Noise Signal Identification Research Articles

Enabling Boomless CubeSat Magnetic Field Measurements With the Quad‐Mag Magnetometer and an Improved Underdetermined Blind Source Separation Algorithm

AbstractIn situ magnetic field measurements are often difficult to obtain due to the presence of stray magnetic fields generated by spacecraft electrical subsystems. The conventional solution is to implement strict magnetic cleanliness requirements and place magnetometers on a deployable boom. However, this method is not always feasible on low‐cost platforms due to factors such as increased design complexity, increased cost, and volume limitations. To overcome these problems, we propose using the Quad‐Mag CubeSat magnetometer with an improved Underdetermined Blind Source Separation (UBSS) noise removal algorithm. The Quad‐Mag consists of four magnetometer sensors in a single CubeSat form‐factor card that allows distributed measurements of stray magnetic fields. The UBSS algorithm can remove stray magnetic fields without prior knowledge of the magnitude, orientation, or number of noise sources. UBSS is a two‐stage algorithm that identifies signals through cluster analysis and separates them through compressive sensing. We use UBSS with single‐source point detection to improve the identification of noise signals and iteratively‐weighted compressed sensing to separate noise signals from the ambient magnetic field. Using a mock CubeSat, we demonstrate in the lab that UBSS reduces four noise signals producing more than 100 nT of noise at each magnetometer to below the expected instrument resolution (5 nT at 65 Hz). Additionally, we show that the integrated Quad‐Mag and improved UBSS system works well for 1U, 2U, 3U, and 6U CubeSats in simulation. Our results show that the Quad‐Mag and UBSS noise cancellation package enables high‐fidelity magnetic field measurements from a CubeSat without a boom.

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

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

Noise signal identification in time projection chamber data using deep learning model

Deep learning has been employed in various scientific fields and has provided promising results. In this study, a deep learning classifier was implemented to improve the quality of data obtained from a time projection chamber. Digital waveforms of the detected signals were classified into the following three categories: particles, noises, and particles piled up with noises. A simple 1-dimensional convolutional neural network was developed for the classification. The model demonstrated an excellent performance on the test dataset. Its practical performance was also examined using track images and particle identification plots by comparing the original and clean data without the noise signals. The comparison clearly showed that the deep learning model improved the quality of data. The current study presents an effective application of the deep learning model for the time projection chamber data.

GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition

The purpose of seismic data processing in nuclear explosion monitoring is to accurately and reliably detect seismic or explosion events from complex ambient noises. Accurate detection and identification of seismic phases are of great significance to the detection and parameter estimation of seismic events. In seismic phase identification, discriminating between noise signals and real seismic signals is essential. Accurate identification of noise signals helps reduce false detections, improves the accuracy of automatic bulletins, and relieves the workload of analysts. At the same time, in seismic exploration, the prime objective in data processing is also to enhance the signal and suppress the noises. In this study, we combined a generative adversarial network (GAN) with a long short-term memory network (LSTM) to discriminate between noise and phases in seismic waveforms recorded by the International Monitoring System (IMS) array MKAR. First, using the beamforming data of the array as the input, we obtained the signal features of seismic phases through the learning of the GAN discriminator network. Then, we input these features and trained the joint network on mixed seismic phase and noise data, and successfully classified seismic phases and noise signals with a recall of 95.28% and 97.64%, respectively. Based on this model, we established a real-time data processing method, then validated the effectiveness of this method with real 2019 data of MKAR. We also verified whether improved noise signal identification improves the quality of phase association and event detection.

VIBROSIEVE DIAGNOSTICS OF ELECTROMAGNETIC EQUIPMENT

In the article one of variants of checking the operability of relay equipment for railway automation is examined with the use of method of noise diagnostics (an identification of noise signals on the basis of using the moments of density of probability distribution for random noise signals).

Strategies for the Subsystem and Noise Signal Identification of Hierarchically Structured Systems

For hierarchically structured systems consisting of linear subsystems an approach for the determination of strategies for the subsystem and noise signal identification is given. This approach is based on the solvability of the matrix equation system formed by the spectral density matrices of the input and output signals. It is applied to a hierarchically structured system consisting of three levels. First it is shown that for the identification of closed loop subsystems only the direct method and the correlation method should be used. Then for some selected subsystems and noise signals unknown in the system under consideration corresponding strategies are derived. The results obtained for different signals measured and a priori information known are discussed under the point of view of an optimal design of the identification experiments.

Noise Signal Identification in Hierarchically Structured Systems

For hierarchically structured systems consisting of linear subsystems an approach is given for the determination of nonparametric models for the immeasurable noise signals. This approach is based on the solvability of the matrix equation system formed by the spectral density matrices of the input and output signals measured. For some selected noise signals it is derived what input and output signals must be measured for the noise signal identification. Furthermore the replacement of an input or output signal by another one is shown. The results obtained for several experimental conditions (signals measured, a priori information known) are discussed under the point of view of an optimal design of the identification experiments.

Adaptive noise cancellation in neuromagnetic measurement systems

The noise spectrum of a superconducting second-derivative gradiometer has been investigated in an urban environment. Data have been acquired from the gradiometer with three orthogonal accelerometers and a triaxial fluxgate magnetometer attached to the dewar. These data have been analyzed by using signal processing techniques, primarily an adaptive noise canceller, to reduce noise in the gradiometer data. Results shown here indicate that the environmental noise and/or the dewar motion noise can be reduced as much as 40 dB in noise power. It is also shown, in the case of poor signal-to-noise ratio (S/N∼1), that a 10 Hz sine wave can be extracted. This technique not only shows promise for noise reduction, but also aids in the identification of noise signals which might be misconstrued as part of the evoked response. Analysis of data containing evoked response is now underway.