Strong Noise Interference Research Articles

Faulty Feeder Detection Under High Impedance Faults for Resonant Grounding Distribution Systems

The problem of high impedance fault (HIF) detection in resonant grounding distribution systems has not been well solved. In practical applications, the existing detection methods either have complex calculation processes and low accuracy or have a weak ability to adapt to complex fault conditions, which limits the adaptability of the existing methods. Based on a theoretical analysis, a novel faulty feeder detection method based on the inner product transformation of extreme values is proposed in this paper. First, to eliminate the interference of high-frequency components, the low-frequency characteristic modes of zero-sequence current of each feeder are extracted by the variational mode decomposition (VMD) algorithm. On this basis, the low-frequency intrinsic mode functions (IMFs) are processed by the first-order difference to obtain the current waveform after the difference. Second, the zero crossing point of the differential current waveform is obtained, and then the extreme value point of the original zero-sequence current is calibrated, including the maximum and minimum. Third, at the extreme value point, the comprehensive inner product values (CIPV) of each feeder are calculated. Then, when the values are positive, it is judged as a bus fault, when the value has at least one negative value, it is judged as a feeder fault, and the feeder with the minimum value is judged as a faulty feeder. A large number of simulations and field tests show that this method has better fault detection accuracy in practical applications and is suitable for strong noise interference and asynchronous sampling.

Combined noise reduction and DOA estimation algorithm for MEMS vector hydrophone based on variational mode decomposition

PurposeThis paper aims to solve the problem that strong noise interference seriously affects the direction of arrival (DOA) estimation in complex underwater acoustic environment. In this paper, a combined noise reduction algorithm and micro-electro-mechanical system (MEMS) vector hydrophone DOA estimation algorithm based on singular value decomposition (SVD), variational mode decomposition (VMD) and wavelet threshold denoising (WTD) is proposed.Design/methodology/approachFirstly, the parameters of VMD are determined by SVD, and the VMD method can decompose the signal into multiple intrinsic mode functions (IMFs). Secondly, the effective IMF component is determined according to the correlation coefficient criterion and the IMF less than the threshold is processed by WTD. Then, reconstruction is carried out to achieve the purpose of denoising and calibration baseline drift. Finally, DOA estimation is achieved by the combined directional algorithm of preprocessed signal.FindingsSimulation and field experiments results show that the algorithm has good noise reduction and baseline drift correction effects for nonstationary underwater signals, and high-precision azimuth estimation is realized.Originality/valueThis research provides the basis for MEMS hydrophone detection and positioning and has great engineering significance in underwater detection system.

Deep Learning for Operating Performance Assessment of Industrial Processes with Layer Attention-Based Stacked Performance-Relevant Denoising Auto-Encoders.

The operating performance of a process may degenerate due to process interferences and operation errors, which cancel the benefits of technology design and economic production. Traditional operating performance assessment methods are either lack of real-time due to the post-analysis or difficult to distinguish performance grades for process data with weak differences and strong noise interferences based on shallow learning structures. In this paper, a new layer attention-based stacked performance-relevant denoising auto-encoder (LA-SPDAE) is proposed for the operating performance assessment of industrial processes. It overcomes the defect that the original SDAE ignores task-relevant information in training and only uses the feature of the last hidden layer to complete special tasks. In this study, the original SDAE is improved by optimizing the cross-entropy loss of the performance grade labels in the layer-wise pretraining, which is named stacked performance-relevant denoising auto-encoder (SPDAE), and the performance-relevant features can be extracted under supervision. Moreover, for making good use of performance-relevant features of each layer, they are fused by adaptive weights based on the layer attention mechanism. In the case study of cyanide leaching, the assessment accuracy of the proposed LA-SPDAE model is up to 99.85% under the corrupted proportion of 20%, and the advantage is still maintained as the proportion increases to 80%, which demonstrates the superiority of LA-SPDAE compared with conventional deep neural networks and shallow learning structures.

A Reinforced Noise Resistant Correlation Method for Bearing Condition Monitoring

Condition monitoring plays a significant role in guaranteeing the reliability and safety of rotating machinery, which aims to detect an incipient fault and assess the degradation tendency. The construction of a health index (HI) is a crucial step to realize above tasks. At present, kurtosis, crest factor, and so on have been recognized as popular HIs to depict the operating condition. However, shortcomings of these classical HIs still exist: 1) classical HIs are prone to be affected by strong white Gaussian noise; 2) classical HIs are not sensitive to incipient faults. To deal with these two shortcomings, a reinforced noise resistant correlation method is proposed in this paper. Firstly, a new signal is constructed using the steps of segmenting and averaging to suppress the interference of noise. Then, a novel correlation function is used to get the hidden period. The proposed HI is constructed based on the discrete version of this correlation function to increase the sensitivity of incipient faults. Subsequently, theoretical values of the proposed HI under healthy states are investigated. The effectiveness of the method is demonstrated using simulated degradation processes and two accelerated degradation datasets of rolling element bearings. Through comparisons with other classical HIs, the proposed HI can simultaneously suppress the interference of strong noise and detect incipient faults. The comparison results identify the effectiveness of the proposed method in monitoring the condition of rotating machinery. Note to Practitioners—This work aims to provide a novel health index construction method for rotating machinery condition monitoring. The key issues involved in this problem include 1) how to capture the complex relationship between the measured vibration signals and the underlying health condition of rotating machinery; 2) how to suppress the interference of environmental noise. The novelty of this work is that it develops a method for condition monitoring considering the measured signals with strong white Gaussian noise. It properly captures the complex relationship between measured signals and the underlying health condition. There are four main steps to implement this approach: 1) collecting the vibration signals from rotating machinery; 2) constructing new periodic signal to suppress the interference of strong background noise; 3) constructing novel correlation functions to establish the relationship between the vibration signals and the underlying health condition of the rotating machine; 4) modeling the HI. A simulation and two real cases of the bearing degradation process are used to show that the proposed HI can simultaneously suppress the interference of strong noise and detect the incipient faults compared with some classical HI construction methods. In the future, the problem of how to address the measured signals contaminated by non-Gaussian noise and the machinery containing compound faults should be solved to extend this method in a complex system.

Cycle kurtosis entropy guided symplectic geometry mode decomposition for detecting faults in rotating machinery

Strong noise interference or compound fault coupling phenomenon may lead to the failure of fault diagnosis technology. This paper focuses on weak feature extraction and compound faults detection for rotating machinery fault diagnosis and proposes adaptive symplectic geometric mode decomposition (SGMD) using cycle kurtosis entropy. Firstly, an index named cycle kurtosis entropy (CKE) is presented to measure the strength of periodic impulses in a signal. The CKE uses the entropy value of calculating all delay cycle kurtosis (CK) to overcome the shortcomings of the CK in adaptive ability and obtain more stable values. Thirdly, CKE is applied to construct an adaptive slip window with optimal length. This process is called the adaptive window segmentation method, which is mainly used to dig out weak fault features in signals. Finally, CKE is used as the component selection criterion to select the components decomposed by SGMD. The selected components are reconstructed to obtain a de-noised signal. Hilbert envelope analysis is applied to the denoised signal to demodulate the fault characteristic frequency. Numerical simulations and experimental investigations using bearings and gears are performed to testify the property of the presented method. The results indicate that the adaptive slip window can enhance the decomposing ability of SGMD under strong noise condition. Moreover, for the strong periodic impulse identification ability, the cycle kurtosis entropy is suitable to determine the optimal components of SGMD. It is expected that the presented method will be effectively used for fault feature extractions in rotating machinery under stationary running conditions.

Time-frequency Feature Extraction Method of the Multi-Source Shock Signal Based on Improved VMD and Bilateral Adaptive Laplace Wavelet

Vibration signals have the characteristics of multi-source strong shock coupling and strong noise interference owing to the complex structure of reciprocating machinery. Therefore, it is difficult to extract, analyze, and diagnose mechanical fault features. To accurately extract sensitive features from the strong noise interference and unsteady monitoring signals of reciprocating machinery, a study on the time-frequency feature extraction method of multi-source shock signals is conducted. Combining the characteristics of reciprocating mechanical vibration signals, a targeted optimization method considering the variational modal decomposition (VMD) mode number and second penalty factor is proposed, which completed the adaptive decomposition of coupled signals. Aiming at the bilateral asymmetric attenuation characteristics of reciprocating mechanical shock signals, a new bilateral adaptive Laplace wavelet (BALW) is established. A search strategy for wavelet local parameters of multi-shock signals is proposed using the harmony search (HS) method. A multi-source shock simulation signal is established, and actual data on the valve fault are obtained through diesel engine fault experiments. The fault recognition rate of the intake and exhaust valve clearance is above 90% and the extraction accuracy of the shock start position is improved by 10°.

Multi-Modal Multi-Stage Underwater Side-Scan Sonar Target Recognition Based on Synthetic Images

Due to the small sample size of underwater acoustic data and the strong noise interference caused by seabed reverberation, recognizing underwater targets in Side-Scan Sonar (SSS) images is challenging. Using a transfer-learning-based recognition method to train the backbone network on a large optical dataset (ImageNet) and fine-tuning the head network with a small SSS image dataset can improve the classification of sonar images. However, optical and sonar images have different statistical characteristics, directly affecting transfer-learning-based target recognition. In order to improve the accuracy of underwater sonar image classification, a style transformation method between optical and SSS images is proposed in this study. In the proposed method, objects with the SSS style were synthesized through content image feature extraction and image style transfer to reduce the variability of different data sources. A staged optimization strategy using multi-modal data effectively captures the anti-noise features of sonar images, providing a new learning method for transfer learning. The results of the classification experiment showed that the approach is more stable when using synthetic data and other multi-modal datasets, with an overall accuracy of 100%.

Non-contact diagnosis for gearbox based on the fusion of multi-sensor heterogeneous data

Non-contact sensing technology plays an important role in the health monitoring of the gearbox. However, a single non-contact measurement is challenging to achieve the simultaneous monitoring of both structural and non-structural damages. In order to explore the fusion mechanism of multi-sensor heterogeneous measurements, acoustic and thermal characteristics of the gearbox under typical fault states are analyzed, and it is verified the fusion of infrared thermal (IRT) images and acoustic data integrates complementary fault information. In this paper, an attention-enhanced information fusion diagnosis network (AIFN-IA) is proposed for the complementary fusion of IRT images and acoustic data. Firstly, the acoustic data is converted into images by the non-hyperparameter encoding method and then fused with IRT images in data-level. Secondly, the limited shuffle attention module is designed to adaptively focus on the fault elements hidden in the complex fusion features. Finally, experimental data verify the effectiveness of the proposed AIFN-IA method in recognizing six structural and non-structural damages of the gearbox. Compared with seven state-of-the-art methods, the proposed AIFN-IA method performs best in extracting discriminating features with the highest diagnosis accuracy. Moreover, the proposed AIFN-IA method can still achieve satisfactory results under the challenges of small sample datasets and strong noise interference, which is more competitive in real industrial applications.

Light-weight segmentation network based on SOLOv2 for weld seam feature extraction

The laser vision sensor-based welding robot collects weld images of the workpiece surface and utilizes image processing algorithms to locate feature points in real time during welding. Among the existing methods of weld seam feature extraction, the traditional image processing methods cannot overcome the interference of strong noise, while deep learning-based methods have high computational overhead. Therefore, a light-weight segmentation network based on SOLOv2 is proposed in this paper. It is verified that the proposed network can accurately segment laser stripes under strong noise and achieve a speed of about 29 FPS on the CPU. The proposed network is integrated into the ECO tracking algorithm for welding experiments and the mean absolute error of welding is stable at about 0.2 mm, outperforming the original ECO method. This demonstrates that the proposed network can improve the robustness and accuracy of the weld seam tracking system without significantly increasing the computational resource requirements.

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.

Weighted envelope spectrum based on reselection mechanism and its application in bearing fault diagnosis

As a powerful algorithm, fast spectral correlation (Fast-SC) is widely used in bearing fault diagnosis. However, the interference of strong background noise and the existence of multiple fault-related frequency bands make it difficult to diagnose bearing faults using enhanced envelope spectrum and improved envelope spectrum on the basis of Fast-SC. To alleviate the problem, fast kurtogram is imitated to divide the spectral frequency bands; then, the threshold-based diagnostic feature (TDF) of each integration band is calculated to guide the weighting function of the corresponding level; finally, weighted envelope spectrum with the largest TDF is determined as the output; this process is called the reselection mechanism. Simulation and experimental results indicate that the design of weighting function highlights the contribution of the fault-related frequency bands, and the reselection mechanism further improves its performance.

Feasibility of seismic wave communication using electromagnetic vibrator under strong environmental noise interference: Field experimental research.

Although various wired or wireless communication facilities play a significant role in underground mine production monitoring and disaster warning, some communication nodes will be damaged when an accident occurs, resulting in unreliable emergency communications. Due to the excellent transmission characteristics of seismic waves in the earth channel, there is no need for the seismic wave communication system to set up relay nodes. Since seismic waves are not easily disturbed by the external environment, they can be used as a new type of emergency communication carrier to realize information transmission. However, under strong background noise interference in a real environment, the feasibility of seismic wave communication using an electromagnetic vibrator is not yet known. Therefore, a field experiment research on electromagnetic vibrator seismic wave communication was conducted. First, the model of electromagnetic vibrator seismic wave communication was established. In addition, the on-off keying modulation method, the cross-correlation segment threshold demodulation method, and the noise suppression method based on the time-frequency characteristics of the carrier were introduced. Finally, an electromagnetic vibrator seismic wave communication system was built, and binary data communication up to 72m without deploying additional relay nodes was achieved successfully. The experimental results illustrate the feasibility of the electromagnetic vibrator seismic wave communication under strong background noise interference in a real environment. Furthermore, combined with the characteristics of the seismic wave, the time-frequency slice filtering can significantly improve the signal-to-noise ratio of the seismic wave and can further increase the communication distance with respect to the traditional methods.

Pressure vessel leakage detection method based on online acoustic emission signals

Pressure vessel leakages cannot initially be visited directly and will gradually cause deterioration, which can result in catastrophic damage. Acoustic emission (AE) signals generated by leakage have the potential of being used for online monitoring. Unfortunately, AE signals have the characteristics of being non-stationary, wide-band and with strong noise interference, which causes the monitoring results to have low reliability. To address the poor robustness of traditional time-domain and time-frequency domain-based monitoring methods, an online monitoring method based on adaptive singular value decomposition (ASVD) is proposed in this paper. Firstly, singular value decomposition (SVD) is used to divide the signal space into a signal subspace and a noise subspace. Experiments indicate that SVD can distinguish leakages under conditions of different pressures and variable temperature, which means that SVD is sensitive to changes in signal. Subsequently, update iteration-based ASVD algorithms are proposed for long-term online health monitoring and ASVD is shown to be successful in distinguishing the different statuses of intact, leakage and repaired. To improve the robustness of ASVD, a novel energy indicator is proposed, which can identify the status change more effectively. With the proposed methodology, an online monitoring application for pressure vessel leakage detection is expected to be achievable.

Research on the Multilabel Data Stream Classification Method Based on Fuzzy Complex Set‐Valued Measure Learning

In the process of modern industrial production equipment developing towards the direction of structure, automation, and intelligence, motor is still the main power output equipment. If the data flow classification occurs during the operation of the motor, it will lead to problems such as the reduction of its operation efficiency and the increase in system energy consumption. In serious cases, it will even cause motor damage, and the overall system equipment will be shut down for maintenance for a long time, resulting in serious economic losses. Therefore, the research on intelligent multilabel data stream classification technology of motor is of great significance to ensure the stability and reliability of efficient operation of production equipment. In order to improve the recognition efficiency and accuracy of ms‐1dcnn’s multilabel data stream classification method in the environment of variable motor conditions and strong noise interference, a multiscale feature fusion framework is constructed based on the residual network structure. The implementation principles of two kinds of attention mechanism algorithms, squeeze and excitation module and convolution attention module, are studied, respectively. The attention module suitable for one‐dimensional residual network is designed and embedded into the residual module to build a multiscale attention residual network model. Finally, the effectiveness and superiority of the proposed model are verified by using the experimental platform data.

High-Resolution Fast-Rotating Sound Localization Based on Modal Composition Beamforming and Bayesian Inversion

Rotating source beamforming techniques have been effective means of noise localization on rotary machines. In this letter, we derive an alternative expression for modal composition beamforming (MCB) and subsequently consider the equivalent source assumption and cyclostationarity of the constant angular-speed rotating sound source so that a rotating sound source power (RSP) propagation model is derived. By estimating a suitable solution for the RSP model using the subspace variational Bayesian (SVB) technique with sparsity and total variation (TV) priors, the validity of the RSP model was established. According to the simulation results, the proposed RSP-SVB method leads to a significantly higher resolution than the MCB method. It can localize multiple fast-rotating sound sources accurately, rapidly, and effectively in environments with strong background noise interference. Therefore, our proposed RSP-SVB can offer a reliable solution for identifying fast-rotating blade noise.

Detection-Performance Tradeoff for Watermarking in Industrial Control Systems

The watermarking method, which adds unique watermarks to data, has been widely used for integrity attack detection in industrial control systems (ICSs). Existing literature generally designs watermarking mechanisms without considering the existence of noises, which cannot be trivially applied to realistic ICS scenarios in the presence of strong noise interference. On one hand, the low-intensity watermarking will be ineffective under the strong noise environment; while on the other hand, the oversized watermarking can possibly degrade the control performance or even destabilize the system. Therefore, the intensity of watermarks plays a fundamental role in balancing the tradeoff between detection effectiveness and control performance, which, to the best of our knowledge, has never been thoroughly analyzed yet. To this end, in this paper, we for the first time propose an optimal watermarking design method for ICSs considering the detection-performance tradeoff. To begin with, we shift the watermark container from data points to segments and update the detection metrics to reduce the noise impact. Then, we formulate an optimization problem to determine the strength of watermarks to balance the detection-performance tradeoff. Meanwhile, the detection effectiveness and control performance metrics are analytically modeled and theoretically analyzed considering the discrepancy between added watermarks and noises, signal quality, detection latency, as well as estimation of detection metrics. Finally, extensive numerical simulations and systematical experiments based on a practical Ethanol Distillation ICS are conducted to validate the theoretical analysis and demonstrate the outperformance of our proposed watermarking method in comparison with related works.

Underwater bubble escape volume measurement based on passive acoustic under noise factors: Simulation and experimental research

Passive acoustic methods are suitable for long-term seabed gas leak monitoring with high accuracy under the condition of low cost and simple experimental equipment. However, the impact of noise in complex underwater environments poses great challenges for measurements. In this paper, aiming at the quantitative detection problem, a theoretical model of the bubble generation process by underwater gas escape is established, and the mechanism of bubble acoustic signal generation is clarified. The model identification of bubble volume oscillation is realized through the complementary ensemble empirical mode decomposition (CEEMD) method. Then continuous bubbles are segmented by the normalized energy method to achieve a high-precision measurement of volume inversion. A measurement prototype is developed and tested in the lake with multiple noises caused by seawater disturbance, sea wind, and ships. The results showed that the measurement errors could be kept within 10% even under the condition of strong noise interference.

Weak Fault Feature Extraction of Axle Box Bearing Based on Pre-Identification and Singular Value Decomposition

The axle box bearing is one of the core rotating components in high-speed trains, having served in complex working conditions for a long time. With the fault feature extraction of the vibration signal, the noise interference caused by the interaction between the wheels and rails becomes apparent. Especially when there is a shortwave defect in the rail, the interaction between wheels and rails will produce high-amplitude impulse interference. To solve the problem of the collected vibration signals of axle box bearings containing strong noise interference and high amplitude impact interference caused by rail shortwave irregularities, this paper proposes a method based on pre-identification via singular value decomposition technology to select the signals in sections and filter the noise, followed by feature extraction and fault diagnosis. The method is used to analyze the axle box bearing fault simulation signal and the weak fault signal collected by the railway bearing comprehensive experimental platform, and these signals are then compared with the random screening signal and the manual screening signal to verify the effectiveness of the method.

Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network

In the case of strong noise, when the damage occurs at different locations of the frame structure, the fault vibration signals generated are relatively close. It is difficult to accurately diagnose the specific location of the damage by using the traditional convolution neural network method. In order to solve this problem, this paper proposes a novel convolutional neural network. The method first uses wavelet decomposition and reconstruction to filter out the noise signal in the original vibration signal, then uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Analysis) to decompose the filtered signal to highlight the feature information in the filtered signal. Finally, a convolution neural network combined with WDCNN (First Layer Wide Convolution Kernel Deep Convolution Neural Network) and LSTM (Long Short-Term Memory Network) is used to achieve the accurate classification of the signal, so as to achieve the accurate diagnosis of the damage location of the frame structure. Taking the four-story steel structure frame of Columbia University as the research object, the fault diagnosis method proposed in this paper is used to carry out experimental research under strong noise conditions. The experimental results show that the accuracy of the fault diagnosis method proposed in this paper can reach 99.97% when the signal-to-noise ratio is −4 dB and the objective function value is reduced to 10−4. Therefore, the fault diagnosis method proposed in this paper has a high accuracy in the strong noise interference environment; it can realize a high precision diagnosis of the damage location of the frame structure under a strong noise environment. The contribution and innovation of this paper is to propose a novel fault diagnosis method based on the convolutional neural network, which solves the problem of accurate damage location diagnosis of frame structures under strong noise environment.

De-noising of partial discharge ultrasonic signal of insulation bar in large motor based on GMC-wavelet

Abstract In view of the bad operation environment of large motor, which often suffers from various strong noise interference, the partial discharge ultrasonic signal is often annihilated, which makes it difficult to detect and analyse. A de-noising method based on generalized minimax concavity (GMC) and wavelet for partial discharge (PD) ultrasonic signal is proposed. GMC is used to enhance the sparsity of PD ultrasonic signal and eliminate the high-frequency noise signal at the same time. Then the residual high-frequency sparse noise and low-frequency noise of the former are de-noised again combined with wavelet. Finally, the signal is reconstructed to achieve the purpose of de-noising the original PD ultrasonic signal with noise. Compared with ℓ1 -norm method, GMC method, wavelet method and ℓ1 -norm-wavelet method, the simulation results show that based on time domain analysis, the de-noising effect of the proposed method is obviously better than the other four methods. The SNR and MSE of the former are better than those of the latter. In addition, the insulation bar discharge model of large motor is constructed to obtain the actual PD ultrasonic signal, which further verifies its effectiveness, and its de-noising effect is also better than the four methods. This method can not only enhance the sparsity of the target signal and improve the estimation accuracy, but also achieve the de-noising effect, while retaining the effective information of PD ultrasonic signal characteristics. This method can provide new ideas for other types of PD signal de-noising, and lay the foundation for later feature analysis.