Spectrum Analysis Of Signals Research Articles

Nonlinear vibro-acoustic modulation for microcrack detection of steel strands based on S-transform bispectrum

Fatigue crack detection is an important issue to ensure the safety of steel strands. The key to solve this problem is to extract the nonlinear response generated by fatigue cracks. In this paper, the nonlinear VAM method is used to detect the structural cracks. The structure with fatigue cracks is excited using low-frequency pumping and high-frequency probing, and the power spectrum analysis of the modulated signal is carried out. In view of the shortcomings of spectral analysis, a new method combining S-transform and bispectrum is proposed, which is called S-transform bispectrum. S transform contains the phase factor, which can retain the absolute phase characteristics of each frequency, and has good time–frequency multi-scale focusing performance. The bispectrum can suppress Gaussian noise, retain phase information, and quantitatively describe the quadratic phase coupling in the signal. Then the simulation and experiment of damaged straight rod, helical rod, and steel strands are carried out. The results show that the proposed method can effectively detect the nonlinear features by using the sideband peaks in the S-transform bispectrum three-dimensional plot, and the nonlinear features are important to identify the structure with damage. At the same time, in order to prove the ability of S-transform bispectrum, an S-transform bispectrum detector is used to verify it, which is superior to the spectrum in terms of its ability to localize modulation sidebands. The proposed S-transform bispectrum has a good application prospect and provides a new tool for structural damage detection.

Application of Acoustic Emission Technology for Bearing Condition Monitoring on Passenger Ropeway

Rolling bearings are important stressed components of driving wheels and detour wheels on passenger ropeway, and their failure may lead to accidents and casualties. After the ropeway is installed and put into operation, the rolling bearings in the wheels are difficult to disassemble. Once disassembled, the bearing may be damaged. Due to the installation position and the complex operation environment, the health status of the rolling bearings is a blind spot for ropeway inspection and detection. Acoustic emission detection technology is used to monitor the rolling bearings of passenger ropeways. After a large number of field tests, the acoustic emission monitoring program and signal analysis method of rolling bearing on ropeway were proposed. The parameter characteristics of acoustic emission signals obtained in bearing operating were analyzed from two aspects: the ropeway operating cycle and the bearing operating cycle. Spectrum analysis of typical signals was performed, and the spectrum characteristics of typical acoustic emission signals of rolling bearing during the operation of the ropeway were obtained. On-site tests results show that using acoustic emission technology to perform condition monitoring and fault diagnosis of low-speed rolling bearings on passenger ropeways has good application prospects.

Exploring the neural correlates of fat taste perception and discrimination: Insights from electroencephalogram analysis

Understanding neural pathways and cognitive processes involved in the transformation of dietary fats into sensory experiences has profound implications for nutritional well-being. This study presents an efficient approach to comprehending the neural perception of fat taste using electroencephalogram (EEG). Through the examination of neural responses to different types of fatty acids (FAs) in 45 participants, we discerned distinct neural activation patterns associated with saturated versus unsaturated fatty acids. The spectrum analysis of averaged EEG signals revealed notable variations in δ and α-frequency bands across FA types. The topographical distribution and source localization results suggested that the brain encodes fat taste with specific activation timings in primary and secondary gustatory cortices. Saturated FAs elicited higher activation in cortical associated with emotion and reward processing. This electrophysiological evidence enhances our understanding of fundamental mechanisms behind fat perception, which is helpful for guiding strategies to manage hedonic eating and promote balanced fat consumption.

A feature extraction method of rub-impact based on adaptive stochastic resonance and Hjorth parameter.

The characteristic frequency of a rub-impact fault is usually very complex and may contain higher harmonics and subharmonics. Due to the uncertainty of harmonic components and the complexity of signal-to-noise ratio (SNR) operation, the general scale transformation stochastic resonance (GSTSR) has certain limitations in the identification of rub-impact faults. To solve this problem, the paper starts with complexity and proposes a rub-impact fault identification method combining a swarm intelligence optimized algorithm (SIOA) with Hjorth parameters and GSTSR. The complexity of vibration signals will change greatly before and after rub-impact faults. The complexity parameter in Hjorth parameters can effectively embody the complexity of signals and is invulnerable to noise interference. Therefore, the complexity parameter in the Hjorth parameters is taken as the objective function of SIOA and combined with GSTSR. Vibration signals from cases are taken as input to adaptive stochastic resonant (ASR) systems, and the system parameters are adaptively and synchronously adjusted to realize the maximal resonant effect. Finally, the spectrum analysis of signals obtained from ASR is used to extract failure features and recognize faults in the rotor-stator rub-impact. The proposed method is verified by comparing it with other schemes under different SIOAs and different operating conditions. The result of the comparison shows that the complexity parameter of the Hjorth parameters can be taken as the objective function of SIOA to accurately identify the rub-impact fault. Meanwhile, the proposed method, compared with the method of taking SNR as an objective function, has a better effect on reducing time costs and strengthening fault characteristics.

Research on Magnetic Memory Detection of Weld Fatigue Damage Based on Two-Dimensional Spectral Entropy

Abstract In light of the intrinsic relationship between weld fatigue damage and the characteristics of magnetic leakage signals, fatigue experiments were conducted on butt welds to analyze the evolution of magnetic leakage signal as fatigue cycles increased. The introduction of two-dimensional spectral entropy allowed for the construction of a fatigue stage division model based on the two-dimensional spectral entropy diagram. The results revealed that distinctions between magnetic leakage signal under loaded and unloaded conditions were solely evident in their magnitude, with consistent curve distribution trends observed across various fatigue cycles. Furthermore, the distribution pattern of magnetic leakage signal displayed significant fluctuations, characterized by a sharp gradient increase following the emergence of fatigue cracks. These distribution characteristics of magnetic leakage signal differed markedly from the magnetic field distribution at other fatigue stages, with a higher growth rate observed as fatigue cycles increased. The two-dimensional spectral entropy spectrum analysis of magnetic leakage signals at different fatigue stages indicated the disorder in the magnetic leakage signal distribution within the high fatigue zone, signifying the expansion of fatigue microcracks. To some extent, two-dimensional spectral entropy can serve as an early warning system for detecting fatigue cracks in welds and achieving early identification of fatigue microcracks.

Spectrum analysis of digital UPWM signals generated from random modulating signals

This work studies the spectrum of discrete-time Uniform-sampling pulse width modulation (UPWM) signals originating from stochastic input signals. It demonstrates that for ergodic input sequences of independent and identically distributed random variables, the Discrete Fourier Transform (DFT) of the UPWM signals can be directly estimated from the input signal’s statistics. Consequently, it is shown that if the input signal can be modeled as such a random sequence, only statistical information of the sequence is required for the accurate estimation of the DFT of the UPWM signal. This is achieved here by proving that the DFT estimators obtained by observation of the input sequence within a time window are consistent estimators of the DFT coefficients of the underlying random process. Moreover, for signals whose generalized probability density functions can be expressed as functions of a small number of parameters, the DFT coefficients can be estimated or even calculated via closed-form expressions with linear complexity. Examples are given for input signals derived from symmetric and asymmetric distributions. The results are validated by comparison with evaluations of the UPWM signal’s DFT via the Fast Fourier Transform (FFT). The proposed method provides a mathematical framework for the analysis and design of UPWM systems whose inputs have known statistical properties.

A transformer acoustic signal analysis method based on matrix pencil and hybrid deep neural network

Acoustic signal analysis is an important component of transformer online monitoring. Currently, traditional methods have problems such as low spectral resolution, imbalanced sample distribution, and unsatisfactory classification performance. This article first introduces the matrix pencil algorithm for time-frequency spectrum analysis of acoustic signals, and then uses the SMOTE algorithm to expand the imbalanced samples. Then, an ACmix hybrid deep neural network model is constructed to classify 11 types of transformer operation and environmental acoustic signals. Finally, detailed experiments were conducted on the method proposed in this paper, and the experimental results showed that the matrix pencil algorithm has high time-frequency resolution and good noise resistance performance. The SMOTE sample expansion method can significantly improve the recognition accuracy by more than 2%. Overall accuracy of the proposed method in acoustic signal classification tasks reaches 91.81%.

Generalized spectrum analysis of Chirp Cyclostationary signals associate with linear canonical transform

A chirp cyclostationary (CCS) process is a nonstationary stochastic process, whose statistics have been defined and utilized in radar and communication signal processing. However, spectrum analysis of CCS signals, which provides a spectral view to characterize a signal, has not been performed. Classical spectrum analysis is based on the Fourier transform, which is not suitable for CCS signals because of the multiplicative chirp signals in a CCS model. In this study, we propose a generalized spectral representation of chirp cyclostationary signals associated with a linear canonical transform. The proposed representation shows that a CCS signal can be represented by a linear combination of a series of jointly stationary and equal-bandwidth processes, which bridge the nonstationary and stationary processes. Furthermore, the relationships between the second-order statistics of the CCS process and its stationary representations are introduced. Finally, based on the above observations, a linear time-varying Wiener filter was designed for CCS signal denoising and time-varying channel estimation. Finally, the relationships between the nonstationarity of the input and output and time-varying properties of the systems are discussed, which are helpful for determining the nonstationarity of the outputs or the time-varying property of a system.

ВИБІР НЕЙРОМЕРЕЖЕВОГО КЛАСИФІКАТОРА ДЛЯ ВИРІШЕННЯ ЗАВДАННЯ АНАЛІЗУ СИГНАЛІВ РЕБ ТА ШТУЧНИХ ЗАВАД

At present, the task of ensuring high-quality control of unmanned aerial vehicles and the transmission of reliable information in a complex interference environment is relevant. This task becomes especially important when intentional interference is used in the form of countermeasures. Various methods are used to detect and localize powerful sources of parasitic signals, including: time domain methods: signal spectrum analysis, correlation function analysis, phase shift analysis; frequency domain methods: signal amplitude response analysis, signal phase response analysis, power spectral density analysis; spatial domain methods: antenna directivity analysis, signal strength analysis at different points in space. Analyzing signals affected by EW and artificial interference is a challenging task. Traditional signal analysis methods are often ineffective for this task because they cannot take into account the complex characteristics of EW and artificial interference. Neural network classifiers are a promising tool for analyzing signals with EW and artificial interference. Neural networks can learn from large datasets and detect complex patterns that cannot be detected by traditional methods. This article describes the process of selecting a neural network classifier for analyzing EW and artificial interference signals. Various factors that should be considered when choosing a neural network architecture, training algorithm, and network parameters are discussed). The paper presents an analytical review of neural network classifiers, their varieties and features, and selects the optimal one for solving the task.

Feature Extraction and Musical Personality Analysis of Piano Artistic Performances

Abstract Starting from the basic process of piano music recognition, this paper focuses on the feature extraction process of piano art performance. In the feature extraction of piano art performance, the fundamental frequency characteristics of piano music signals were analyzed by using short-time Fourier transform theory, focusing on the feature extraction of musical pitch, time value, and tonality, and the spectrum analysis of piano music signals and the reconstruction of piano timbre were completed. The piano performance work, “Autumn Moon on the Peaceful Lake,” is used as an example to analyze the musical characteristics and personality. The overall structure of “Autumn Moon on the Peaceful Lake” is a typical three-part structure: the key is the bD palace seven-tone mode, the key is single, and the marking range of performance strength is ppp-pp-p-mf-mf-f-ff. In the three versions played by Yin Chengzong, Lang Lang, and Chen Jie, the average tempo of the performance was 31.5 beats, 26.5 beats, and 26.8 beats, respectively. The performance process of different pianists is a “slow-fast-slow” layout, and other passages are added to the individual free processing to further enhance the charm of the work.

A Comprehensive Investigation of Fault Signatures and Spectrum Analysis of Vibration Signals in Distributed Bearing Faults

A Comprehensive Investigation of Fault Signatures and Spectrum Analysis of Vibration Signals in Distributed Bearing Faults

Machine learning-enhanced gesture recognition through impedance signal analysis.

Gesture recognition is a crucial aspect in the advancement of virtual reality, healthcare, and human-computer interaction, and requires innovative methodologies to meet the increasing demands for precision. This paper presents a novel approach that combines Impedance Signal Spectrum Analysis (ISSA) with machine learning to improve gesture recognition precision. A diverse dataset that included participants from various demographic backgrounds (five individuals) who were each executing a range of predefined gestures. The predefined gestures were designed to encompass a broad spectrum of hand movements, including intricate and subtle variations, to challenge the robustness of the proposed methodology. The machine learning model using the K-Nearest Neighbors (KNN), Gradient Boosting Machine (GBM), Naive Bayes (NB), Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) algorithms demonstrated notable precision in performance evaluations. The individual accuracy values for each algorithm are as follows: KNN, 86%; GBM, 86%; NB, 84%; LR, 89%; RF, 87%; and SVM, 87%. These results emphasize the importance of impedance features in the refinement of gesture recognition. The adaptability of the model was confirmed under different conditions, highlighting its broad applicability.

Investigation of wetting and drying process in a spout-fluid bed using acoustic sensor and electrical capacitance tomography

To investigate complex gas-solids flow characteristics of a spout-fluid bed with bafflers in wetting and drying process, a non-intrusive measurement technique combining pressure, acoustic transmitters and an electrical capacitance tomography (ECT) sensor is proposed in this paper. The proposed approach can collect acoustic and pressure signals and realize visualization in three flow regions separated by two bafflers synchronously. By ECT images and power spectrum and wavelet analysis of the pressure drop signals, two typical flow state transitions in the wetting and drying process can be identified. The result analysis based on acoustic transmitter indicates that the particle–wall collision frequency band is narrow and the particle–particle collision frequency band is wide. A key recurrence quantization parameter of acoustic signal, Laminarity, increases with the decrease of particle flowability. The parameter of Laminarity combined with the capacitance signal related to the particle moisture content can effectively reflect the wetting rate and provide an accurate monitoring of de-fluidization process in the bed.

Analysis of pressure pulsation and energy characteristics of guide vane for axial flow pump based on Hilbert-Huang transform considering impeller-guide vane interaction.

In order to explore the characteristics of pressure pulsation signals and energy distribution of water flow at the guide vane considering impeller-guide vane interaction. The numerical simulation of the vertical axial flow pump device's steady and unsteady three-dimensional flow fields was carried out. The Hilbert-Huang method was used to conduct empirical mode decomposition decomposition and Hilbert spectrum analysis of pressure pulsation signal at each monitoring point in the inlet and outlet regions of the guide vane. The results show: Under the condition of 0.3Qbep, the internal pressure of the guide vane is obviously affected by the impeller, and there are large block-shaped vortex structures in the guide vane. Under the operating conditions of 1.0Qbep and 1.2Qbep, the size of the pressure area in the guide vane is basically not affected by the impeller, and the vortex structures in the guide vane are concentrated near the outlet of the guide vanes, and there are long strip-shaped vortex structures at the edge of the guide vane. The size and number of vortex structures decrease with the increase in flow rate. The pressure pulsation signal at the inlet of the guide vane is affected by the rotation of the impeller and exhibits good periodicity, with the main frequency centered around 146 Hz, and the energy ratio of the main frequency is up to 97.7%. There are low-frequency signals below 100 Hz and high-frequency signals fluctuating around 146 Hz in all three flow conditions. When the flow rate increases, the fluctuation amplitude of the high-frequency signal increases. The flow rate has a significant impact on the water flow at the outlet of the guide vane. At 0.3Qbep, its frequency is distributed in the range of 0-500 Hz, mainly concentrated in the area below 400 Hz. At 1.0Qbep, the frequency of pressure pulsation is distributed below 250 Hz after the guiding function of the guide vane. At 1.2Qbep, the water flow is mainly controlled by the rotation of the impeller, and after the energy recovery of the guide vane, its main frequency is still concentrated around 150 Hz, which is 337.2% and 268.5% of 0.3Qbep and 1.0Qbep. Under the working condition of 0.3Qbep, the proportion of intrinsic mode function energy corresponding to the dominant frequency at the center of the guide vane inlet is as high as 95.9%, and the proportion of intrinsic mode function energy corresponding to the dominant frequency at the shroud side and hub side of the guide vane is rather low. If the flow rate rises from 0.3Qbep to 1.2Qbep, the proportion of intrinsic mode function energy increases by more than 42%. Under the working conditions of 0.3Qbep and 1.0Qbep, the main frequency of pressure pulsation signal of water flow at the guide vane outlet is less affected by the impeller and the corresponding energy proportion is low. Under the working condition of 1.2Qbep, the main frequency of pressure pulsation signal is 4 times the rotational frequency and the corresponding energy proportion is higher than 60%.

Fourier transform-based electronic logic information processing architecture design

Abstract To better develop the electronic information industry and make the electronic logic information processing architecture more widely used in production life. This paper uses the Fourier transform model based on the discrete Fourier transform and fast Fourier transform data simulation, for the electronic logic information architecture of single-signal and dual-signal signal spectrum analysis for simulation analysis. The electronic logic signal, whether it contains whole harmonics, non-harmonics, or attenuated DC components, can be accurately decomposed even when the system frequency deviates from 50 Hz, and the error value is guaranteed to be around 0.0016%. It is also pointed out that the presence of the attenuated DC component does not affect the results. The simulation results show that the design of electronic logic information processing architecture based on Fourier transform is better than other models under the sample data conditions. And the Fourier transform model can effectively guarantee the probability strength of logic information transfer and enhance the reliable line of results, which provides a research direction for the design of electronic logic information processing architecture.

Evaluation of Drug Abuse on Brain Function using Power Spectrum Analysis of Electroencephalogram Signals in Methamphetamine, Opioid, Cannabis, and Multi-Drug Abuser Groups

The effect of different types of substances on brain function is still challenging; however, many studies have shown the functional and structural damage to the brain under influence of substance abuse. This study aimed to quantitatively compare the effect of opioid (Op), methamphetamine (Meth), cannabis (Can), and simultaneous methamphetamine and opioid (Multi-Drug (MD)) abuse on brain function. Furthermore, the impacts of pure Op and Meth abuse were considered with simultaneous substance abuse. In this descriptive study, the electroencephalogram (EEG) signal was recorded from 52 participants in the Meth, Op, Can, and MD abusers, and the Healthy Control (HC) groups at rest state. EEG data were analyzed on the frequency domain with electrode-based, cortex-based, and hemisphere-based approaches. However, the power spectrum in the delta band in the Op group, the gamma band in the Can group, and the gamma and beta bands in the MD group more significantly increased compared to the HC group, the power spectrum values in the Meth group reduced in the alpha, beta, and gamma bands. Moreover, the power spectrum values in the MD group more significantly higher than the Meth and Op groups in the beta and gamma bands. Since substance abuse in different types caused various changes in frequency components, the different power spectrum bands analysis in abusers can be reasonable to apply as a biomarker to detect the drug types.

Effects of Different Culture Densities on the Acoustic Characteristics of Micropterus salmoide Feeding

The intensity and frequency of the acoustic signals generated by different behaviors of largemouth bass (Micropterus salmoides) have different characteristics. The acoustic signals generated during feeding can be used to analyze the characteristic patterns of their used behavior, which can provide a theoretical basis for applications such as automatic feeding based on acoustic signals. We used passive acoustics combined with video to study the feeding acoustic signals of largemouth bass in a recirculating water culture system (4, 8, 12, and 16 fish/m3). The result of the time–frequency and power spectrum analysis of the feeding sound signals showed that the short-time average amplitude of the feeding signal was well distinguished from the background noise, both the swallowing and the chewing sounds were positively correlated with the culture density, and the correlation between the swallowing sound and number of fish was stronger; at different culture densities, the short-time average zero-crossing in the swallowing phase of the largemouth bass suddenly dropped to about 500 and rose to about 1000 in the chewing process. Therefore, both the chewing and the swallowing sounds can be used as parameters to automatically identify the largemouth bass feeding process signal. The spectrum entropy in the feeding process of the largemouth bass was maintained at about 4–6 for different culture densities. In the power spectrum, the main feeding sounding frequencies of the largemouth bass at different farming densities and the distinguishable spectral range of the background noised ranged from 1 to 20 kHz, the main peak frequency of the feeding sound was within the range of 1.2 to 3.0 kHz, and the main power value of the power spectrum was positively correlated with farming density.

Experimental validation of vibrodiagnostic signs of defects in transmission assembly

A method for diagnosing the main defects of the transmission shafts — imbalance and misalignment is proposed. The method is based on the analysis of vibration signal spectra and their statistical processing, which makes it possible to determine the signs of separation of these defects. Keywords:transmission, imbalance, misalignment, vibration signal spectrum, statistical processing. andrienko@bmstu.ru, aazemskov@ciam.ru, kuts@bmstu.ru

Partition Management of Soil Nutrients Based on Capacitive Coupled Contactless Conductivity Detection

A method based on capacitively coupled contactless conductivity detection (C4D), which has been proven effective for the rapid detection of available soil potassium content, was firstly proposed to apply to soil nutrient detection. By combining a detection signal spectrum analysis, geographic information system (GIS) data, and a cluster analysis, a soil nutrient management system to match the detection device was developed. This system included six modules: soil sample information management, electrophoresis analysis, quantitative calculation, nutrient result viewing, cluster analysis, and nutrient distribution map generation. The soil samples, which were collected from an experimental field in Xuchang City of Henan Province, were analyzed using the C4D and flame photometer methods. The results showed that the detection results for the soil samples obtained via the two methods were in good agreement. C4D technology was feasible for the detection of the soil available nutrients and had the advantages of a high timeliness, low sample volume, and low pollution. The soil nutrient management system adopted the hierarchical clustering method to classify the grid cells of the experimental field according to the nutrient detection results. A soil nutrient distribution map displayed the spatial difference in nutrients. This paper provides a systematic solution for soil nutrient zone management that includes nutrient detection, signal analysis, data management for the nutrient zone, and field nutrient distribution map generation to support decision making in variable fertilization.

Detection of Oscillatory Failures in Hydraulic Actuators of Aircraft using Linear Predictive Coding and Signal Spectrum Analysis

This paper focuses on fault detection for oscillatory failures in hydraulic actuators of aircraft. These oscillatory failures, if not rectified in time, can cause severe loads on the airframe and eventually lead to structural damage. In this paper, a novel oscillatory failure case (OFC) detection algorithm is proposed which uses a nonlinear observer based on the mathematical model of the actuator in order to generate residual following a Linear Predictive Coding analysis of the residual to detect oscillatory behavior. Finally, OFC decisions are made after the quantification of the residual in frequency domain. To illustrate the effectiveness of the proposed algorithm, results are presented using a high-fidelity industrial benchmark simulation. Furthermore, a comparative study is provided against an existing technique.