Heavy Background Noise Research Articles

Research on multi-source sparse optimization method and its application on gearbox compound fault detection

In general, gearbox is prone to occur compound fault frequently because of its harsh working environment, its fault vibration signal often contains polymorphic-oscillatory components and is corrupted by heavy background noise, which brings great difficulty to diagnose fault. Sparse decomposition is often utilized to extract weak fault feature among heavy background noise. In order to solve the problems of traditional sparse decomposition method, such as lacking signal fidelity, causing local optimal solution by using the non-convex objective function, and presenting poor universality, a multi-source sparse optimization objective function with convexity is constructed based on the generalized mini-max concave penalty function. By using forward–backward splitting algorithm combination with Laplace wavelet dictionary, Morlet wavelet dictionary and DFT dictionary, the sparse coefficients corresponding to polymorphic-oscillatory components can be computed efficiently and each oscillatory component can be extracted accurately. Finally, simulation and experimental signal validate that the proposed method can decompose fault signal according to oscillatory property and diagnose gearbox compound fault without the prior knowledge of specific fault numbers.

Isolation and identification of rolling bearing compound faults based on adaptive periodized singular spectrum analysis and Rényi entropy

Due to the coupling of multiple fault feature information and contamination of heavy background noise, it is a challenging task to accurately identify rolling bearing compound faults (RBCFs). A method for isolating and identifying the RBCF is proposed by integrating adaptive periodized singular spectrum analysis (APSSA) with Rényi entropy (RE). The adaptive selection of the embedding dimension of the Hankel matrix in APSSA without setting parameters empirically is proposed, and a selection criterion for singular values is established to preprocess the vibration signals of the rolling bearing and enhance the periodic component of the fault. An RE-based threshold value is introduced to further isolate and decouple the impulse segments of the vibration signal in the time domain. By considering the inner raceway fault, outer raceway fault, ball fault, and skidding, a comprehensive simulation model of the compound fault is constructed by the response mechanism of different excited resources. Simulated and experimental data are applied to validate the effectiveness and practicability of the proposed method. The results demonstrate that the RBCF can be identified correctly by the proposed method under strong background noise.

Single acoustic vector sensor DOA enhanced by unsaturated bistable stochastic resonance with linear amplitude response constrained

Acoustic vector sensors (AVS) are increasingly utilized for remote target passive perception, but accurate direction-of-arrival (DOA) estimation remains a formidable challenge, particularly in the presence of heavy background noise. In this paper, we propose a novel DOA estimation method that utilizes complex acoustic intensity measurements (CAIM) and unsaturated bistable stochastic resonance with linear amplitude response constrained (LAR-UBSR). The proposed method aims to exploit the excellent denoising performance of stochastic resonance (SR) to enhance the accuracy of DOA estimation. The proposed LAR-UBSR method is based on the theoretical analysis of the signal pre-processing requirements of CAIM, which determines the linear amplitude response and fixed phase shift. LAR-UBSR achieves a stable phase shift and linear amplitude response by utilizing the unsaturated potential well and limited potential parameters adjustment method of SR. We analyze the amplitude response linearity of LAR-UBSR under different signal-to-noise ratios (SNRs) and a wide range of frequencies, demonstrating a larger linear amplitude response interval that meets the signal pre-processing requirements of CAIM. Numerical analysis and application verification show that the proposed DOA method outperforms existing methods, particularly under low SNR conditions. The results suggest the potential for high-accuracy DOA estimation using nonlinear signal processing strategies in the presence of heavy background noise.

A New Approach to Detect Power Quality Disturbances in Smart Cities Using Scaling-Based Chirplet Transform with Strategically Placed Smart Meters

The growth of Internet of Things (IoT)-enabled devices has increased the amount of data created by the distribution network’s periphery nodes, requiring more data transfer capacity. Recent applications’ real-time requirements have strained standard computing paradigms, and data processing has struggled to keep up. Edge computing is employed in this research to detect distribution network faults, allowing for instant sensing and real-time reaction to the control room for faster investigation of distribution problems and power outages, making the system more reliable. Moreover, to overcome the challenges of fault detection, advanced signal processing methods need to be integrated with the Adaboost classifier. An Adaboost-based edge device, suitable for installation on top of a power pole, is proposed in this research as a means of real-time fault detection. To increase throughput, decrease latency and offload network traffic, data collecting, feature extraction and Adaboost-based problem identification are all performed in an integrated edge node. Enhanced detection accuracy (98.67%) and decreased latency (115.2 ms) verify the effectiveness of the suggested approach. In this research, we enhance the classical chirplets transform to create the scaling-basis chirplet transform (SBCT) for time–frequency (TF) analysis. This approach modulates the TF basis around the relevant time function to modify the chirp rate with frequency and time. By carefully selecting the sampling frequency, it is possible to discriminate between short circuit fault and high-impedance fault (HIF) by calculating spectral entropy. The TF representation obtained with the SBCT provides considerably higher energy concentrations, even for signals with numerous components, closely spaced frequencies and heavy background noise.

Feed-forward cascaded stochastic resonance and its application in ship radiated line signature extraction

Extracting ship-radiated line signatures from intense background noise presents a significant challenge in remote passive sonar detection and identification. While stochastic resonance (SR) has shown promise for enhancing signal-to-noise ratio (SNR), cascaded stochastic resonance (CSR) offers a superior extension by gradually transitioning energy from high to low frequencies, resulting in smoother waveforms and more evident signatures. However, CSR relies heavily on the first level and lacks robustness, especially at slightly lower SNR. To overcome these limitations, we propose a feed-forward cascaded stochastic resonance (FCSR) method that leverages complete target signal information in each level and superimposes it with the output from the last level, leading to gradual improvements in SNR with high robustness. The superposition weights are designed as a function of the number of cascaded levels with an increasing trend to optimize the output of the entire cascaded system. Furthermore, a phase alignment strategy was developed to improve the superposition process. Through theoretical analysis, we demonstrate the effectiveness of the proposed FCSR method. Further simulation analyses demonstrates that FCSR outperforms CSR, with a remarkable 18 dB improvement in filtering performance under low SNR conditions, an average anti-noise ability enhancement of over 10 dB, and a robustness improvement exceeding 30% at −30 dB. We also validate the practicality and effectiveness of our proposed method through application verification, exhibiting excellent enhancement performance. This study illuminates the importance of reutilizing complete target signal information and emphasizes the potential of cascaded systems to extract signatures from heavy background noise.

Minimum entropy deconvolution enhanced by KLOF and phase editing for fault diagnosis of rotating machinery

Repetitive impulses are critical characteristics in vibration signals of fault rotating machinery and minimum entropy deconvolution (MED) is an effective tool to extract these characteristics for fault diagnosis. However, early fault-induced impulses are usually weak and submerged in heavy background noise when rotating machinery operate in severe environment. Under this circumstance, MED generally fails to isolate useful impulses from contaminated signals. To overcome the shortcoming, this paper proposed a kernel local outlier factor MED enhanced by phase editing method (KPMED) for fault diagnosis of rotating machinery. First, non-Gaussian noise components in fault signals are removed by kernel-based local outlier factor (KLOF) in data cleaning way. Second, the residual Gaussian noise components are eliminated by phase editing (PE) in complex domain with phase perspective. Finally, the fault-induced repetitive periodic impulses are extracted by selecting MED filtered signal with large value of kurtosis for diagnosis. The effectiveness of KPMED is validated by a simulated bearing fault signal and two vibration signals collected from a pinion of a wind turbine and a rolling element bearing, respectively. Compared with original MED and its corresponding variants, KPMED is more accurate and suitable for fault diagnosis of rotating machinery under severe working conditions.

Dual-kernel driven convolutional sparse learning for bearing transient feature enhancement

The transient structure feature of bearing reflects the dynamic response of vibration, which is of vital importance for rotating machinery fault diagnosis. However, the measured signals are complicated and non-stationary, where the transient impacts are always submerged in heavy background noise. In this study, a novel dual-kernel driven convolutional sparse learning (DKCSL) is proposed to enhance the transient feature with the intrinsic structure mined. Considering the benefits of data-driven representation with time–frequency manifold (TFM) learning and model-driven representation with wavelet analysis, two time–frequency kernels with different principles are constructed for 2-dimensional convolutional sparse learning (CSL) in a fusion way of both data-driven and model-driven. From the perspective of image processing, the representation of desired transient structure features can be well sensed from time-frequency distribution (TFD) by those two dual-driven time–frequency kernels. In this manner, the desired defective intrinsic features can be mined, which is consistent with the physical dynamic response. In addition, image entropy is introduced to output the optimal TFM kernel and a series of inverse transform are applied to obtain the enhanced signal waveform. Simulated and experimental data are tested here to verify the creativity and effectiveness of the proposed DKCSL method, and a comparison with the traditional single TFM kernel-based sparse result and wavelet kernel-based sparse result are further provided, which deeply indicates the originality of our method that its potential in intrinsic structure feature extraction with dual-kernel sensing for bearing fault diagnosis.

A Hybrid Fault Diagnosis Method Using Translation Invariant Wavelet Denoising, Hierarchical Entropy, and Support Vector Machine with PSO Algorithm

The rolling element bearing is used in various machinery and produces vibration due to imperfections, surface irregularities during manufacture, damaged bearings, and inaccuracies in the allied element. Also, the rolling element bearing vibration generally shows non-linear dynamic characteristics and is masked with heavy background noise. This noble investigation advances a hybrid technique for removing background noise from the vibration signal and detecting bearing defects. Translation invariant wavelet denoising is the initial stage in this hybrid method for noise removal from the signal. The second phase uses Hierarchical Entropy (HE) for defect feature frequency extraction. Hierarchical entropy at scale four and SampEns of eight hierarchical decomposition nodes was utilized to determine the defect feature vector. In particular, low-frequency components are investigated through multi-scale entropy (MSE), but hierarchical entropy (HE) incorporates low-frequency and high-frequency components and can extract more defective information. Implemented a multi-class support vector machine (SVM) for extracting Hierarchical entropy as feature vectors. These feature vectors are trained by utilizing particle swarm optimization (PSO). To accomplish a prediction model, examine the optimal SVM parameters and then various bearing conditions with the variation of type, size, speed, and load severity identified by SVM. The investigation results show that hierarchical entropy can adequately and more precisely express the features of bearing vibration signals. It is beyond MSE, and the proposed Nobel hybrid Translation invariant wavelet denoising and Hierarchical entropy-based method will effectively remove the noisy background signal. Also, it distinguishes different bearings successfully, indicates the bearing conditions correctly, and is more prominent than those found on MSE.

Identification of initial fault time for bearing based on monitoring indicator, WEMD and Infogram

Rolling element bearing is a core component in the rotating machine. The performance of the whole machine is mainly dominated by the performance condition of the rolling element bearing. The Initial Fault Time (IFT) is a beginning landmark of the unhealthy condition of bearings. In order to identify accurately and rapidly the IFT under the weak fault signatures and heavy background noise, an identification method of the IFT is proposed by the monitoring indicator and envelope analysis with Weighted Empirical Mode Decomposition (WEMD) and Infogram. The monitoring indicator is constructed by the variation coefficient of the summation of the multiple standardized statistical features of the vibration signal. The approximate IFT can be obtained by the minimum before the early stage of the continuous increase in the monitoring indicator. Whereafter, a more accurate IFT can be detected by envelope analysis with WEMD and Infogram based on interval-halving backtracking strategy. The proposed method is verified by the tested dataset provided by Intelligent Maintenance System (IMS). The results show that the proposed method is efficient, rapid and simple for identifying the IFT.

Sparsity enforced time–frequency decomposition in the Bayesian framework for bearing fault feature extraction under time-varying conditions

Fault characteristic extraction of rolling bearings is essential for fault diagnosis. Rolling bearings are usually operated at changing speeds, and the nonstationary signals of the bearings are covered by the heavy background noise, making the extraction task of fault features very difficult. To address this issue, a robust fault characteristic extraction approach based on the time–frequency analysis under variable speed conditions is proposed in this paper. Firstly, the sparse property of the time-variant fault characteristics and low-rankness of background noise are explored and utilized in the time–frequency representation (TFR). Then, the sparse and the low-rank components are integrated into a hierarchical Bayesian model, and a random error term is considered to make the Bayesian model more robust. The Gibbs sampler is applied to extract the desired sparsity-enhanced component of the TFR in the Bayesian framework. Eventually, the time–frequency reassignment technique is adopted further to optimize the time–frequency resolution of the sparse component. Two simulated scenarios and a real-data experiment are used to evaluate the suggested approach’s performance. It turns out that the proposed approach is robust to noise and can extract the bearing time-varying fault features effectively.

Few-shot bearing fault diagnosis based on meta-learning with discriminant space optimization

In practical industrial applications, the collected fault data are usually insufficient due to the sudden occurrence of faults. However, the current deep-learning-based fault diagnosis methods often rely on a large number of samples to achieve satisfactory performance. Moreover, the heavy background noise and the variability of working conditions also degrade the performance of existing fault diagnostic approaches. To address these challenges, a new fault diagnosis method for few-shot bearing fault diagnosis based on meta-learning with discriminant space optimization (MLDSO) is proposed in this research. First, the fault feature of the rolling bearing is extracted through the tailored networks. Then, the feature extractor is optimized by the discriminant space loss proposed in this paper, to promote the clustering of the extracted fault features of the same category and to distinguish between different types of fault features. Next, the feature extractor and discriminant space optimizer are constructed to optimize the feature discriminant space; thus, a high fault-tolerant discriminant space is obtained for meta-learning. Eventually, the faults in the new task can be accurately classified with the assistance of previously learned meta-knowledge and a few known samples when dealing with new tasks under different working conditions. The effectiveness and superiority of the proposed MLDSO method are verified via the datasets collected from our self-designed experimental platform and the Case Western Reserve University test platform. The experimental results show superior performance over the advanced methods. This indicates that the proposed method is a promising approach under small sample situations, heavy noise, and variable working conditions.

Multi-sparsity-based blind deconvolution and its application to wheelset bearing fault detection

Fault detection of wheelset bearing is important to maintain the safety of high-speed train. The key in detecting the fault is to recover repetitive transients under heavy background noise from wheel/rail contacting. In this paper, a multi-sparsity-based blind deconvolution technique is proposed for vibration fault detection. Instead of maximizing impulsiveness nor cyclostationarity in finding an optimal filter, a comprehensive and compromised consideration of these two aspects is taken to increase the sparsity of time- and frequency-domain simultaneously. Based on this framework, smoothness index and Gini index are used in measuring the multi-sparsity. Then a knee point-driven strategy is incorporated to extend particle swarm optimization algorithm to solve the multi-objective blind deconvolution. The proposed approach is validated using a simulated signal and two cases of real wheelset bearing vibration signals. The comparative studies also demonstrate its robustness than some peer blind deconvolution techniques as well as envelope analysis methods.

Active acoustic scene monitoring through spectro-temporal modulation filtering for intruder detection.

An indoor acoustic scene monitoring system using a periodic impulse signal was previously developed. Compared with the impulse signal, the chirp signal is more robust against environmental noise due to its specific spectro-temporal structure. Such specific structure makes the chirp sound easily detected using a spectro-temporal modulation filtering mechanism. In this paper, we demonstrated a system that employs a two-dimensional spectro-temporal filtering mechanism on a Fourier spectrogram to measure the total energy of the reverberations of the chirp signal as the representation of the acoustic scene. The system compares the reverberation energy difference between consecutive chirps with a predefined threshold to automatically detect the change in the acoustic scene. Simulations were conducted in real living rooms with various types of background noise. Test results demonstrated that the proposed system is much more effective than previously developed systems for detecting the acoustic scene changes due to the intruder silently walking in the rooms. In the biggest test room (18 × 9.8 × 2.5 m3) with heavy background noise, the proposed system can still yield a correct identification rate higher than 80% to the intruder walking at 7 m from the microphone without producing any false alarms.

Varying-parameter time-frequency manifold for gearbox fault diagnosis

Abstract The vibration signals of a faulty gearbox are non-stationary and contaminated by heavy background noise. Time-frequency transform is able to present the non-stationary fault impulsive features in the time-frequency distribution (TFD). However, the time-frequency fault information is still contaminated by the noise. This paper proposes a varying-parameter time-frequency manifold (VPTFM) method with the aim to remove the noise in the TFD for accurate identification of gearbox fault. First, a high-dimensional TFD is constructed by performing short- time Fourier transform (STFT) using some variable window lengths. Then, local tangent space alignment (LTSA) algorithm is carried out on the high-dimensional TFD to extract the manifold of the fault impulsive features with two dimensions, in which Rényi entropy is employed to select the proper neighborhood size for the LTSA by evaluating the first dimensional manifold. Afterwards, a threshold is designed by exploring the characteristics of the amplitudes of the manifold at two dimensions to adaptively remove the noise survived in the first dimensional manifold. Finally, the amplitudes at the frequency possessing the largest energy in the denoised manifold are taken out for spectrum analysis to identify the fault characteristic frequency. The enhanced performance of the proposed method in extraction of fault impulses and removal of background noise is validated by a gearbox experimental vibration signal measuring when the gear has a wearing fault.

Fault feature extraction of rolling element bearings based on short-time processing

Fault diagnosis of bearings is a crucial part of the maintenance process of the rotary machinery. Extracting the cyclic characteristics of the impact force is of significant importance for the bearing diagnosis. To highlight the fault features from signals combined with heavy background noise, a novel approach for bearing fault diagnosis based on the short-time processing is proposed. Fault signals are regarded as periodic impulse response signals. Firstly, a vibration signal is band-pass filtered with a subsequent spectral analysis. Then we integrate the energy of the filtered signal with a constant length, and the natural logarithm is considered to obtain the energy curve. The energy curve is a straight decaying curve, and its spectral energy is more concentrated on the fault characteristic frequency compared with envelope. Finally, the fault characteristic frequency of the bearing is found by the spectral analysis of the energy curve. The effectiveness of the proposed method is verified by simulation and experiments. The harmonics and sidebands in logarithmic energy spectrum are suppressed well, and the fault characteristic frequency is highlighted. Comparison of the proposed method with Hilbert envelope method shows that the proposed method can highlight the fault characteristic frequency.

Fast Spectral Correlation Detector for Periodic Impulse Extraction of Rotating Machinery

Periodic impulse components are a typical symptom of rotating machinery failure, which are often masked by heavy background noise. It is of great practical significance to research how to obtain periodic impulse components to achieve fault diagnosis of rotating machinery. Fast spectrum correlation (Fast-SC), as a typical non-stationary and nonlinear signal processing method, has been studied in feature extraction by suppressing background noise to enhance periodic impulse components. However, effectively determining the bandwidth of Fast-SC is still a challenging issue. To address this issue, a novel method called Fast-SC detector is developed, which decomposes the measured signal of rotating machinery into multiple Fast-SC slices with different frequency bands. The whale optimization algorithm (WOA) is utilized to optimize the number of Fast-SC slices with minimum mean entropy as the optimal Fast-SC bandwidth. In addition, an adaptive combination morphological filter (ACMF) is applied to suppress the residual noise and narrowband impulses in the WOA-based Fast-SC slices to enhance the fault features. Finally, the Fast-SC detector is constructed using the average denoising Fast-SC slices to infer the type of rotating machinery. The proposed Fast-SC detector is compared with existing methods by applying simulation signal model and experimental data. The results prove that Fast-SC detector is superior to some excellent periodic impulse extraction algorithms in extracting the symptoms of rotating machinery failure.

Infrared Small UAV Target Detection Based on Depthwise Separable Residual Dense Network and Multiscale Feature Fusion

Unmanned aerial vehicles (UAVs) have been widely applied in military and civilian fields, but they also pose great threats to restricted areas, such as densely populated areas and airports. Thermal infrared imaging technology is capable of monitoring UAVs at a long range in both day and night conditions. Therefore, the anti-UAV technology based on thermal infrared imaging has attracted growing attention. However, the images acquired by infrared sensors often suffer from small and dim targets as well as heavy background clutter and noise. Conventional detection methods usually have a high false alarm rate and low detection accuracy. This paper proposes a detection method that formulates the UAV detection as predicting the residual image (i.e., background, clutter, and noise) by learning the nonlinear mapping from the input image to the residual image. The UAV target image is obtained by subtracting the residual image from the input infrared image. The constructed end-to-end U-shaped network exploits the depthwise separable residual dense blocks in the encoder stage to extract the abundant hierarchical features. Besides, the multi-scale feature fusion and representation block is introduced to fully aggregate multi-scale features from the encoder layers and intermediate connection layers at the same scale as well as the decoder layers at different scales to better reconstruct the residual image in the decoder stage. In addition, the global residual connection is adopted in the proposed network to provide long-distance information compensation and promote gradient backpropagation, which further improves the performance in reconstructing the image. The experimental results show that the proposed method achieves favorable detection performance in real-world infrared images and outperforms other state-of-the-art methods in terms of quantitative and qualitative evaluation metrics.

Adaptive adjacent signal difference lasso for bearing fault detection

Extraction of the fault-related periodic impulses from vibration signals with heavy background noise is fundamental but difficult for bearing fault diagnosis. In this paper, a novel fault feature extraction method based on a new sparse representation, adaptive adjacent signal difference lasso (AdaASDL) is proposed for bearing fault diagnosis. AdaASDL model is composed of l1-norm sparse regularization term and adjacent signal difference sparse regularization term, which are used to enhance the sparsity of the signal amplitude and adjacent signal difference, respectively. Moreover, a weighted method is proposed for setting the regularization parameter adaptively. The majorization–minimization (MM)-based optimization algorithm is derived to solve the objection function optimization of AdaASDL. In comparison with other state-of-the-art methods including the conventional sparse representation and spectral kurtosis (SK), AdaASDL has the better denoising performance on simulation signals and bearing vibration signals and the comparison results demonstrate the superiority of AdaASDL in bearing fault diagnosis.

Infrared small target detection algorithm based on spatial dissimilarity weighted local contrast

To solve the problem that the local contrast algorithm is easily influenced by the heavy clutter background or strong noise and does not fully consider the spatial neighbourhood characteristics of the small target, a spatial dissimilarity weighted local contrast-based method (SDWLCM) for infrared small target detection is proposed in this paper. Firstly, the two-dimensional difference of the Gaussian filter with central excitation and lateral suppression characteristics is chosen to preprocess the original infrared image for removing the flat background and improving the signal-to-noise ratio (SNR) of the small target. Secondly, the spatial dissimilarity between the target and its surrounding backgrounds is designed for local contrast weighting to generate the contrast saliency map so that the heavy clutter background is greatly suppressed and the small target is further highlighted. Thirdly, the saliency map is segmented by the adaptive threshold to get the real targets. Experimental results show that, compared with other methods, the SDWLCM, which owns not only a higher SNR gain and a larger background suppression factor but also a higher detection rate and a lower false alarm rate, is confirmed to be an effective method to detect the small targets.

A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis

Being an effective methodology to adaptatively decompose a multi-component signal into a series of amplitude-modulated-frequency-modulated (AMFM) sub-signals with limited bandwidth, the variational mode decomposition (VMD) has received increasing attention in the diagnosis of rolling element bearings. In implementing VMD, an optimal determination of decomposition parameters, including the mode number and bandwidth control parameter, is the pivotal starting point. However, in practical engineering, heavy background noise, abnormal impulses and vibration interferences from other internal components, often bring great challenges in selecting mode number and bandwidth control parameter. These issues may lead to the performance degradation of VMD for bearing fault diagnosis. Therefore, a fault information-guided VMD (FIVMD) method is proposed in this paper for extracting the weak bearing repetitive transient. To minimize the effects of background noise and/or interferences from other components, two nested statistical models based on the fault cyclic information, incorporated with the statistical threshold at a specific significance level, are used to approximately determine the mode number. Then the ratio of fault characteristic amplitude (RFCA) is defined and utilized to identify the optimal bandwidth control parameter, through which the maximum fault information is extracted. Finally, comparisons with the original VMD, empirical mode decomposition (EMD) and local mean decomposition (LMD) are conducted using both simulation and experimental datasets. Successful fault diagnosis of rolling element bearings under complicated operating conditions, including early bearing fault signals in run-to-failure test datasets, signals with impulsive noise and planet bearing signals, demonstrates that the proposed FIVMD is a superior approach in extracting weak bearing repetitive transients.