Heavy Background Noise Research Articles

Stochastic resonance subject to multiplicative and additive noise: The influence of potential asymmetries.

The influence of potential asymmetries on stochastic resonance (SR) subject to both multiplicative and additive noise is studied by using two-state theory, where three types of asymmetries are introduced in double-well potential by varying the depth, the width, and both the depth and the width of the left well alone. The characteristics of SR in the asymmetric cases are different from symmetric ones, where asymmetry has a strong influence on output signal-to-noise ratio (SNR) and optimal noise intensity. Even optimal noise intensity is also associated with the steepness of the potential-barrier wall, which is generally ignored. Moreover, the largest SNR in asymmetric SR is found to be relatively larger than the symmetric one, which also closely depends on noise intensity ratio. In addition, a moderate cross-correlation intensity between two noises is good for improving the output SNR. More interestingly, a double SR phenomenon is observed in certain cases for two correlated noises, whereas it disappears for two independent noises. The above clues are helpful in achieving weak signal detection under heavy background noise.

Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive

As one of most critical component of high-speed locomotive, wheel set bearing fault identification has attracted an increasing attention in recent years. However, non-stationary vibration signal with modulation phenomenon and heavy background noise make it difficult to excavate the hidden weak fault feature. Variational Mode Decomposition (VMD), which can decompose the non-stationary signal into couple Intrinsic Mode Functions adaptively and non-recursively, brings a feasible tool. However, heavy background noise seriously affects setting of mode number, which may lead to information loss or over decomposition problem. In this paper, an independence-oriented VMD method via correlation analysis is proposed to adaptively extract weak and compound fault feature of wheel set bearing. To overcome the information loss problem, the appropriate mode number is determined by the criterion of approximate complete reconstruction. Then the similar modes are combined according to the similarity of their envelopes to solve the over decomposition problem. Finally, three applications to wheel set bearing fault of high speed locomotive verify the effectiveness of the proposed method compared with original VMD, EMD and EEMD methods.

Detecting Moving Object via Projection of Forward-Backward Frame Difference

Frame difference is a quick dissimilarity based segmentation approach for object detection, unfortunately, it gets trapped in over-segmented when the pixels of interest over time overlap each other. This paper presents a rather fast visual object detection approach capable of approximating the location of moving object under heavy background noise or big overlap caused by negative similarity. Specifically, frame forward-backward difference concept is proposed to extract object features in current frame through fusion of pixel-based current-previous and current-following frame difference. Based on this, we formulate object localization applying the statistics of horizontal-vertical projection of the fused difference. Therefore, our object detection can be regarded as a direct thresholding process which guarantees high efficiency while holds good accuracy performance. We evaluate our method on Weizmann human action dataset and some traffic videos for both single and multiple objects detection which demonstrates its applicability and prospect.

Weak-signal detection based on the stochastic resonance of bistable Duffing oscillator and its application in incipient fault diagnosis

Stochastic resonance (SR) is an important approach to detect weak vibration signals from heavy background noise and further realize mechanical incipient fault diagnosis. The stochastic resonance of a bistable Duffing oscillator is limited by strict small-parameter conditions, i.e., SR can only take place under small values of signal parameters (signal amplitude, frequency, and noise intensity). We propose a method to treat the large-parameter SR for this oscillator. The linear amplitude-transformed, time/frequency scale-transformed, and parameter-adjusted methods are presented and used to produce SR for signals with large-amplitude, large-frequency and/or large-intensity noise. Furthermore, we propose the weak-signal detection approach based on large-parameter SR in the oscillator. Finally, we employ two practical examples to demonstrate the feasibility of the proposed approach in incipient fault diagnosis.

Partly Duffing Oscillator Stochastic Resonance Method and Its Application on Mechanical Fault Diagnosis

Due to the fact that the slight fault signals in early failure of mechanical system are usually submerged in heavy background noise, it is unfeasible to extract the weak fault feature via the traditional vibration analysis. Stochastic resonance (SR), as a method of utilizing noise to amplify weak signals in nonlinear dynamical systems, can detect weak signals overwhelmed in the noise. However, based on the analysis of the impact of noise intensity on SR effect, it is concluded that the detection results are dramatically limited by the noise intensity of measured signals, especially for incipient fault feature of mechanical system with poor working environment. Therefore, this paper proposes a partly Duffing oscillator SR method to extract the fault feature of mechanical system. In this method, to locate the appearance of weak fault feature and decrease noise intensity, the permutation entropy index is constructed to select the measured signals for the input of Duffing oscillator system. Then, according to the regulation of system parameters, a reasonable match between the selected signals and Duffing oscillator model is achieved to produce a SR phenomenon and realize the fault diagnosis of mechanical system. Experiment results demonstrate that the proposed method achieves a better effect on the fault diagnosis of mechanical system.

Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution.

The early fault characteristics of rolling element bearings carried by vibration signals are quite weak because the signals are generally masked by heavy background noise. To extract the weak fault characteristics of bearings from the signals, an improved spectral kurtosis (SK) method is proposed based on maximum correlated kurtosis deconvolution (MCKD). The proposed method combines the ability of MCKD in indicating the periodic fault transients and the ability of SK in locating these transients in the frequency domain. A simulation signal overwhelmed by heavy noise is used to demonstrate the effectiveness of the proposed method. The results show that MCKD is beneficial to clarify the periodic impulse components of the bearing signals, and the method is able to detect the resonant frequency band of the signal and extract its fault characteristic frequency. Through analyzing actual vibration signals collected from wind turbines and hot strip rolling mills, we confirm that by using the proposed method, it is possible to extract fault characteristics and diagnose early faults of rolling element bearings. Based on the comparisons with the SK method, it is verified that the proposed method is more suitable to diagnose early faults of rolling element bearings.

Vibration sensor approaches for the sand detection in gas–sand two phases flow

The real-time measurement of solid phase in gas–sand flow is significant in the process of industrial production, especially for the economic benefit of natural gas production. Aiming to improve the existing limitations in solid particle detection of a gas conveying system, a vibration detection device for gas–solid flow has been developed and its evaluation test is conducted indoor. This paper illustrates the application of special wideband vibration sensor and sand mass flow computing method to monitor gas–sand flow in pipeline. In this work, a real-time computational formula for sand mass flow rate was firstly proposed. Both of the gas background noise reduction and the calibrated reference sand signals were considered in this formula. Besides, time–frequency analysis and characteristic sand frequency band digital filter methods were proposed to enhance the ability of sand detection in heavy gas flow background noise. The experimental investigation considered gas–sand flow both with sand content ranging from 0.0156vol.‰ to 0.1vol.‰ and sand size ranging from 45μm to 180μm. The results showed that there was a good correlation (less than ±8% uncertainties) between vibration energy and sand mass flow rate. Accordingly, the vibration technology provides a method for the detection of tiny particle size and dilute solid phase in solid–gas system, which lays the foundation for the quantitative particles detection in more complex multiphase flow.

Multi-frequency weak signal detection based on wavelet transform and parameter compensation band-pass multi-stable stochastic resonance

In actual fault diagnosis, useful information is often submerged in heavy noise, and the feature information is difficult to extract. A novel weak signal detection method aimed at the problem of detecting multi-frequency signals buried under heavy background noise is proposed based on wavelet transform and parameter compensation band-pass multi-stable stochastic resonance (SR). First, the noisy signal is processed by parameter compensation, with the noise and system parameters expanded 10 times to counteract the effect of the damping term. The processed signal is decomposed into multiple signals of different scale frequencies by wavelet transform. Following this, we adjust the size of the scaled signals' amplitudes and reconstruct the signals; the weak signal frequency components are then enhanced by multi-stable stochastic resonance. The enhanced components of the signal are processed through a band-pass filter, leaving the enhanced sections of the signal. The processed signal is analyzed by FFT to achieve detection of the multi-frequency weak signals. The simulation and experimental results show that the proposed method can enhance the signal amplitude, can effectively detect multi-frequency weak signals buried under heavy noise and is valuable and usable for bearing fault signal analysis.

Enhanced Rotating Machine Fault Diagnosis Based on Time-Delayed Feedback Stochastic Resonance

The fault-induced impulses with uneven amplitudes and durations are always accompanied with amplitude modulation and (or) frequency modulation, which leads to that the acquired vibration/acoustic signals for rotating machine fault diagnosis always present nonlinear and nonstationary properties. Such an effect affects precise fault detection, especially when the impulses are submerged in heavy background noise. To address this issue, a nonstationary weak signal detection strategy is proposed based on a time-delayed feedback stochastic resonance (TFSR) model. The TFSR is a long-memory system that can utilize historical information to enhance the signal periodicity in the feedback process, and such an effect is beneficial to periodic signal detection. By selecting the proper parameters including time delay, feedback intensity, and calculation step in the regime of TFSR, the weak signal, the noise, and the potential can be matched with each other to an extreme, and consequently a regular output waveform with low-noise interference can be obtained with the assistant of the distinct band-pass filtering effect. Simulation study and experimental verification are performed to evaluate the effectiveness and superiority of the proposed TFSR method in comparison with a traditional stochastic resonance (SR) method. The proposed method is suitable for detecting signals with strong nonlinear and nonstationary properties and (or) being subjected to heavy multiscale noise interference.

Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain

Fault identification timely of rolling mill drivetrain is significant for guaranteeing product quality and realizing long-term safe operation. So, condition monitoring system of rolling mill drivetrain is designed and developed. However, because compound fault and weak fault feature information is usually sub-merged in heavy background noise, this task still faces challenge. This paper provides a possibility for fault identification of rolling mills drivetrain by proposing customized maximal-overlap multiwavelet denoising method. The effectiveness of wavelet denoising method mainly relies on the appropriate selections of wavelet base, transform strategy and threshold rule. First, in order to realize exact matching and accurate detection of fault feature, customized multiwavelet basis function is constructed via symmetric lifting scheme and then vibration signal is processed by maximal-overlap multiwavelet transform. Next, based on spatial dependency of multiwavelet transform coefficients, spatial neighboring coefficient data-driven group threshold shrinkage strategy is developed for denoising process by choosing the optimal group length and threshold via the minimum of Stein’s Unbiased Risk Estimate. The effectiveness of proposed method is first demonstrated through compound fault identification of reduction gearbox on rolling mill. Then it is applied for weak fault identification of dedusting fan bearing on rolling mill and the results support its feasibility.

Vibration sensor approaches for sand detection in oil–water–sand multiphase flow

The real-time measurement of solid phase in oil–water–sand multiphase flow is significant in production process control and reliable operations, especially for reasonable sanding in oilfield. Aiming to improve the existing limitations in solid particle detection of a liquid–solid system, a vibration detection device for liquid–solid flow has been developed and its evaluation test is conducted indoor. This paper illustrates the application of a special wideband vibration sensor to monitor sand–water and sand–oil–water flows in pipeline. In this work, a primary measuring instrument was firstly proposed and designed to amplify vibration signal caused by sand impacting so that it can be much easier to receive for the sensor. Time–frequency analysis method and a characteristic sand frequency band digital filter were proposed to enhance the ability of sand detection in heavy liquid flow background noise. The experimental investigation considered sand–water flow and sand–oil–water flow, both with sand content more than 0.3wt.‰ and size less than 325mesh. The experimental findings showed that there is an obvious correlation between vibration power spectrum amplitude and sand concentration. Accordingly, the vibration technology would be helpful to enhance the solid detection for the multiphase system.

Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning

Impulse components in vibration signals are important fault features of complex machines. Sparse coding (SC) algorithm has been introduced as an impulse feature extraction method, but it could not guarantee a satisfactory performance in processing vibration signals with heavy background noises. In this paper, a method based on fusion sparse coding (FSC) and online dictionary learning is proposed to extract impulses efficiently. Firstly, fusion scheme of different sparse coding algorithms is presented to ensure higher reconstruction accuracy. Then, an improved online dictionary learning method using FSC scheme is established to obtain redundant dictionary and it can capture specific features of training samples and reconstruct the sparse approximation of vibration signals. Simulation shows that this method has a good performance in solving sparse coefficients and training redundant dictionary compared with other methods. Lastly, the proposed method is further applied to processing aircraft engine rotor vibration signals. Compared with other feature extraction approaches, our method can extract impulse features accurately and efficiently from heavy noisy vibration signal, which has significant supports for machinery fault detection and diagnosis.

Effects of underdamped step-varying second-order stochastic resonance for weak signal detection

Stochastic resonance (SR) has been proved to be an effective approach for weak signal detection. In this paper, an underdamped step-varying second-order SR (USSSR) method is proposed to further improve the output signal-to-noise ratio (SNR). In the method, by selecting a proper underdamped damping factor and a proper calculation step, the weak periodic signal, the noise and the potential can be matched with each other in the regime of second-order SR to generate an optimal dynamical system. The proposed method has three distinct merits as: 1) secondary filtering effect produces a low-noise output waveform; 2) good band-pass filtering effect attenuates the multiscale noise that locates in high- and (or) low-frequency domains; and 3) good anti-noise capability in detecting weak signal being submerged in heavy background noise. Numerical analysis and application verification are performed to confirm the effectiveness and efficiency of the proposed method in comparison with a traditional SR method.

Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction

Stochastic resonance (SR) is an important approach to detect weak vibration signals from heavy background noise. In order to increase the calculation speed and improve the weak feature detection performance, a new bistable model has been built. With this model, an adaptive and fast SR method based on dyadic wavelet transform and least square system parameters solving is proposed in this paper. By adding the second-order differential item into the traditional bistable model, noise utilization can be increased and the quality of SR output signal can be improved. The iteration algorithm for implementing the adaptive SR is given. Compared with the traditional adaptive SR method, this algorithm does not need to set up the searching range and searching step size of the system parameters, but only requires a few iterations. The proposed method, discrete wavelet transform and the traditional adaptive SR method are applied to analyzing simulated vibration signals and extracting the fault feature of a rotor system. The contrastive results verify the superiority of the proposed method, and it can be effectively applied to weak mechanical fault feature extraction.

Cascaded-Automatic Segmentation for Schistosoma japonicum eggs in images of fecal samples

BackgroundTo recognize parasite eggs automatically, the automatic segmentation of parasite egg images is very important for the extraction of characteristics and genera classification. MethodsA Cascaded-Automatic Segmentation approach was proposed. Firstly, image contrast between the border of an egg and its background for all samples was strengthened by the Radon-Like Features algorithm and the enhanced image was processed into a binary image to get an initial set. Then, the elliptical targets are located with Randomized Hough Transform (RHT). The fitted data of an elliptical border are considered the initial border data and the accurate border of a Schistosoma japonicum egg can be finally segmented using an Active Contour Model (Snake). ResultsSeventy-three cases of S. japonicum eggs in fecal samples were found; 61 images contained a parasite egg and 12 did not. Although the illumination, noise pollution, boundary definitions of eggs, and egg position are different, they are all segmented and labeled accurately. DiscussionThe results proved that accurate borders of S. japonicum eggs could be recognized precisely using the proposed method, and the robustness of the method is good even in images with heavy noise. This indicates that the proposed method can overcome the disadvantages of the traditional threshold segmentation method, which has limited adaptability to images with heavy background noise.

A New Cascaded Stochastic Resonance System and its Application to Weak Double-Frequency Signal Separation

Weak signal detection has attracted many researchers attention all over the world. Most of the methods focus on the single frequency signal. This paper introduces a new method to separate the weak double-frequency signal overwhelmed in heavy background noise. The cascaded stochastic resonance (SR) system (CSRS) is composed of a bistable SR model in the first stage and a tristable one in the second. Based on the characteristic of the SR system, we can amplify the useful signal of high frequency using twice sampling technic to make its parameters matching the requirements of the system. In our proposed cascade stochastic resonance system, we highlight the appointed frequency from high to low successfully with adjusting the twice sampling multiple and the high pass filter band. The signal composition of different frequency can be obtained from the systems two stage output. Simulated experiment validates the CSRSs availability in weak double-frequency signal separation and also its promising application in a more complex mixed signal.

Intelligent Mechanical Fault Diagnosis Based on Multiwavelet Adaptive Threshold Denoising and MPSO

The condition diagnosis of rotating machinery depends largely on the feature analysis of vibration signals measured for the condition diagnosis. However, the signals measured from rotating machinery usually are nonstationary and nonlinear and contain noise. The useful fault features are hidden in the heavy background noise. In this paper, a novel fault diagnosis method for rotating machinery based on multiwavelet adaptive threshold denoising and mutation particle swarm optimization (MPSO) is proposed. Geronimo, Hardin, and Massopust (GHM) multiwavelet is employed for extracting weak fault features under background noise, and the method of adaptively selecting appropriate threshold for multiwavelet with energy ratio of multiwavelet coefficient is presented. The six nondimensional symptom parameters (SPs) in the frequency domain are defined to reflect the features of the vibration signals measured in each state. Detection index (DI) using statistical theory has been also defined to evaluate the sensitiveness of SP for condition diagnosis. MPSO algorithm with adaptive inertia weight adjustment and particle mutation is proposed for condition identification. MPSO algorithm effectively solves local optimum and premature convergence problems of conventional particle swarm optimization (PSO) algorithm. It can provide a more accurate estimate on fault diagnosis. Practical examples of fault diagnosis for rolling element bearings are given to verify the effectiveness of the proposed method.

A New Image Denoising Method Based on Wave Atoms and Cycle Spinning

A new method for image denoising was presented，which colligated the strong point of wave atoms transform and Cycle Spinning. Due to lack of translation invariance of wave atoms transform，image denoising by coefficient thresholding would lead to Pseudo-Gibbs phenomena. Cycle Spinning was employed to avoid the artifacts. Experimental results show that the method can remove noisy and remain edges, while Pseudo-Gibbs phenomena are controlled efficiently, and can get better visual effect and PSNR gains compared with the methods like simplex wave atoms or wavelet denoising using Cycle Spinning.And in heavy background noise, this advantage is significant.

Multi-scale bistable stochastic resonance array: A novel weak signal detection method and application in machine fault diagnosis

Weak signal detection based on stochastic resonance (SR) can hardly succeed when noise intensity exceeds the optimal value of SR. This paper explores a novel parallel bistable SR array mechanism by decomposed multi-scale noises from input signal. A smoother output with lower noise is obtained from the combination of colored noise SR effect and parallel bistable SR array. The influence of noise intensity and array size on the SR effect and output noise intensity is analyzed through numerical simulation. A signal detection method based on the new SR mechanism and normalized scale transform is proposed for the case of heavy background noise. Simulation is conducted to confirm the effectiveness of parameter tuning and amplitude tuning of normalized scale transform on the proposed SR model. The proposed method has three advantages: the input noise intensity of each unit is reduced by wavelet decomposition; the output noise level decreases due to array ensemble average; the SR effect of each unit is optimized by normalized scale transform for high frequency signal. Experiment on bearing inner and outer race fault diagnosis has verified the effectiveness and advantages of the proposed SR model in comparison with traditional SR method and kurtogram.

Compressed sensing based on dictionary learning for extracting impulse components

It is essential to extract impulse components embedded in heavy background noise in engineering applications. The methods based on wavelet have obtained huge success in removing noises, leading to state-of-the-art results. However, complying with the minimum noise principle, the shrinkage/thresholding algorithms unreasonably remove most energy of the features, and sometimes even discard some important features. Thus it is not easy to guarantee satisfactory performance in actual applications. Based on a recently proposed theory named compressed sensing, this paper presents a new scheme, Sparse Extraction of Impulse by Adaptive Dictionary (SpaEIAD), to extract impulse components. It relies on the sparse model of compressed sensing, involving the sparse dictionary learning and redundant representations over the learned dictionary. SpaEIAD learns a sparse dictionary from a whole noisy signal itself and then employs greedy algorithms to search impulse information in the learned sparse dictionary. The performance of the algorithm compares favourably with that of the mature shrinkage/thresholding methods. There are two main advantages: firstly, the learned atoms are tailored to the data being analyzed and the process of extracting impulse information is highly adaptive. Secondly, sparse level of representation coefficients is promoted largely. This algorithm is evaluated through simulations and its effectiveness of extracting impulse components is demonstrated on vibration signal of motor bearings. The advantage of SpaEIAD is further validated through detecting fault components of gearbox, which illustrates that SpaEIAD can be generalized to engineering application, such as rotating machinery signal processing.