data cells, and extended Kalman filtering data fusion is carried out; by introducing a bistable stochastic resonance system and an MPA population optimization algorithm, a weak magnetic field signal is obviously enhanced, and by calculating an array inclination angle and compensating an Abbe error and a bidirectional projection error, the space consistency of a measurement result is improved.

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 numerical control machine tool power tool apron noise reduction method based on UUSGSR, and belongs to the technical field of numerical control machine tool servo tool rest power tool apron monitoring. The method comprises the specific steps that Hilbert transform and elliptic filtering preprocessing are conducted on monitored bearing vibration signals in a tool rest power tool apron; constructing an unsaturated scaling type Gaussian potential stochastic resonance model processing signal; a high-order weighted harmonic noise ratio HWHNR is proposed as a target function to reflect signal nonlinear characteristics; optimizing the structural parameters and the damping factors by using a grey wolf optimization algorithm to obtain an optimal under-damping unsaturated scaling type Gaussian potential stochastic resonance model UUSGSR containing the model structural parameters and the damping factors, and performing stochastic resonance processing on bearing vibration signals in the power tool apron of the tool rest by using the model. Noise contained in a bearing vibration signal in the power tool apron of the tool rest is weakened, and the signal-to-noise ratio is improved. In conclusion, the method can realize noise reduction of the vibration signal of the bearing in the power tool apron of the tool rest.

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 present application discloses a method for frequency identification of a measurement while drilling signal, which relates to the technical field of signal processing. The method comprises the following steps: s ( t ) and noise n ( t ) input a high-order bistable stochastic resonance system; transform the damping coefficient, system parameters and system output response in the high-order bistable stochastic resonance system to obtain an equivalent equation; detect the noise intensity D Is it consistent with the signal to be tested? s ( t ) matches, if it matches, the signal is output directly and the signal to be tested is restored s ( t If the frequency does not match, the parameters of the high-order bistable stochastic resonance system are adjusted according to the parameter adjustment rules. This application solves the problem that the useful signal may be damaged or even unrecognizable during filtering.

The present invention discloses an image enhancement method for optimizing the parameters of array stochastic resonance based on an improved whale algorithm, which is specifically implemented according to the following steps: Step 1, image dimensionality reduction and encoding to obtain a binary sequence; Step 2, performing binary amplitude pulse modulation on the sequence obtained in Step 1 to obtain a one-dimensional bipolar aperiodic BPAM signal; Step 3, establishing a parameter adaptive array model of IWOA; Step 4, demodulation and decoding; Step 5, restoring the image; The parameter adaptive array stochastic resonance strategy based on the improved whale optimization algorithm has more excellent performance than the array stochastic resonance method with fixed parameters and the classical image denoising method, both in terms of visual effect and PSNR index; and as the number of arrays increases, both the visual effect and PSNR of the denoised image are improved; This further proves the good application of the method of the present invention in weak signal detection and extraction.

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 present invention discloses a method for detecting the freshness of beef and a computer-readable storage medium. It includes the following steps: introducing clean air into the detection chamber for cleaning; after the detection chamber is cleaned, inhaling the volatile gas generated by the beef sample to be detected into the detection chamber, and the gas sensor array in the detection chamber detects the volatile gas and sends the detection data to a computer; the computer performs partial differential structure processing on the detection data to obtain corresponding partial differential structure signals; the computer inputs the partial differential structure signals into a nonlinear stochastic resonance model to obtain corresponding signal-to-noise ratio eigenvalue; the computer determines the freshness of the beef according to the signal-to-noise ratio eigenvalue. The present invention can perform partial differential structure processing on the detection data, so that the input signal input into the nonlinear stochastic resonance model matches the nonlinear stochastic resonance model, avoiding the situation of non-resonance, thereby improving the detection stability and detection accuracy.

The invention provides a rolling bearing fault diagnosis method based on novel asymmetric multistable coupling stochastic resonance. The rolling bearing fault diagnosis method comprises the following steps: establishing a novel asymmetric multistable situation function model; establishing a novel asymmetric multistable situation coupling function model; forming a novel linear coupling asymmetric multistable stochastic resonance model by two independent asymmetric multistable systems in a linear coupling mode; signal acquisition and signal preprocessing; calculating the fault characteristic frequency of the rolling bearing; stochastic resonance calculation based on novel asymmetric coupling multistable states; and comparing the output result with the actual fault characteristic frequency. According to the rolling bearing fault diagnosis method based on novel asymmetric multistable coupling stochastic resonance provided by the invention, the problems that potential function symmetry is difficult to maintain in an actual physical model and mutual coupling influence of all parts of equipment is difficult to detect by a single system in an actual working condition are effectively solved; and the fault characteristic frequency can be better highlighted.

The present invention proposes a method for diagnosing motor faults based on a particle swarm-based Lorenz-like stochastic resonance system, which includes collecting stator current data of a motor under different fault types by using a current sensor as the original signal; inputting the original signal data into the particle swarm-based Lorenz-like stochastic resonance system, taking the signal-to-noise ratio as the objective function, adaptively adjusting the parameters of the Lorenz-like system, and outputting the data with the optimal signal-to-noise ratio under the optimal parameters; and inputting the data into a well-trained extreme learning machine for fault diagnosis. The Lorenz-like stochastic resonance system proposed by the present invention has advantages such as multiple adjustable parameters and strong plasticity, providing a large adjustment space for the system to adaptively adjust parameters for different types of fault signals. The optimal signal-to-noise ratio data output by the system can highlight the signal characteristics, and thus can accurately perform fault diagnosis.

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.

Disclosed are oligomeric machines for energy harvesting having a first oligomeric module having a first end and a second end, a second oligomeric module having a first end and a second end. Exemplary oligomeric machines are configured to exhibits stochastic resonance and / or spontaneous vibrations and are configured such that in response to a prescribed amount of energy applied thereto, relative movement occurs between the first oligomeric module and the second oligomeric module in a manner causing the mechanical action of the second oligomeric module on an electric generating element to produce an electrical voltage and / or current. Also disclosed are energy harvesting cells having a thermal cell, a mechanical-electrical transducer with at least two capacitor plates, and at least one oligomeric machine.

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.

The invention discloses a motor state monitoring method based on a stochastic resonance-like reaching law sliding-mode observer, and the method comprises the following steps: constructing a sliding-mode observer based on a doubly-fed induction motor state equation in combination with a stochastic resonance-like reaching law; the Lyapunov stability theory is utilized to carry out stability analysis on the designed reaching law, the value range of gain parameters in the reaching law is defined, and disturbance rejection analysis is carried out on the designed sliding mode observer; a variable wind speed fault, an input interference voltage fault, a voltage drop fault, a stator turn-to-turn short circuit fault and a rotor current sensor fault are introduced, and the applicability and tracking performance of the designed observer under different motor faults are verified by using a residual value of a motor actual rotor current output value and a sliding mode observation estimation value. The motor state monitoring method based on the similar stochastic resonance reaching law sliding mode observer can reduce sliding mode buffeting and has the advantages of being good in universality and high in tracking precision.

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.