Generative adversarial networks-enhanced quasi-square wave modulated photoacoustic spectroscopy: A highly sensitive NH3 detection method under strong background noise.

The noise immune characteristics of chaotic systems can effectively detect useful signals in the background of strong noise. Aiming at the problem that the state of phase space is judged by human eye observation as the main means and lack of reliable theoretical support, a small sample SVM algorithm for intelligent recognition of chaotic images is proposed in this paper. Firstly, the detection flow of Duffing oscillator under the background of strong acoustic noise while drilling is given, and the weak signal detection models of different state space are given based on phase space and Lyapunov index. The simulation is carried out under different SNR and measured data. Finally, the SVM model is used to intelligently identify chaos and other state space results. The simulation results show that the chaotic system can detect the target signal effectively under different SNR. The minimum SNR is about-80dB and the recognition accuracy of SVM intelligent model is 95%. This conclusion verifies the strong noise immunity of Duffing oscillator system and the effectiveness of SVM model recognition image.

In this study, a multi-layer perceptron model (EL-NEP-MLP) combining ensemble learning and noise-enhanced prediction is proposed to improve the prediction accuracy and stability of gas concentration in photoacoustic spectroscopy (PAS) under complex noise environments. In practical applications, PAS signals are susceptible to various noises, especially in low-concentration gas detection. The multi-layer perceptron (MLP) is used for photoacoustic signal feature extraction, ensemble learning and noise-enhanced prediction to improve system robustness and generalization capability. The experimental results show that EL-NEP-MLP can predict the gas concentration quickly and accurately under complex noise conditions.

Research on fault diagnosis method of electric gate valve under strong background noise

Signal-to-noise ratio improvement of the signal immersed in the strong background noise using a bistable circuit with tunable potential-well depth

In order to solve the problem of extracting ultrasonic signals from strong background noise, a novel method, which is termed APSO-SD algorithm and based on improved adaptive particle swarm optimization (APSO) and sparse decomposition (SD) theory, is proposed in this paper. This method can convert the ultrasonic signal denoising problem into optimizing the function on the infinite parameter set. First, based on the sparse decomposition theory and the structural characteristics of ultrasonic signal, the objective function of particle swarm optimization algorithm and the reconstruction algorithm of the denoised signal are constructed, so that particle swarm optimization and ultrasonic signal denoising can be combined. Second, in order to improve the robustness of the proposed approach, an APSO algorithm is proposed. What is more, because particle swarm optimization algorithm can be used to optimize in continuous parameter space, and according to the empirical characteristics of the ultrasonic signals used in practical engineering, a continuous super complete dictionary for matching ultrasonic signals is established. Since the super complete dictionary is continuous, there are an infinite number of atoms in the established dictionary. The redundancy of dictionaries is enhanced by the method in this paper. Based on the fact that the inner product of the optimal atom and the ultrasonic signal is one and the inner product of the noise and the optimal atom is zero in the established dictionary, the objective optimization function of APSO-SD algorithm is established. Finally, the optimal atom is determined based on the optimization result of the objective function. In this way, the denoising ultrasonic signal can be reconstructed by using the optimal atom according to the reconstruction algorithm. The processing results of simulated ultrasonic signals and measured ultrasonic signals show that the proposed method can effectively extract weak ultrasonic signals from strong background noise whose signal-to-noise ratio is lowest, as low as-4 dB. In addition, compared with the adaptive threshold based wavelet method, the proposed method in this paper shows the good denoising performance. In this paper, it is demonstrated that the problem of ultrasonic signal denoising can be transformed into the optimization of constraint functions. Furthermore, the ability of the proposed APSO-SD algorithm to accurately recover signals from noisy acoustic signals is better than that of the common wavelet method.

Impulsive modulated signals are widely existed in mechanical systems especially when faults occurred. Traditional envelope analysis has provided an important and effective approach to extract the impulsive features but failed to get an ideal result under strong background noise. A new approach to demodulation and de-noising the impulsive modulated signal with strong background noise using multi-scale morphological filters is put forward and examined in this study. Adaptive method for selecting the length and amplitude of structure elements using local-peak-value is introduced. Four different type of structure elements, i.e. flat, line, triangle and cosine SE, are constructed and applied in demodulate and de-noising the simulated impulse modulated signal with strong noise. The effects of the four type SE and traditional envelope demodulation method are compared. Results show that the line, triangle and cosine SE presented an excellent ability to extract the impulsive features from strong noised signal. The effect of the flat SE is not very ideal but still better than the envelope demodulation method.

In practical mechanical equipment operation, bearing vibration signals are challenging to analyze due to their non-stationarity and low signal-to-noise ratio for traditional detection methods. As a new signal decomposition method, the feature mode decomposition (FMD) method has been successfully applied to bearing fault diagnostics. However, FMD’s decomposition efficiency is easily influenced by the input parameter settings, and the efficiency of the decomposed signals is related to the number of initialized filter banks. For this reason, this paper proposes an adaptive parameterized feature mode decomposition method. Firstly, based bearing fault diagnosis method using a cuckoo search algorithm with logarithmic decline of nonlinear inertial weights and random adjustment discovery probability (DWCS), which is used to optimize the three parameters of the FMD. Then, a new approach to finding out the maximum feature fault frequency and its multiplicative feature frequency is proposed, called the feature frequency ratio (FFR), obtained from the envelope spectrum of bearing fault signal. Finally, this paper uses the DWCS based on the maximum FFR to adaptively select the best FMD parameter combination, which is verified by simulation, the results of the proposed AFMD can effectively extract bearing fault features under strong background noise with a signal-to-noise ratio of −15 dB, and actual experiments further verify the superiority of the method, which not only improves the accuracy of fault diagnosis under strong background noise, but also contributes to the overall maintenance and operational efficiency of the mechanical system.

Underground magnetic resonance sounding (MRS) low signal-noise ratio (SNR) weak signal is hard to manage with traditional de-noising method such as fitting and filter.In view of this, this article makes use of both transient electromagnetic method (TEM) data and MRS data obtained from integrated detecting equipment.The goal is to extract weak MRS signal based on TEM under strong background noise.At first, underground electrical distribution can be acquired from TEM data, which is processed by equivalent conductive plane method.Then, the initial model of moisture content distribution was constructed on geological information and 3D MRS forwarding was made with finite element method.At last, the correlation coefficient series and deviations between forwarding data and measured data were calculated.The initial model would be corrected until the result is satisfying.Model calculation shows that this method can effectively extract the weak nuclear magnetic signals in tunnel under strong background noise.

We report on a sapphire fiber Raman imaging probe's use for challenging applications where access is severely restricted. Small-dimension Raman probes have been developed previously for various clinical applications because they show great capability for diagnosing disease states in bodily fluids, cells, and tissues. However, applications of these sub-millimeter diameter Raman probes were constrained by two factors: first, it is difficult to incorporate filters and focusing optics at such small scale; second, the weak Raman signal is often obscured by strong background noise from the fiber probe material, especially the most commonly used silica, which has a strong broad background noise in low wavenumbers (<500-1700 cm-1). Here, we demonstrate the thinnest-known imaging Raman probe with a 60 μm diameter Sapphire multimode fiber in which both excitation and signal collection pass through. This probe takes advantage of the low fluorescence and narrow Raman peaks of Sapphire, its inherent high temperature and corrosion resistance, and large numerical aperture (NA). Raman images of Polystyrene beads, carbon nanotubes, and CaSO4 agglomerations are obtained with a spatial resolution of 1 μm and a field of view of 30 μm. Our imaging results show that single polystyrene bead (~15 µm diameter) can be differentiated from a mixture with CaSO4 agglomerations, which has a close Raman shift.

The fault feature extraction of rolling element bearings is of critical interest for fault diagnosis. The fault impulses are always buried in strong and complex background noise, which makes it hard to detect the fault characteristics and further diagnose the bearing. Many feature extraction techniques have been developed, which assumed that the noise obeys a single and straightforward Gaussian distribution. However, the noise is usually non-Gaussian and cannot be characterized by a single distribution in practical industrial scenarios. A fault feature extraction model for rolling bearings within complex noise is proposed in this article, where the complex noise is modeled by the Gaussian mixture model (GMM), thus highlighting the fault characteristics. The 2-D representation of the measurement is obtained by exploiting cyclic spectral analysis. Then, the measurement is further modeled as a low-rank faulty component and a complex noise component, where the noise is characterized by the GMM. Therefore, the model is named the GMM enable low-rank (GMM-LR) model. The variational Bayes inference method is employed to estimate the posterior of the proposed model, which can obtain the optimal solution to characterize the fault characteristics. Finally, the bearing fault features are detected by the enhanced envelope spectrum (EES). Both the synthetic and experimental signals are studied to demonstrate the efficacy of the proposed technique. The superiority is also validated by comparisons with envelope spectrum (ES), cyclic spectral analysis, spectral kurtosis (SK), and robust principal component analysis (RPCA).

In view of the problem that the fault signal of the rolling bearing is weak and the fault feature is difficult to extract in the strong noise environment, a method based on minimum entropy deconvolution (MED) and local mean deconvolution (LMD) is proposed to extract the weak fault features of the rolling bearing. Through the analysis of the simulation signal, we find that LMD has many limitations for the feature extraction of weak signals under strong background noise. In order to eliminate the noise interference and extract the characteristics of the weak fault, MED is employed as the pre-filter to remove noise. This method is applied to the weak fault feature extraction of rolling bearings; that is, using MED to reduce the noise of the wind turbine gearbox test bench under strong background noise, and then using the LMD method to decompose the denoised signals into several product functions (PFs), and finally analyzing the PF components that have strong correlation by a cyclic autocorrelation function. The finding is that the failure of the wind power gearbox is generated from the micro-bending of the high-speed shaft and the pitting of the #10 bearing outer race at the output end of the high-speed shaft. This method is compared with LMD, which shows the effectiveness of this method. This paper provides a new method for the extraction of multiple faults and weak features in strong background noise.

It is extremely challenging to rapidly and accurately extract target echo photon signals from massive photon point clouds with strong background noise without any prior geographic information. Herein, we propose a fast surface detection method realized by combining the improved density-dimension algorithm (DDA) and Kalman filtering (KF), termed the DDA-KF algorithm, for photon signals with a high background noise rate (BNR) to improve the extraction of surface photon signals from spacecraft platforms. The results showed that the algorithm exhibited good adaptability to strong background noise and terrain slope variations, and had real-time processing capabilities for massive photon point clouds in large-scale detection range without prior altitude information of target. Our research provides a practical technical solution for single-photon lidar applications in deep space navigation and can help improve the performance in environments characterized by strong background noise.

&lt;abstract&gt; &lt;p&gt;Rolling bear is a major critical component of rotating machinery, as its working condition affects the performance of the equipment. As a result, the condition monitoring and fault diagnosis of bearings get more and more attention. However, the strong background noise makes it difficult to extract the bearing fault features exactly. Furthermore, regular gradient disappearance and overfit appear in traditional network model training. Therefore, taking the printing press bearings as the research object, an intelligent fault diagnosis method based on strong background noise is proposed. This method integrates frequency slice wavelet transform (FSWT), deformable convolution and residual neural network together, and realizes the high-precision fault diagnosis of the printing press bearings. First, FSWT is used to preprocess the original vibration signal to obtain bearing fault features in the time and frequency domain, reconstruct the signal in any frequency band and describe local features accurately. Second, the ResNet is selected as the base network, and the two-dimensional time-frequency diagrams (TFD) obtained by preprocessing are used as input. For the model that has a poor ability to extract subtle features under strong background noise, the deformable convolution layer is introduced to reconstruct the convolution layer of ResNet, called deformable convolution residual neural network (DC-ResNet). Finally, the effectiveness of this method is verified by using the data sets collected under experimental conditions and actual working conditions for fault diagnosis of the printing press. The results show that the DC-ResNet can classify different bearing faults under strong background noise, and the accuracy and stability are greatly improved, which the accuracy meets 93.90%. The intelligent fault diagnosis with high-precision of printing press bearings under complex working conditions is realized by the proposed method.&lt;/p&gt; &lt;/abstract&gt;

Weak signal detection has garnered considerable attention in numerous research fields, especially weak signal detection under strong noise, which is an urgent problem researches are concerned with. In this paper, a new criterion to select singular values using correlation coefficients is proposed for detecting weak exponential damped sinusoidal signals. This method has a wide variety of signal processing applications. The innovation of our method lies in selecting the most informative singular values of K rather than the most energetic singular values. The proposed method measures the similarity between component signals and useful signals via the autocorrelation function and correlation coefficient, which can preserve more information of the original signal and be more suitable for weak signal detection scenarios under strong noise. Numerical experiments and analysis are performed to verify the efficiency and effectiveness of our method, and indicate that the presented method is superior to the singular value selection methods based on energy or simple difference principle for correlation coefficients. Compared to stochastic resonance methods suitable for weak signal detection under strong background noise, our proposed method also offer significant advantages. Thus, it is beneficial for theoretical analysis and engineering applications.

Adaptive thresholding and coordinate attention-based tree-inspired network for aero-engine bearing health monitoring under strong noise