Strong Noise Interference Research Articles

A maximum cyclic impulses energy ratio deconvolution fused with enhanced envelope derivative operator frequency spectrum for early fault diagnosis of rolling bearing

Blind deconvolution is a powerful tool for rolling bearing fault diagnosis. As one of deconvolution methods, maximum second-order cyclostationarity blind deconvolution (CYCBD) is proved to be effective in extracting bearing fault characteristics. However, the performance of CYCBD method is greatly compromised by setting of fault characteristic frequency (FCF) in advance. Moreover, its performance decreases dramatically under the interference of random shocks and strong noise. To address these issues, a new deconvolution method, named as maximum cyclic impulses energy ratio deconvolution (MCIERD) fused with enhanced envelope derivative operator frequency spectrum (EEDOFS) is proposed in this research. In this method, the EEDOFS is proposed to estimate the FCF. Furthermore, the cyclic impulses energy ratio (CIER) is employed as the deconvolution indicator. Moreover, the hybrid firefly and particle swarm optimization algorithm is used to solve the optimal filter coefficients by maximizing the CIER. Simulation results show that EEDOFS exhibits a greater robustness in estimating FCF accurately under strong interferences and MCIERD performs well in extracting fault cyclic impulses under the interference of heavy noise and random shocks. Finally, three run-to-failure bearing datasets are employed for experimental validation, whose results demonstrate the effectiveness of EEDOFS in accurate estimating FCF and identifying the early bearing fault. Meanwhile, MCIERD fused with EEDOFS is proved to have greater advantages in extracting early bearing fault feature.

Semi-supervised diagnosis method of refrigeration compressor hidden defect based on convolutional transformer autoencoder model

Improving product quality and service life by diagnosing defects in compressors through vibration signal analysis of the compressor's shell is challenging due to strong noise interference and limited labeled samples. The paper proposed a semi-supervised diagnosis method based on the convolution Transformer autoencoder (CTAE) for diagnosing hidden defects with small samples and strong noise. Firstly, an optimized variational modal decomposition is utilized to decouple and reduce noise from the raw vibrations, and the decomposition results are concatenated with the vibration signals as input to the model; Secondly, the CTAE is employed to learn the feature distribution of unlabeled samples and to extract and fuse local and global features from the input data; Finally, a labeled samples are used to fine-tune the model and to fuse features from multi-sensor information. The results of using a compressor dataset for validation show that the proposed method has high diagnosis accuracy and robustness with limited labeled samples and different signal-to-noise ratios.

An Investigation of ECAPA-TDNN Audio Type Recognition Method Based on Mel Acoustic Spectrograms

Audio signals play a crucial role in our perception of our surroundings. People rely on sound to assess motion, distance, direction, and environmental conditions, aiding in danger avoidance and decision making. However, in real-world environments, during the acquisition and transmission of audio signals, we often encounter various types of noises that interfere with the intended signals. As a result, the essential features of audio signals become significantly obscured. Under the interference of strong noise, identifying noise segments or sound segments, and distinguishing audio types becomes pivotal for detecting specific events and sound patterns or isolating abnormal sounds. This study analyzes the characteristics of Mel’s acoustic spectrogram, explores the application of the deep learning ECAPA-TDNN method for audio type recognition, and substantiates its effectiveness through experiments. Ultimately, the experimental results demonstrate that the deep learning ECAPA-TDNN method for audio type recognition, utilizing Mel’s acoustic spectrogram as features, achieves a notably high recognition accuracy.

A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA.

Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)-variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.

Weighted IMF-based denoising and multi-scale kurtosis weighted K singular value decomposition dictionary learning model for bearing fault diagnosis

Generally, bearing early failure-induced impulses is weak and easily submerged by strong background noise. How to extract weak impulses is a big challenge. In this paper, based on the K Singular Value Decomposition (KSVD) algorithm, an improved dictionary learning model (WID-MSKW-KSVD) is proposed, which integrates a special denoising method and a multi-scale transformation into a typical dictionary learning model and it successfully overcomes two major shortcomings of traditional KSVD model including the lack of multi-scale learning process and the ability of dictionary atoms to resist strong noise interference. Furthermore, the kurtosis of the envelope spectrum is weighted into the proposed model as a fault matrix to enhance the fault feature. Finally, the feasibility of the proposed model is demonstrated by numerical simulation and practical experiments, and the superiority and reliability of this model in suppressing noise interference and extracting fault features are verified by comparison with other state-of-the-art methods.

A Symmetrized Dot Pattern Extraction Method Based on Frobenius and Nuclear Hybrid Norm Penalized Robust Principal Component Analysis and Decomposition and Reconstruction.

Due to their symmetrized dot pattern, rolling bearings are more susceptible to noise than time-frequency characteristics. Therefore, this article proposes a symmetrized dot pattern extraction method based on the Frobenius and nuclear hybrid norm penalized robust principal component analysis (FNHN-RPCA) as well as decomposition and reconstruction. This method focuses on denoising the vibration signal before calculating the symmetric dot pattern. Firstly, the FNHN-RPCA is used to remove the non-correlation between variables to realize the separation of feature information and interference noise. After, the residual interference noise, irrelevant information, and fault features in the separated signal are clearly located in different frequency bands. Then, the ensemble empirical mode decomposition is applied to decompose this information into different intrinsic mode function components, and the improved DPR/KLdiv criterion is used to select components containing fault features for reconstruction. In addition, the symmetrized dot pattern is used to visualize the reconstructed signal. Finally, method validation and comparative analysis are conducted on the CWRU datasets and experimental bench data, respectively. The results show that the improved criteria can accurately complete the screening task, and the proposed method can effectively reduce the impact of strong noise interference on SDPs.

Denoising land-based controlled-source electromagnetic data based on a same-site noise reference channel

SUMMARY The applications of land-based controlled-source electromagnetic (CSEM) exploration are severely limited by strong noise interferences, particularly in mining areas. In this study, we introduce a novel denoising method for CSEM data using a same-site noise reference channel (NRC). While recording data through the normal survey channel (NSC), an additional set of the NRC was added at the same site. The NRC had a different surveying azimuth compared to the NSC and contained minimal or no useful signals. However, the noise characteristics in both the NRC and NSC were considerably similar due to their simultaneous acquisition at the same site. By establishing a set of overdetermined equations for the NSC based on quantified spectrogram characteristics of the NRC, the noise can be effectively eliminated from the NSC using least-squares inversion, resulting in enhanced signal-to-noise ratio data. The effectiveness of the proposed CSEM data-denoising method was validated through its application on real data, and the proposed method is applicable to other types of artificial source data.

Metamaterial-based acoustic enhanced sensing for gearbox weak fault feature diagnosis

In gear fault diagnosis of practical engineering, the gear fault signal is affected by external strong noise interference, the multipath superposition and interface loss of the internal fault excitation transmission, which leads to weak feature of incipient fault signals, making the gear fault diagnosis difficult. Inspired by the acoustic manipulation capabilities of acoustic metamaterials, this study proposes a method for weak gear fault feature diagnosis via gradient acoustic metamaterial (GAM), which utilises the acoustic rainbow capture and compression ability to reduce the difficulty of gear incipient fault diagnosis. According to the gear frequency modulation/amplitude modulation modulation principle, enhancement of fault feature frequencies can be achieved by collecting gear acoustic signals from the selected air gaps in GAM structure. In this study, the feasibility of GAM-based gear weak fault feature diagnosis is proved by experiments, which verify the multiscale feature denoising and frequency selective enhancement characteristics of GAM. The results show that the amplitude of the target signal is amplified more than eight times, the sideband component containing the fault signal is enhanced obviously, and the effect of denoising outside the target sideband is evident, making the fault feature can be easily identified from the weak fault signal after demodulation. By comparing with digital filtering in traditional signal processing, this method is more straightforward in extracting weak gear fault features. Additionally, this method adopts non-contact measurement method with micro-electroMechanical system (MEMS) microphone, which has advantages over acceleration sensors in overcoming the space limitation. All in all, the proposed method is effective and facilitates the identification of weak gear faults.

Cavitation Diagnostics Based on Self-Tuning VMD for Fluid Machinery with Low-SNR Conditions

Variational mode decomposition (VMD) is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters: the decomposed number K and penalty factor α under strong noise interference. To solve this issue, this study proposed self-tuning VMD (SVMD) for cavitation diagnostics in fluid machinery, with a special focus on low signal-to-noise ratio conditions. A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition. A hybrid optimized sparrow search algorithm (HOSSA) was developed for optimal α fine-tuning in a refined space based on fault-type-guided objective functions. Based on the submodes obtained using exclusive penalty factors in each iteration, the cavitation-related characteristic frequencies (CCFs) were extracted for diagnostics. The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition. The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs. Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost. SVMD especially enhances the denoising capability of the VMD-based method.

A fault detection method for induction motors with sliding mode observers based on stochastic resonance and the Teager energy operator

To accurately and sensitively track the stator current of an induction motor (IM) and detect faults, stochastic resonance (SR) and the Teager energy operator (TEO) are combined to detect a fault in the residual stator current of a sliding mode observer (SMO) under strong noise interference and complex weak fault conditions. First, a new reaching law is constructed to establish an SMO for better state tracking. Second, SR is used to absorb noise and amplify the detection residuals of the SMO, and the output results are estimated by the TEO in the time domain to achieve fault detection. Finally, the detection results of the IM stator and rotor winding faults and sensor intermittent faults are presented. The experimental results show that the SMO has higher state tracking accuracy and a faster rate of convergence. Moreover, the residual of the stator current is processed by SR and the TEO, and the effectiveness of fault detection is enhanced.

Sparse random similarity feature decomposition and its application in gear fault diagnosis

As a new signal decomposition method, sparse random mode decomposition (SRMD) does not depend on the signal sampling process and has good anti-mode aliasing performance. However, SRMD needs preset decomposition parameters and cannot perform accurate decomposition for strong noise interference signals. Thus, this paper proposes a sparse random similarity feature decomposition (SRSFD) method to overcome the problems of SRMD. Firstly, SRSFD utilizes the windowed sinusoidal function to randomly generate time–frequency features. Then, by solving sparse optimization problem, the sparse random feature approximation of the input signal can be obtained and its corresponding time–frequency features can be applied to reconstruct the initial signal components. Finally, the initial signal components are regrouped by using periodic similarity to obtain the final components. The analytical results of simulation and actual gear fault signals show that SRSFD can effectively achieve adaptive signal decomposition and has better decomposition performance and noise robustness compared with existing methods.

Rolling bearing fault diagnosis method based on ELMD hybrid feature extraction and wavelet neural network

Rolling bearings are the most important components in the transmission system of coal mining machinery, and their operating condition significantly impacts the entire mechanical and electrical equipment. Therefore, the fault diagnosis of rolling bearing can effectively ensure the operation reliability of equipment. Given the strong noise, coal impact, and other interference, the vibration signal of the rolling bearing cannot be effectively decomposed, and the fault identification efficiency is low. According to the method based on vibration analysis, this article proposes a rolling bearing fault diagnosis method based on ensemble local mean decomposition (ELMD) hybrid feature extraction and wavelet neural network. ELMD is used to solve the problem of modal aliasing in local mean decomposition (LMD), which can improve the efficiency of LMD. Quantitatively extracting the mixed features of each component and introducing a wavelet neural network for fault type recognition. The experimental results demonstrate that the proposed method has a high accuracy in fault recognition and is an effective fault diagnosis method.

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.

Weak Fault Feature Extraction Method of Rolling Bearings Based on MVO-MOMEDA Under Strong Noise Interference

Weak Fault Feature Extraction Method of Rolling Bearings Based on MVO-MOMEDA Under Strong Noise Interference

Fault Diagnosis Method of Rolling Bearing Based on CBAM_ResNet and ACON Activation Function

In order to cope with the influences of noise interference and variable load on rolling bearing fault diagnosis in real industrial environments, a rolling bearing fault diagnosis method based on CBAM_ResNet and ACON activation function is proposed. Firstly, the collected bearing working vibration signals are made into input samples to retain the original features to the maximum extent. Secondly, the CBAM_ResNet fault diagnosis model is constructed. By taking advantage of the convolutional neural network (CNN) in classification tasks and key feature extraction, the convolutional block attention module network (CBAM) is embedded in the residual blocks, to avoid model degradation and enhance the interaction of information in channel and spatial, raise the key feature extraction capability of the model. Finally, the Activate or Not (ACON) activation function, is introduced to adaptively activate shallow features for the purpose of improving the model’s feature representation and generalization capability. The bearing dataset of Case Western Reserve University (CWRU) is used for experiments, and the average accuracy of the proposed method is 97.68% and 93.93% under strong noise interference and variable load, respectively. Compared with the other three published bearing fault diagnosis methods, the results indicate that this proposed method has better noise immunity and generalization ability, and has good application value.

Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method

Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs.

Implementation of Real-Time Space Target Detection and Tracking Algorithm for Space-Based Surveillance

Space-based target surveillance is important for aerospace safety. However, with the increasing complexity of the space environment, the stellar target and strong noise interference pose difficulties for space target detection. Simultaneously, it is hard to balance real-time processing with computational performance for the onboard processing platform owing to resource limitations. The heterogeneous multi-core architecture has corresponding processing capabilities, providing a hardware implementation platform with real-time and computational performance for space-based applications. This paper first developed a multi-stage joint detection and tracking model (MJDTM) for space targets in optical image sequences. This model combined an improved local contrast method and the Kalman filter to detect and track the potential targets and use differences in movement status to suppress the stellar targets. Then, a heterogeneous multi-core processing system based on a field-programmable gate array (FPGA) and digital signal processor (DSP) was established as the space-based image processing system. Finally, MJDTM was optimized and implemented on the above image processing system. The experiments conducted with simulated and actual image sequences examine the accuracy and efficiency of the MJDTM, which has a 95% detection probability while the false alarm rate is 10−4. According to the experimental results, the algorithm hardware implementation can detect targets in an image with 1024 × 1024 pixels in just 22.064 ms, which satisfies the real-time requirements of space-based surveillance.

Small sample fault diagnosis for wind turbine gearbox based on lightweight multiscale convolutional neural network

The maintenance and diagnosis of wind turbine gearboxes are crucial for enhancing the stability and operational efficiency of wind power systems. However, there are still two challenges in gearbox fault diagnosis methods based on deep learning: (1) limited failure sample; (2) interference of strong noise. To solve the above issues, a lightweight multiscale convolutional neural network (LMSCNN) based fault diagnosis method is proposed in this paper. Among them, a large kernel convolution is used to denoise the original vibration signal. A lightweight multiscale architecture is constructed using depthwise separable convolutional blocks, which mine fault features at different scales and improve the operational efficiency of the model. Moreover, a parallel global pooling block is designed to provide a more comprehensive feature for the fusion layer, enabling the effective diagnosis of vibration signals. Experiments are conducted on the datasets of two different gearboxes, which prove that LMSCNN has excellent generalization capability and diagnostic speed.

SKND-TSACNN: A novel time-scale adaptive CNN framework for fault diagnosis of rotating machinery

Recently, deep learning models represented by convolutional neural networks (CNN) have played an increasingly important role in rotating machinery fault diagnosis (FD). However, complex working conditions and uncertain noise interference pose severe challenges to the accuracy and anti-noise abilities of CNN-based FD models. To address these, based on a selective kernel block (SKB), a novel time-scale adaptive CNN (TSACNN) and neural network denoiser (NND) are constructed in this study, and an FD framework with strong generalization and anti-noise abilities is established through their deep fusion. Based on the end-to-end idea, the kernel adaptive capability of the SKB enables the traditional CNN to dynamically extract the time-domain information of different-scales hidden in signals, thus enhancing the FD accuracy under complex working conditions. The NND constructed based on the SKB (named SKND) overcomes the defect of information loss in the process of encoding and decoding and effectively improves the anti-noise ability of the TSACNN by improving the signal-to-noise ratio of the input signals. Experimental results show that TSACNN can achieve better performance than four state-of-the-art CNN-based FD models under both noise interference and complex working conditions and that SKND can achieve better denoising ability than a state-of-the-art NND model. By deep fusion of SKND and TSACNN, the proposed SKND-TSACNN can achieve satisfactory FD accuracy and robustness under complex working conditions and strong noise interference.

Fault Traveling Wave Detection Method based on ICEEMDAN-MCKD

To address the challenge of calibrating the traveling wave-front for fault location in high noise environments, we propose an improved method that combines the ICEEMDAN and maximum correlation kurtosis deconvolution (MCKD). Our approach first decomposes the fault traveling wave signal into modal components using the ICEEMDAN algorithm. Next, the MCKD algorithm enhances the energy amplitude of the decomposed signal, suppresses strong background noise interference, and accurately calibrates the fault traveling wave-front. Simulation results demonstrate that ICEEMDAN-MCKD achieves precise calibration of the wave-front, exhibits strong denoising ability, and performs better than existing methods in terms of detecting traveling waves and minimizing wave-front determination time errors in noisy environments.