Strong Interference Environment Research Articles

A denoising algorithm based on ARIMA and competitive K-SVD for the diagnosis of rolling bearing faults

Rolling bearings are extensively employed in industrial production as essential support components for rotating machinery. However, under conditions of high load and harsh operation, bearings are highly susceptible to failure. The weak vibration signals associated with these failures may be obscured by complex harmonic interference and strong noise, posing challenges for the accurate diagnosis of rolling bearing failures. In this paper, an autoregressive integrated moving average and competitive K-singular value decomposition (ARIMA-CK-SVD) algorithm is proposed to realize effective extraction of faulty pulse signals in a strong interference environment. First, the ARIMA model is used to preprocess the original signal to eliminate the interference of harmonic components. Second, a method is proposed for the adaptive selection of parameters in the ARIMA model, with consideration given to the characteristics of K-SVD. Subsequently, a competitive mechanism is introduced during the dictionary update phase of the algorithm to adjust the pattern of atomic updates and eliminate noise atoms. Finally, the effectiveness of the ARIMA-CK-SVD has been validated through simulation experiments and engineering tests.

A novel detection method for warhead fragment targets in optical images under dynamic strong interference environments

A measurement system for the scattering characteristics of warhead fragments based on high-speed imaging systems offers advantages such as simple deployment, flexible maneuverability, and high spatiotemporal resolution, enabling the acquisition of full-process data of the fragment scattering process. However, mismatches between camera frame rates and target velocities can lead to long motion blur tails of high-speed fragment targets, resulting in low signal-to-noise ratios and rendering conventional detection algorithms ineffective in dynamic strong interference testing environments. In this study, we propose a detection framework centered on dynamic strong interference disturbance signal separation and suppression. We introduce a mixture Gaussian model constrained under a joint spatial-temporal-transform domain Dirichlet process, combined with total variation regularization to achieve disturbance signal suppression. Experimental results demonstrate that the proposed disturbance suppression method can be integrated with certain conventional motion target detection tasks, enabling adaptation to real-world data to a certain extent. Moreover, we provide a specific implementation of this process, which achieves a detection rate close to 100% with an approximate 0% false alarm rate in multiple sets of real target field test data. This research effectively advances the development of the field of damage parameter testing.

A New Method for Anti-Interference Measurement of Capacitance Parameters of Long-Distance Transmission Lines Based on Harmonic Components

In the context of strong electromagnetic interference environments, the measurement accuracy of the capacitance parameters of transmission lines under power frequency measurement methods is not high. In this paper, a capacitance parameter anti-interference measurement method for transmission lines based on harmonic components is proposed to overcome the impact of power frequency interference. When applying this method, it is first necessary to open-circuit the end of the line under test. Subsequently, apply voltage to the head end of the tested line through a step-up transformer. Due to the saturation of the transformer during no-load conditions, a large number of harmonics are generated, primarily third harmonic. The third harmonic components of voltage and current on the tested transmission line are extracted using the Fourier transform. The proposed method addresses the influence of line distribution effects by establishing a distributed parameter model for long-distance transmission lines. The relevant transmission matrix for the zero-sequence distributed parameters is obtained by combining Laplace transform and similarity transform to solve the transmission line equations. Using synchronous measurement data from the third harmonic components of voltage and current at both ends of the transmission line, combined with the transmission matrix, this method accurately measures the zero-sequence capacitance parameters. The PSCAD/EMTDC simulation results and field test outcomes have demonstrated the feasibility and accuracy of the proposed method for measuring line capacitance parameters under strong electromagnetic interference.

IMU/Magnetometer-Based Azimuth Estimation with Norm Constraint Filtering.

A typical magnetometer-based measurement-while-drilling (MWD) system determines the azimuth of the bottom hole assembly during the drilling process by employing triaxial accelerometers and magnetometers. The geomagnetic azimuth solution is susceptible to magnetic interference, especially strong magnetic interference and so a rotary norm constraint filtering (RNCF) method for azimuth estimation, designed to support a gyroscope-aided magnetometer-based MWD system, is proposed. First, a new magnetic dynamical system, one whose output is observed by the magnetometers triad, is designed based on the Coriolis equation of the desired geomagnetic vector. Second, given that the norm of the non-interfered geomagnetic vector can be approximated as a constant during a short-term drilling process, a norm constraint procedure is introduced to the Kalman filter. This is achieved by the normalization of the geomagnetic part of the state vector of the dynamical system and is undertaken in order to obtain a precise geomagnetic component. Simulation and actual drilling experiments show that the proposed RNCF method can effectively improve the azimuth measurement precision with 98.5% over the typical geomagnetic solution and 37.1% over the KF in a RMSE sense when being strong magnetic interference environment.

DOA Estimation of Broadband Sources Using Dimension-Reduced Matrix Filter with Deep Nulling in a Strong Interference Environment

To enhance the performance of the adaptive matrix filter with nulling, a dimension-reduced matrix filter with deep nulling (DR-MFDN) is proposed in this paper and used for direction of arrival (DOA) estimation of broadband sources in a strong interference environment. Consider the change of noise after filtering, the gaussian matrix is used for prewhitening to prevent the noise from converting into colored noise without affecting the response characteristics of the beam-space. Finally, the multiple measurement vector orthogonal matching pursuit (MOMP) algorithm is used for DOA estimation of weak targets after filtering, and the simulation results show a better DOA estimation performance compared to the OMP and MUSIC algorithms with short snapshots.

Dual-directional SCR device with dual-gate controlled mechanism for ESD protection in photoelectric chip

The dual-directional silicon-controlled rectifier (DDSCR) is an electrostatic discharge (ESD) protection device. It can provide positive and negative ESD surge paths and has excellent robustness. However, industry-level sensors operating in strong electromagnetic interference environments impose higher reliability requirements on photoelectric chips. This paper proposed a novel DDSCR with a dual-gate controlled mechanism. By incorporating the gate diode triggering and the gate field modulation mechanism into the traditional DDSCR, and further utilizing additional parasitic bipolar junction transistors (BJTs) for diversion, the proposed device exhibits significantly improved ESD characteristics. Measurement results indicate that, compared to DDSCR, the proposed device exhibits a 27.5% reduction in trigger voltage (Vt1 ), a 96.1% improvement in holding voltage (Vh ), and achieves an equivalent human body model protection level of 11.45 kV, demonstrating exceptional design area efficiency. The experimental findings validate the effectiveness of the proposed device in 5 V photoelectric chip applications.

Evaluation Study on Wavelet Denoising of Antenna-based PD Measurements in Strong Interference Environments considering a New Reliability Score of Pulse Detection

Evaluation Study on Wavelet Denoising of Antenna-based PD Measurements in Strong Interference Environments considering a New Reliability Score of Pulse Detection

Fast and accurate wideband sparse spatial spectrum estimation in an underwater strong interference environment.

Wideband sparse spatial spectrum estimation is an important direction-of-arrival (DOA) estimation method that can obtain a high resolution with few snapshots and a low signal-to-noise ratio. However, in an underwater strong interference environment, the accuracy of DOA estimation may be seriously affected, and even the weak targets could be completely masked. In this paper, we propose a fast matrix filter design method based on truncated nuclear norm regularization to attenuate strong interferences while passing weak targets. The matrix filter operator and the exact covariance matrix after filtering can be obtained simultaneously by solving a convex optimization problem that contains the output power term and non-Toeplitz error propagation control term. Then the modified sparse spectrum fitting algorithm based on the matrix filter is used to estimate spatial spectrum over closely spaced wideband signals. Compared with existing methods, the proposed method achieves higher DOA estimation accuracy and lower computational time for matrix filter design. Meanwhile, the estimation accuracy of the proposed method is verified with the experimental results.

Lightning protection technology and grounding technology of transmission tower shared equipment

For the power transmission tower shared equipment, there are strong current wireless interference and complex grounding environment, which leads to problems such as unstable operation of the shared equipment. Based on the multi-grade surge protector and new grounding technology, through the impulse experiments, we apply simulated lightning current generated from combined wave generator (1.2/50 μs, 8/20 μs) on the modle, and an oscilloscope is used to collect residual voltage and current data to calculate the absorbed energy of the grounding unit. It is concluded that when the impulse voltage is between 300 V and 3500 V, as the impulse voltage increases, after passing through the multi-grade surge protector and grounding unit, the residual voltage and current value continue to increase, and the residual voltage value is 23.4 V–29.6 V increases linearly, and the increase is small; the internal resistance of the grounding unit decreases with the increase of the impulse voltage. The energy absorbed by the grounding unit is positively correlated with the impulse voltage and negatively correlated with the internal resistance, and its energy absorption percentage decays linearly. The multi-grade surge protector can effectively clamp the residual voltage value within a safe value, and its discharge effect is better than that of the traditional protection mode. It provides a theoretical basis and data reference for the lightning protection and anti-interference projects of the actual transmission tower sharing equipment, and has certain practical value.

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.

An Improved Innovation Robust Outliers Detection Method for Airborne Array Position and Orientation Measurement System

The airborne array position and orientation measurement system (array POS) is a key device for high-resolution multi-dimensional real-time imaging motion compensation of military reconnaissance mapping. Abnormal values will appear in array POS inertial devices and measurement data in an environment of strong interference, which often leads to a decrease or even divergence in the combination accuracy. The existing detection methods based on innovation characteristics are only sensitive to measurement outliers, which are the abnormal data caused by the strong interference environment. In this paper, an improved innovation robust outliers detection method is proposed, which is valid for both measurement outliers and inertial device outliers. First, the improved outliers detection method based on the innovation of array POS is described. The gain matrix is adaptively adjusted by using the statistical characteristics of innovation. At the same time, the information distribution coefficient is adaptively adjusted by using the filtering performance of the sub filter, which realizes the detection and correction of measurement outliers. Then, the outlier detection method of inertial devices based on extrapolation prediction is added. The predicted value of the inertial device is extrapolated by the fourth-order difference method, and the outliers are recognized and eliminated by the adaptive threshold, which contributes to improving the robustness and accuracy of array POS. STD is selected in this paper to statistic the accuracy of array POS. Compared with the traditional federated Kalman filtering (KFK) methods, the accuracies of position, speed, heading angle and horizontal attitude angle of the left node and right node are all improved when there are outliers in the measurement data. Compared with the fault-tolerant federated combination method based on innovation characteristics, the accuracies of position, speed, heading angle and horizontal attitude angle of the left node and right node are all improved when there are abnormal values in the inertial device data.

Automatic Time Picking for Weak Seismic Phase in the Strong Noise and Interference Environment: An Hybrid Method Based on Array Similarity.

The extraction of travel-time curve of seismic phase is very important for the subsequent inference of the structural properties of underground media in seismology. In recent years, with the increase in the amount of data, manual processing is facing significant challenges, and automatic signal processing has gradually become the mainstream. According to the similarity of array signals and considering the elimination of outliers, we propose an improved multi-channel cross-correlation method using the L1 norm measure to obtain preliminary results, which builds on a new controllable measurement mode. Then, the post-correction step is carried out in combination with the signal gain property of beamforming technique. Based on these two methods, this paper proposes a new scheme of automatic arrival time picking. We apply the scheme to actual data to verify the effects of the two methods step by step. The entire scheme achieves fine results: direct water waves, seismic waves refracted by the crust and seismic waves reflected by the upper mantle are automatically detected. In addition, compared with the two traditional methods, the scheme proposed in this paper has a better overall effect and a reasonable computation cost.

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.

A nonlinear total variation based denoising method for electrostatic signal of low signal-to-noise ratio

Aero-engine electrostatic monitoring technology (EMT) is a novel and effective condition monitoring technology. With the help of EMT, effective monitoring of early failures can be achieved. Since the electrostatic monitoring of the running engine will be strongly interfered, the sampled electrostatic signal has various noise components and low signal-to-noise ratio (SNR). After analyzing the source of the noise components carried by the electrostatic signal, this paper proposes a method for electrostatic signal denoising in a strong interference environment, which is based on the nonlinear total variation theory. In the experiments, the simulated electrostatic measurement signal and the actual test-run electrostatic measurement signal were used as the analysis objects, and the denoising test was carried out by using the proposed method. Meanwhile, the denoising effect was compared and analyzed with other classical methods. The experimental results show that the proposed denoising method can effectively remove random noise, electromagnetic pulse and periodic noise in electrostatic signal, and is more applicable to the measured electrostatic signal with low SNR than the classical electrostatic signal denoising methods such as wavelet threshold denoising method and empirical mode decomposition method.

Research on the rapid assembly algorithm of the wireless ultraviolet cooperative UAV formation.

Aiming at the problem that the strong electromagnetic interference environment restricts the communication of the Unmanned Aerial Vehicle(UAV) formation, which affects the rapid assembly of the UAV formation, a wireless ultraviolet cooperative UAV formation rapid assembly algorithm is proposed to realize the fixed-point assembly of the UAV formation in the airspace. First, the Ultraviolet(UV) light beacon model is used to realize the information interaction of the UAV formation. secondly, the UV four-node positioning algorithm is used to realize the three-dimensional positioning of the UAV formation. Combined with the method, the UAV formation can reach a consensus speed when assembling. The simulation results show that when there are more than 6 reference nodes, the three-dimensional space positioning accuracy can reach 96%. The assembly algorithm can realize the fixed-point assembly of the UAV formation, and can maintain the circular motion trajectory after the assembly is completed.

Speaker Recognition of Fiber-Optic External Fabry-Perot Interferometric Microphone Based on Deep Learning

In order to realize speaker recognition in flammable, explosive and strong electromagnetic interference environments, the speaker recognition technology of fiber-optic external Fabry-Perot interferometric (EFPI) microphone based on Deep learning is studied in this paper. The combination of convolutional neural network and long short-term memory (CNN-LSTM) model is proposed for speaker recognition in EFPI acoustic sensing system. The intensity self-compensation method is used to demodulate the voice signals of different speakers, then, the spectral features of voice signals are divided into training set and test set. The training set are input into the established CNN-LSTM model for model training, and the test set are input into the trained model for testing. The experimental results show that the CNN-LSTM model proposed in this paper has good training accuracy and test accuracy, reaching 100% and 94.0% respectively. Comparing with CNN and LSTM recurrent neural network (LSTM-RNN), the test accuracy of this model is 11.1% and 13.4% higher respectively.

Improving the polarisation and depolarisation current measuring method to avoid ground wire interference

The ground wire is significantly interfered by the current above 1 μA in the industrial field during the traditional polarisation and depolarisation current (PDC) measurement. This paper explains a method to test PDC from high-voltage wire using a principle of equipotential picoammeter, which is completely isolated from the ground wire and connected in series with the high voltage wire. In this method, a device is designed with a multi-range automatic switching function. The device can automatically switch the test range current from 1 pA to 10 mA with a resolution of 1 pA. To demonstrate the device and effectiveness of this new method, the traditional and the proposed new device are used to test the PDC of cable samples in the laboratory as well as in an industrial environment. It is worth noting that there is almost no interference on the Earth wire in the laboratory environment but massive interference in the industrial environment using a traditional method of PDC measurement from ground or Earth wire. The new method of high-voltage testing can efficiently eliminate the interference and accurately measure PDC in a strong industrial interference environment. Furthermore, the wiring of this new method and device is relatively simple, which is convenient for industrial applications.

A novel rolling bearing fault diagnosis method based on generalized nonlinear spectral sparsity

In the fast kurtogram (FK), kurtosis is used as an indicator to locate the fault frequency band, and is widely aplied to fault diagnosis. However, kurtosis has been proven to favor a single large impulse rather than the required small fault characteristics, especially in the strong interference environment. To eliminate the impact of large-amplitude impact and further improve the accuracy of fault extraction, a method based on generalized nonlinear spectral sparsity (GNSS) is proposed for fault diagnosis of bearings. First, Z-score normalization and generalized nonlinear sigmoid activation function are used for signal preprocessing, and the scale distribution of the signal will be changed to eliminate the effects of large amplitude shocks under noisy environment. Then, to improve the sparsity measure capability, an improved L3/2 norm is used to replace kurtosis as the basis for selecting the best resonance frequency band. Finally, the effectiveness of the GNSS is verified by simulation data and experimental data. Compared with FK method, the performance of fault extraction of the proposed method is significantly improved, especially for the interference of abnormal impact.

Sparse Bayesian learning for wideband direction-of-arrival estimation via beamformer power outputs in a strong interference environment.

Sparse Bayesian learning (SBL) offers a useful tool for wideband direction-of-arrival (DOA) estimation, but its performance is limited in the presence of strong interferences. To solve this problem, this letter attempts to extend the SBL to estimate DOAs via the beamformer power outputs (BPO) because the beamformer can efficiently suppress the interferences. A Bayesian probabilistic model effective for the BPO is proposed. Based on this, a BPO-based SBL method is put forward by adopting the variational Bayesian inference to estimate the DOAs from the BPO. Simulation and experimental results confirm the good performance of the proposed method.

Secure Transmission Scheme Based on Fingerprint Positioning in Cell-Free Massive MIMO Systems

In this paper, we mainly study how to improve the secure transmission performance of cell-free massive multiple-input multiple-output (MIMO) systems by using location technology. In cell-free massive MIMO systems, active pilot attacks will contaminate the uplink channel estimation and affect the downlink precoding of access points (APs). In order to effectively reduce the impact of active pilot attacks on user transmission, this paper respectively proposes an user location estimation method based on fingerprint positioning and a channel estimation algorithm based on location information. Firstly, under the imperfect channel state information, the location information of users and eavesdropper is obtained by using fingerprint positioning method and K-means clustering algorithm. Then, combined with location information, AP selection strategy and channel estimation method based on non-overlapping angle of arrival (AOA) criterion are proposed respectively. Based on the location information of users and eavesdropper, we use discrete Fourier transform (DFT) to distinguish the uplink channels of legitimate user and eavesdropper from the angle domain, thus eliminating the pilot contamination caused by active pilot attacks. The results show that compared with the traditional transmission method, the proposed secure transmission strategy can increase the secrecy rate of up to 2 b/s/Hz, which effectively enhances the secure transmission performance of cell-free massive MIMO in strong interference environment.