Strong Noise Signals Research Articles

Toward accurate extraction of bearing fault modulation characteristics with novel time–frequency modulation bispectrum and modulation Gini index analysis

Rolling bearings are extremely critical rotating mechanical components, and when they fail, they can damage the equipment, causing safety threats or economic losses. Collecting and analyzing vibration signals of faulty bearings is a common fault diagnosis method. Modulation signal bispectrum (MSB) can utilize the inherent modulation characteristics of the signal to achieve fault feature recognition, but it is easily affected by strong background noise and other modulation signals, making it difficult to detect the fault feature frequency in the end. Based on this, a time–frequency modulation bispectrum (TFMB) method is proposed in this paper. The signal is first transformed into the time–frequency domain through short-time Fourier transform, retaining both time and frequency information. It reduces the amplitude of bispectral noise, highlights spectral lines related to fault information, and displays modulation information more clearly, making the demodulation results more accurate. Based on the characteristics of bispectrum, a two-dimensional singular value decomposition (2D SVD) strategy is designed to reduce the amplitude of the noise components in the bispectrum, and the relative change rate is proposed to automatically determine the number of effective singular values. At the same time, considering that the Gini index has good sparse property, the modulation Gini index (MGI) is proposed based on the Gini index, which can identify the repetitive pulses in the signal, suppress the random pulses and noise interference, and extract the modulation component. MGI can be used to evaluate the proportion of fault information contained in TFMB slices and select the optimal frequency slice. The simulation signal verifies the effectiveness of the method in fault diagnosis of rolling bearing, and the proposed method is applied to the diagnosis of rolling bearing outer ring fault, inner ring fault, and compound fault respectively. The experimental results show that the method can effectively reduce noise amplitude, enhance fault information in the bispectrum, and extract the optimal frequency slice to accurately detect bearing fault characteristic frequencies. The proposed method is compared with MSB detector, Fast Kurtogram and Autogram, which proves the superiority of TFMB method in extraction of rolling bearing fault characteristics.

Utilizing wavelet packet decomposition to locate the damage of composite structures in strong-noise environment

Abstract This paper proposes an approach for damage localization in a strong noise environment based on wavelet packet decomposition. Experiments were conducted to locate damage in composite structures to verify the method. The signal was decomposed by a wavelet packet, and the damage information was identified through wavelet packet energy ratio deviation, defined as the damage factor. The proposed method effectively localizes the damage and directly captures the original signal without the need to preprocess the strong noise signals. Additionally, it bypasses the complex process of extracting reflected signals, demonstrating substantial potential for detecting and identifying composite damage in noisy environments.

Dual-band filtering and enhanced directional via tunable acoustic metamaterial antennas

The detection of acoustic signals in strong background noise plays a crucial role in industrial non-destructive, mechanical equipment health monitoring and acoustic communication. The major bottleneck of this technology lies in the limited high-sensitivity and high-directivity of acoustic sensors. Here, this study proposes a tunable acoustic metamaterial antenna (TAMAA) with a double bandgap and near-zero refractive index. Different from the traditional geometric scatterer, a gear-shaped structure is introduced to enhance the controllability of the acoustic system. We theoretically demonstrate the physical properties of the structure with a double bandgap and near-zero refractive index. Remarkably, the gear-shaped honeycomb lattice structure exhibits an adjustable bandgap region, which enables the multiplexing of both acoustic shielding and acoustic enhancement functions by controlling the rotation angle of the scatterer. Furthermore, through numerical computational and experimental studies, we demonstrate that the proposed TAMAA exhibits dual-band filtering capabilities and provides excellent acoustic directional enhancement. Moreover, it allows for the recovery of weak acoustic signals even in the presence of extremely low signal-to-noise ratio and strong spatial noise interference. This work breaks through the detection limits of conventional acoustic sensing systems and provides new ideas for the development of acoustic sensing detection.

Interrupted sampling repeater jamming suppression based on multiple extended complex‐valued convolutional auto‐encoders

AbstractInterrupted sampling repeater jamming (ISRJ) with flexible modulation parameters and coherent processing gain seriously threatens the radar detection system. The jamming suppression and target detection performance of existing anti‐jamming methods are limited by strong noise and jamming signals. An ISRJ suppression method combining multiple extended complex‐valued convolutional auto‐encoders (CVCAEs) and compressed sensing (CS) reconstruction is proposed. For the different tasks of parameter estimation and signal denoising, the extended CVCAEs including a complex‐valued convolutional shrinkage network (CVCSNet) and a complex‐valued UNet (CVUNet) are developed. Based on the time‐domain discontinuity of ISRJ signals, the CVCSNet is first used to directly estimate the parameters representing signal components and extract jamming‐free signals from received signals. Then, the extracted signals are denoised using the CVUNet. After that, relying on the denoised signals and the frequency sparsity of de‐chirped target signals, a CS model is established and solved to recover complete target signals for jamming suppression. Utilising the advantages of deep neural networks in weak feature extraction and signal representation, the CVCSNet and CVUNet can effectively improve the signal extraction accuracy and alleviate the limitation of noise on target signal reconstruction. Experimental results verify that the proposed method has superior ISRJ suppression performance and is robust to varying signal‐to‐noise ratios, jamming‐to‐signal ratios and jamming parameters.

Hydraulic fault prediction of integrated transmission system based on LSTM

Integrated drive-line fluids play a role in transmitting torque, providing control pressure, lubrication, and cleaning in the integrated drive-train. The increase of oil abrasive particles will accelerate the wear rate of rotating parts, while insufficient oil will lead to rapid ablation and gluing of rotating parts. According to statistics, more than 75% of hydraulic equipment failure is caused by oil pollution. Oil pollution leads to filter blockage which will cause fatal failure so that the entire transmission system cannot work. Therefore, it is extremely important to predict the fault and health management of the hydraulic system. This paper studies the oil fault of the integrated transmission system, proposes a data preprocessing method, and solves the problem of difficult extraction of deterioration index in the multichannel strong noise signal of the hydraulic system through the information fusion algorithm and the mean smoothing algorithm. According to the abnormal data judgment criterion based on the sliding window, the identification of the early fault starting point of the hydraulic system is realized, which provides a basis for the start-up fault prediction and the division of normal and degraded states. A multi-channel fusion fault prediction algorithm for hydraulic systems based on a Long-Short Term Memory (LSTM) deep network is proposed, which realizes long-term and high-precision fault prediction of hydraulic systems. Finally, the oil failure prediction algorithm of the integrated transmission system is verified by using real vehicle fault data.

Towards precise complex AM-FM signals decomposition under strong noise conditions: A novel two-level chirp mode decomposition approach

Many signals in the real world contain noise, which interferes with the analysis of signals. Under harsh environments, the signal will be seriously disturbed by strong noise and overwhelm its changing features. Decomposing the signal with strong noise can effectively reveal the actual change law of original signal. However, the current methods cannot effectively process complex amplitude modulation-frequency modulation (AM-FM) signals with strong noise, and suffer from serious mode aliasing problems. In order to effectively decompose complex AM-FM signals with strong noise, a novel two-level chirp mode decomposition (TCMD) approach is proposed in this paper. TCMD can effectively solve the mode aliasing problem caused by strong noise interference and accurately decompose each sub-signal from original signal with strong noise. TCMD mainly includes two core parts: instantaneous frequency (IF) extraction and signal decomposition. Firstly, we establish a new IF extraction model for strong noise signals called improved parameterized time–frequency transform (IPTFT), which overcomes the shortcomings of current PTFT that cannot effectively handle multi-component signals. IPTFT can effectively filter out the interference of strong noise and accurately extract the initial IFs of multi-component signal. Then, we propose a novel adaptive iterative envelope filter tracking (AIETF) for signal decomposition that adopts the adaptive decomposition idea. AIETF can effectively decompose complex multi-component signals with strong noise, accurately estimate the IFs and reconstruct sub-signals. Decomposition experiments of both simulated and experimental signals with strong noise were carried out to verify proposed TCMD. The results show that TCMD can effectively decompose strong noisy AM-FM signals with close IFs, fast-changing IFs and crossing IFs. Meanwhile, TCMD can also effectively decompose the vibration signals with strong noise of wind turbine gearboxes, aero-engine gearboxes, and faulty bearings under non-stationary conditions to obtain high-quality TFR. Furthermore, TCMD achieves better decomposition effect for strong noise signals than the existing decomposition and TFT methods.

Denoising distributed acoustic sensing data using unsupervised deep learning

Distributed acoustic sensing (DAS) technology has been widely used in seismic exploration to acquire high-quality data due to its noteworthy advantages, such as high coverage, high resolution, low cost, and strong environmental friendliness. However, the seismic signals acquired in DAS are often masked by various types of noise (e.g., high-frequency random, high-amplitude erratic, horizontal, and coupled noise), which seriously decreases the signal-to-noise ratio. We develop a fully connected neural network with dense and residual connections to attenuate various complex noises in real DAS data. The network is designed to learn the features of useful reflection signals and remove various noises in an unsupervised way, therefore enjoying the convenience of label-free processing. Our network uses several encoders and decoders to compress and reconstruct the abstract waveform features, respectively. Each encoder/decoder consists of one dense block with stacked fully connected blocks (FCBs). To transfer the shallow-level features to the deep level for reuse, we add the skip connections with one FCB between the corresponding encoders and decoders. Our method provides encouraging results when applied to synthetic and real DAS data sets. Compared with several traditional and advanced deep-learning methods, our method can more effectively attenuate strong noise and better extract hidden signals.

A fault diagnosis for rolling bearing based on multilevel denoising method and improved deep residual network

Aiming at the problem that weak faults in rolling bearings make effective fault diagnosis difficult under strong noise, this paper proposes a multilevel denoising technology based on improved singular value decomposition (ISVD) and intrinsic timescale decomposition (ITD), combined with an improved deep residual network (ResNet), for fault diagnosis in rolling bearings. Firstly, the difference ratio (DR) index is introduced to optimize singular value decomposition, combined with ITD for multilevel denoising of strong noise signals. Effective fault information in bearing vibration signals is extracted and converted into grayscale images. Secondly, the multi-scale feature extraction module (MFE-Module) is introduced to enhance the feature extraction capability of ResNet, and the support vector machine (SVM) is used instead of the Softmax function to identify and classify the fault features. The experimental results indicate that, compared with other methods, the proposed method can more accurately realize the fault diagnosis of rolling bearings in strong noise environments.

Intelligent Fault Diagnosis Method through ACCC-Based Improved Convolutional Neural Network

Fault diagnosis plays an important role in improving the safety and reliability of complex equipment. Convolutional neural networks (CNN) have been widely used to diagnose faults due to their powerful feature extraction and learning capabilities. In practical industrial applications, the obtained signals always are disturbed by strong and highly non-stationary noise, so the timing relationships of the signals should be highlighted more. However, most CNN-based fault diagnosis methods directly use a pooling layer, which may corrupt the timing relationship of the signals easily. More importantly, due to a lack of an attention mechanism, it is difficult to extract deep informative features from noisy signals. To solve the shortcomings, an intelligent fault diagnosis method is proposed in this paper by using an improved convolutional neural network (ICNN) model. Three innovations are developed. Firstly, the receptive field is used as a guideline to design diagnosis network structures, and the receptive field of the last layer is close to the length of the original signal, which can enable the network to fully learn each sample. Secondly, the dilated convolution is adopted instead of standard convolution to obtain larger-scale information and preserves the internal structure and temporal relation of the signal when performing down-sampling. Thirdly, an attention mechanism block named advanced convolution and channel calibration (ACCC) is presented to calibrate the feature channels, thus the deep informative features are distributed in larger weights while noise-related features are effectively suppressed. Finally, two experiments show the ICNN-based fault diagnosis method can not only process strong noise signals but also diagnose early and minor faults. Compared with other methods, it achieves the highest average accuracy at 94.78% and 90.26%, which are 6.53% and 7.70% higher than the CNN methods, respectively. In complex machine bearing failure conditions, this method can be used to better diagnose the type of failure; in voice calls, this method can be used to better distinguish between voice and noisy background sounds to improve call quality.

Cyclic Symplectic Ramanujan Component Pursuit: Algorithm and applications

In recent years, signal decomposition method has been widely used in the engineering field to solve the problem of multimodal segmentation, such as Empirical Mode Decomposition (EMD), Ramanujan Mode Decomposition (RMD) and Adaptive Periodic Mode Decomposition (APMD) methods. Although the above methods have excellent decomposition performance, they still face many challenges for strong noise, coupled modes and other complex signals. Based on this, this paper proposes a new Cyclic Symmetric Ramanujan Component Pursuit (CSRCP) method, which can effectively decompose multi-frequency signals while protecting the integrity of the original information. First, to prevent information leakage, the circular matrix is constructed to protect the state information of the signal. Then, symplectic geometry similarity transformation is used to constrain signal decomposition, and multiple component signals with single frequency are obtained while noise reduction is performed. Finally, by constructing the Ramanujan subspace, the projection components in the Ramanujan subspace can be obtained, which enhances the extraction of periodic pulse information. The proposed method is verified by simulation and experimental signals, and the verification results show that CSRCP has superior decomposition performance.

Acoustic Emission Signal Denoising of Bridge Structures Using SOM Neural Network Machine Learning

Identification of noise signals is one of the most challenging problems in health monitoring of a bridge structure using acoustic emission (AE) monitoring and identification technology. Hardware filtering technology and spatial identification technologies are the most common methods used in identifying signals from the defects of the bridge, which have great limitations due to the presence of environmental noise. Therefore, this paper focuses on the AE noise signal from a bridge in an operation state and other specific loading states, which is diagnosed in the hardware filtering technology, spatial identification, and self-organizing map (SOM) neural network, to obtain the new noise recognition methods. It is found that the first two methods can indeed filter the noise signal, but the filtering rate can only reach about 50%, and can barely filter strong noise signals. The SOM neural network had strong self-recognition ability. The classification accuracy of simulated AE signals is 90% and 100%, respectively. The trained network was used to test 183 sample signals, and the defect signal detection accuracy reached 76% and 78.8%; therefore, the noise signal filtering effect is significantly improved.

LFM Signal Parameter Estimation via FTD-FRFT in Impulse Noise

LFM signals are widely applied in radar, communication, sonar and many other fields. LFM signals received by these systems contain a lot of noise and outliers. In order to suppress the interference of strong impulse noise on target signals and realize the accurate estimation of LFM signal parameters, the impulse noise of echo signals need to be filtered. In this paper, to solve the problem of poor performance of LFM signal parameter estimation based on fractional Fourier transform in impulse noise, alpha stable distribution is used to establish the mathematical model of impulse noise. The proposed fastest tracking differentiator with an adaptive tracking factor is used to suppress the strong impulse noise, and fractional Fourier transform is used to estimate the parameter of the LFM signals. The experimental results show that the proposed fastest tracking differentiator with an adaptive tracking factor has a good filtering performance. It can effectively filter the impulse noise in the echo signal and allows the FrFT method to accurate estimate the parameters of the LFM signals in strong impulse noise.

Application of enhanced empirical wavelet transform and correlation kurtosis in bearing fault diagnosis

The traditional empirical wavelet transform (EWT) based on the Meyer wavelet and scale-space method can decompose a signal into several empirical modes. However, this method is not effective in dealing with strong noise and non-stationary signals, which may lead to modal mixing or even decompose too many invalid components. For this purpose, a method based on the combination of enhanced empirical wavelet transform (EEWT) and correlation kurtosis (CK) is proposed in this paper. Firstly, the EEWT is used to segment the spectrum based on the characteristics of the spectrum fluctuations. It uses the minimum points of the envelope as the boundaries of the segmented spectrum. Secondly, a filter bank is constructed based on these boundaries and a maximum value order statistics filter segments the Fourier spectrum with the adaptive decomposition of the signals. Finally, the envelope spectrum generated by CK is used to screen the bearing fault information, which belongs to the decomposition of a signal into empirical modes, so that the rolling bearing fault can be accurately diagnosed. The method’s effectiveness is verified by simulated signal experiments and rolling bearing fault signals. The results show that the performance of the proposed method in this paper is better than that of the traditional EWT. Therefore, the method can be applied to the field of bearing faults or other mechanical fault diagnosis directions.

Maximum correlation Pearson correlation coefficient deconvolution and its application in fault diagnosis of rolling bearings

Blind deconvolution (BD) has been proved to be an effective tool for recovering periodic impulses from strong background noise signals is widely used for fault diagnosis of bearings and gears. However, the existing BD focus more on improving the quantity and amplitude-frequency characteristics of interested periodic impulses without considering their phase-frequency characteristics and impulse waveform characteristics (attenuation coefficient and oscillation frequency), which results in severe distortion of the reconstructed signal. In this scenario, a new BD method, named maximum correlation Pearson correlation coefficient deconvolution (MCPCCD), is presented in this paper. Firstly, a new objective function is constructed which include two terms: correlation Pearson correlation coefficient (CPC) and signal fidelity term (SFT). CPC is a new norm with strong sensitivity to interested impulse signals, while SFT is a dimensionless error measure similar to RMSE. Then, the optimal FIR filter is obtained by maximizing the objective function of the reconstructed signal. Meanwhile, a new preprocessing step is designed to reduce the requirement of MCPCCD for periodic prior. Finally, the application on simulated and two experimental signals indicates that MCPCCD significantly outperforms the traditional BD method in recovering the periodic impulses.

New Procedure for Estimation of Power Fundamental Phasor Parameters in Presence of Decaying DC Components

The paper proposes a new algorithm for the estimation of the fundamental phasor in a power system, based on the removal of exponentially decaying DC components (DDCs). These components, as well as high-order harmonics and noise components, have a considerable effect on accuracy and speed of convergence in numerical and digital relays – speed of the protection relay operation. A Discrete Fourier Transform (DFT) based approach with a modified Prony method was used to calculate and remove the unwanted effect of DDCs in a time interval slightly longer than the period of the fundamental harmonics. The proposed procedure offers the possibility to estimate the parameters of unwanted DDCs in a simpler and analytically more precise way, thus facilitating its program implementation. The algorithm offers the ability to easily adjust the response speed - detection time. This flexibility of the algorithm provides a compromise in terms of response speed as well as expected reliability and security of fault detection. The developed procedure enables the monitoring of the very demanding dynamics of the current signal in short-circuit conditions, and thus the estimation of the phasor parameters of the energy signal so that the relay protection can respond to this emergency in the most adequate (adaptive) way - it becomes more precise and faster in its response. The algorithm has low numerical and computational complexity while maintaining its high performance even in conditions of a very strong noise signal. The simulation results for different test signals demonstrate high precision in the estimation of the fundamental phasor of the proposed algorithm.

A deep residual shrinkage network based on multi-scale attention module for subsea Christmas tree valve leakage detection

Recently, the valve leakage detection of subsea Christmas tree (SCT) attracts considerable attention in the field of underwater resource exploitation. However, most existing leakage detection methods rely on contact-sensors, which are expensive and troublesome to install and maintain. Additionally, it is still a great challenge to extract sensitive fault features for the non-linear and unsteady signal. To address these issues, a novel remote acoustic detection method based on acoustic sensor and deep learning is proposed in this paper. Firstly, the feasibility of SCT valve leakage detection based on acoustic sensors is theoretically demonstrated by acoustic analysis. Secondly, a multi-scale attention module (MSAM) is proposed to obtain rich feature information according to the characteristics of valve leakage acoustic signals. Subsequently, A deep residual shrinkage network based on multi-scale attention module (MSAM-DRSN) is designed to perform valve leakage detection. The proposed method is evaluated by the valve leakage experiment. The results show that the proposed leakage detection method can obtain sensitive fault features from strong background noise signals and the detection performance is better than existing detection methods.

Application of Improved Phase Lock Method in Weak Signal Extraction

The existing method of the weak signal under the background of strong noise (signal amplitude relative to the noise amplitude is much smaller) analysis is not very ideal. This study proposes an improved phase lock method to analyze the weak periodic signal under Gaussian white noise. Experimental results show that this method can better analyze weak signal-related information than the stochastic resonance method in a certain range of signal-to-noise ratio, and the amount of calculation is small, and the correlation theory is simple and adapted to the rapid detection of the signal; in order to further reduce the amount of calculation, the introduction of a high-order accumulation of the signal presence detection, experiments verify that the high-order accumulation can detect whether there is a weak signal in the Gaussian noise. The combination of high-order cumulant and improved phase lock method has a good effect on the analysis of weak periodic signals under strong noise backgrounds.

Multi-lifting synchrosqueezing transform for nonstationary signal analysis of rotating machinery

Blurry time–frequency representation (TFR) of most time–frequency analysis methods influenced by inaccurate instantaneous frequency estimation hinders the meaningful rotating machinery fault detection. To address the drawback, a novel time–frequency analysis method called multi-lifting synchrosqueezing transform (MLST) is proposed, which could enhance the energy concentration of TFR for fast-varying instantaneous frequency and strong noise signals of rotating machinery. Hereinto, the critical points are improved for nonstationary signal analysis: (1) construct a multisqueeze second-order lifting operator to accurately estimate the fast-varying instantaneous frequency; (2) propose a correction operator to correct the deviation in the process of assigning time–frequency energy; (3) put forward a time–frequency fault measure cluster (TFMC) to optimize and evaluate the result of MLST. Through such multi-lifting operations, the final TFR with a high readability could provide a promising support for rotating machinery fault diagnosis. The repeatable simulations and engineering applications are used to verify the effectiveness of the method.

Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method

In recent years, as a simple and effective method of noise reduction, singular value decomposition (SVD) has been widely concerned and applied. The idea of SVD for denoising is mainly to remove singular components (SCs) with small singular value (SV), which ignores the weak signals buried in strong noise. Aiming to extract the weak signals in strong noise, this paper proposed a method of selecting SCs by the correntropy-induced metric (CIM). Then, the frequency components of characteristic signals can be found through cyclic correntropy spectrum (CCES) which is the extension of the correntropy (CE). The proposed method firstly merges the signals collected by the two channels, secondly uses the principal components analysis (PCA) method to reduce the dimensionality, thirdly uses the singular value decomposition method to decompose the signal, fourthly calculates the CIM value to determine the selected singular components for construction, and finally uses the cyclic correntropy spectrum displaying the characteristics of the reconstructed signal. The experimental results show that the proposed method has a good effect on feature extraction.

Characteristics and impact of environmental shaking in the Taipei metropolitan area

Examining continuous seismic data recorded by a dense broadband seismic network throughout Taipei shows for the first time, the nature of seismic noise in this highly populated metropolitan area. Using 140 broadband stations in a 50 km × 69 km area, three different recurring, strong noise signals characterized by dominant frequencies of 2–20 Hz, 0.25–1 Hz, and < 0.2 Hz are explored. At frequencies of 2–20 Hz, the seismic noise exhibits daily and weekly variations, and a quiescence during the Chinese New Year holidays. The largest amplitude occurred at a station located only 400 m from a traffic-roundabout, one of the busiest intersections in Taipei, suggesting a possible correlation between large amplitude and traffic flow. The median daily amplitude for the < 0.2 Hz and 0.2–1.0 Hz frequency bands is mostly synchronized with high similarity between stations, indicating that the sources are persistent oceanic or atmospheric perturbations across a large area. The daily amplitude for the > 2 Hz band, however, is low, indicating a local source that changes on shorter length scales. Human activities responsible for the 2–40 Hz energy in the city, we discovered, are able to produce amplitudes approximately 2 to 1500 times larger than natural sources. Using the building array deployed in TAIPEI 101, the tallest building in Taiwan, we found the small but repetitive ground vibration induced by traffic has considerable effect on the vibration behavior of the high-rise building. This finding urges further investigation not only on the dynamic and continuous interaction between vehicles, roads, and buildings, but also the role of soft sediment on such interaction.