Evaluating ultrasound signals of carbon steel fatigue testing using signal analysis approaches

The application of ultrasound techniques to monitor the condition of structures is becoming more prominent because these techniques can detect the early symptoms of defects such as cracks and other defects.The early detection of defects is of vital importance to avoid major failures with catastrophic consequences.An assessment of an ultrasound technique was used to investigate fatigue damage behaviour.Fatigue tests were performed according to the ASTM E466-96 standard with the attachment of an ultrasound sensor to the test specimen.AISI 1045 carbon steel was used due to its wide application in the automotive industry.A fatigue test was performed under constant loading stress at a sampling frequency of 8 Hz.Two sets of data acquisition systems were used to collect the fatigue strain signals and ultrasound signals.All of the signals were edited and analysed using a signal processing approach.Two methods were used to evaluate the signals,the integrated Kurtosis-based algorithm for z-filter technique(I-kaz) and the short-time Fourier transform(STFT).The fatigue damage behaviour was observed from the initial stage until the last stage of the fatigue test.The results of the I-kaz coefficient and the STFT spectrum were used to explain and describe the behaviour of the fatigue damage.I-kaz coefficients were ranged from 60 to 61 for strain signals and ranged from 5 to 76 for ultrasound signals.I-kaz values tend to be high at failure point due to high amplitude of respective signals.STFT spectrogram displays the colour intensity which represents the damage severity of the strain signals.I-kaz technique is found very useful and capable in assessing both stationary and non-stationary signals while STFT technique is suitable only for non-stationary signals by displaying its spectrogram.

RESEARCH OF WAVELET TRANSFORM INSTRUMENT SYSTEM FOR SIGNAL ANALYSIS

After brief describing the Principle of wavelet transform (WT) of signals, a new signals analysis system based on wavelet transform is introduced. The design and development of the instryment of wavelet transform are described. A number of practical uses of this system demonstrate that wavelet transform system is specially functional in identifying and processing impulse, singular and non-smooth signals, so that it should be evaluated the most advanced signal analyzing system.

Signal quality analysis and quality check of BDS3 Precise Point Positioning in the Arctic Ocean

This study analyzes the signal quality and the accuracy of BeiDou 3 rd generation Satellite Navigation System(BDS3) Precise Point Positioning(PPP) in the Arctic Ocean. Assessment of signal quality of BDS3 includes signal to noise ratio(SNR), multipath(MP), dilution of precision(DOP), and code-minus-carrier combination(CC). The results show that, 5 to 13 satellites are visible at any time in the Arctic Ocean area as of September 2018, which are sufficient for positioning. In the mid-latitude oceanic region and in the Arctic Ocean, the SNR is 25–52 dB Hz and the MP ranges from-2 m to 2 m. As the latitude increases, the DOP values show large variation, which may be related to the distribution of BDS satellites. The CC values of signals B1 I and BIC range from-5 m to 5 m in the mid-latitude sea area and the Arctic Ocean, which means the effect of pseudorange noise is small. Moreover, as to obtain the external precise reference value for GNSS positioning in the Arctic Ocean region is difficult, it is hard to evaluate the accuracy of positioning results. An improved isotropy-based protection level method based on Receiver Autonomous Integrity Monitoring is proposed in the paper, which adopts median filter to smooth the gross errors to assess the precision and reliability of PPP in the Arctic Ocean. At first, the improved algorithm is verified with the data from the International GNSS Service Station Tixi. Then the accuracy of BDS3 PPP in the Arctic Ocean is calculated based on the improved algorithm. Which shows that the kinematic accuracy of PPP can reach the decimeter level in both the horizontal and vertical directions, and it meets the precision requirements of maritime navigation.

Application of Empirical Mode Energy to the Analysis of Fluctuating Signals

After an aerial object enters the water, physical changes to sounds in the water caused by the accompanying bubbles are quite complex. As a result, traditional signal analyzing methods cannot identify the real physical object. In view of this situation, a novel method for analyzing the sounds caused by an aerial object’s entry into water was proposed. This method analyzes the vibrational mode of the bubbles by using empitical mode decomposition. Experimental results showed that this method can efficiently remove noise and extract the broadband pulse signal and low-frequency fluctuating signal, producing an accurate resolution of entry time and frequency. This shows the improved performance of the proposed method.

Analysis of the Signal of Singing Using the Vibrato Parameter in the Context of Choir Singers

Evaluation of quality of singing is an issue subjectively realized by the experts. This paper presents the results of the analysis of the vibrato parameter in the singing. The well-known fact is the existence of vibrato of sufficient quality in the voices of professional singers. The authors focus here on the choral voices to assess the quality of their singing from the point of view of the vibrato parameter. The method presented here is developed to evaluate the vibrato while singing under conditions close to the real ones. The study was carried out on the recordings of the members of an academic choir. As a result of tests it was found that not all singers present the same quality of vibrato in terms of deviation of vibrato confidence （STDCV）.

Mathematical theory of signal analysis vs. complex analysis method of harmonic analysis

We present recent work of harmonic and signal analysis based on the complex Hardy space approach.

Accurate expression of neck motion signal by piezoelectric sensor data analysis

The development of high-precision sensors using flexible piezoelectric materials has the advantages of high sensitivity,high stability,good durability,and lightweight.The main problem with sensing equipment is low sensitivity,which is due to the mismatch between materials and analysis methods,resulting in the inability to effectively eliminate noise.To address this issue,we developed the denoising analysis method to motion signals captured by a flexible piezoelectric sensor fabricated from poly(l-lactic acid)(PLLA)and polydimethylsiloxane(PDMS)materials.Experimental results demonstrate that this improved denoising method effectively removes noise components from neck muscle motion signals,thus obtaining high-quality,low-noise motion signal waveforms.Wavelet decomposition and reconstruction is a signal processing technique that involves decomposing a signal into different scales and frequency components using wavelets and then selectively reconstructing the signal to emphasize specific features or eliminate noise.The study employed the sym8 wavelet basis for wavelet decomposition and reconstruction.In the denoised signals,a high degree of stability and periodic peaks are distinctly manifested,while amplitude and frequency differences among different types of movements also become noticeably visible.As a result of this study,we are enabled to accurately analyze subtle variations in neck muscle motion signals,such as nodding,shaking the head,neck lateral flexion,and neck circles.Through temporal and frequency domain analysis of denoised motion signals,differentiation among various motion states can be achieved.Overall,this improved analytical approach holds broad application prospects across various types of piezoelectric sensors,such as healthcare monitoring,sports biomechanics.

Preliminary Biometrics of ECG Signal Based on Temporal Organization through the Implementation of a Multilayer Perceptron Neural Network

The attributes of the ECG signal signifying the unique electrical properties of the heart offer the opportunity to expand the realm of biometrics, which pertains the identification of an individual based on physical characteristics. The temporal organization of the ECG signal offers a basis for composing a machine learning feature set. The four attributes of the feature set are derived through software automation enabled by Python. These four attributes are the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum and the Q wave minimum and S wave minimum relative to the R wave maximum. The multilayer perceptron neural network was applied and evaluated in terms of classification accuracy and time to develop the model. Superior performance was achieved with respect to a reduced feature set considering only the temporal differential of the P wave maximum and T wave maximum relative to the R wave maximum by comparison to all four attributes applied to the feature set and the temporal differential of the Q wave minimum and S wave minimum relative to the R wave maximum. With these preliminary findings and the advent of portable and wearable devices for the acquisition of the ECG signal, the temporal organization of the ECG signal offers robust potential for the field of biometrics.

Experimental analysis on the nonlinear behavior of DC barrier discharge plasmas

Nonlinear behavior of glow discharge plasmas is experimentally investigated.The glow is generated between a barrier semiconductor electrode,Chromium doped namely Gallium Arsenide（Ga As：Cr）,as a cathode and an Indium–Tin Oxide（ITO） coated glass electrode as an anode,in reverse bias.The planar nature of electrodes provides symmetry in spatial geometry.The discharge behaves oscillatory in the time domain,with single and sometimes multiperiodicities in plasma current and voltage characteristics.In this paper,harmonic frequency generation and transition to chaotic behavior is investigated.The observed current–voltage characteristics of the discharge are discussed in detail.

Visualization of the Machine Learning Process Using J48 Decision Tree for Biometrics through ECG Signal

The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.

Anomalies Observed in VLF and LF Radio Signals on the Occasion of the Western Turkey Earthquake(M_(W)=5.7)on May 19,2011

Since 2009 a network of VLF (20 - 60 kHz) and LF (150 - 300 kHz) radio receivers is operating in Europe in order to study the disturbances produced by the earthquakes on the propagation of these signals. In 2011 the network was formed by nine receivers, of which three are located in Italy and one is in Austria, Greece, Portugal, Romania, Russia and Turkey. On May 19, 2001 an earthquake (Mw = 5.7) occurred in western Turkey, that is inside the “sensitive” area of the network. The radio data collected during April-May 2011 were studied using the Wavelet spectra, the Principal Component Analysis and the Standard Deviation trends as different methods of analysis. Evident anomalies were revealed both in the signals broadcasted by the TRT transmitter (180 kHz) located near Ankara and in a VLF signal coming from a transmitter located in Western Europe and collected by the receiver TUR of the network located in eastern Turkey. Evident precursor phases were pointed out. Some differences in the efficiency of the three analysis methods were revealed.

Operational Modal Analysis of a Ship Model in the Presence of Harmonic Excitation

A ship is operated under an extremely complex environment, and waves and winds are assumed to be the stochastic excitations. Moreover, the propeller, host and mechanical equipment can also induce the harmonic responses. In order to reduce structural vibration, it is important to obtain the modal parameters information of a ship. However, the traditional modal parameter identification methods are not suitable since the excitation information is difficult to obtain. Natural excitation technique-eigensystem realization algorithm （NExT-ERA） is an operational modal identification method which abstracts modal parameters only from the response signals, and it is based on the assumption that the input to the structure is pure white noise. Hence, it is necessary to study the influence of harmonic excitations while applying the NExT-ERA method to a ship structure. The results of this research paper indicate the practical experiences under ambient excitation, ship model experiments were successfully done in the modal parameters identification only when the harmonic frequencies were not too close to the modal frequencies.

Dynamics modeling and electromechanical coupling characteristics analysis of cage induction motors

Induction motors, as typical electromechanical energy conversion devices, have received limited attention in previous studies on electromechanical coupling vibrations, precise modeling, and the use of electromechanical coupling effects for fault diagnosis and condition assessment in motor drive systems. This study proposes a comprehensive model of cage induction motors that integrates the multiple coupled circuit model with a rotor-bearing dynamics model. The model accounts for the linear increase in the magnetomotive force across the slot and incorporates the skidding characteristics of bearings in the rotor-bearing system. By calculating the time-varying mutual inductance parameters based on the air-gap distribution in the vibration environment, the electromechanical coupling vibration of the cage motor is investigated. Furthermore, this study examines the electromechanical coupling vibration characteristics influenced by various factors, including bearing clearances, radial loads, and the vertical excitation frequencies of the stators. The results show that the proposed model improves the excitation inputs for the electrical and mechanical systems of the motor compared with conventional models. Increased bearing clearance and radial load affect the current and torque similarly but have opposite effects on the slip ratio. This study provides a deeper understanding of electromechanical coupling mechanisms and facilitates the use of such phenomena for fault diagnosis and condition assessment in motor-driven systems.

Adaptive Variational Mode Decomposition for Bearing Fault Detection

Rolling element bearings are commonly used in rotary mechanical and electrical equipment. According to investigation, more than half of rotating machinery defects are related to bearing faults. However, reliable bearing fault detection still remains a challenging task, especially in industrial applications. The objective of this work is to propose an adaptive variational mode decomposition (AVMD) technique for non-stationary signal analysis and bearing fault detection. The AVMD includes several steps in processing: 1) Signal characteristics are analyzed to determine the signal center frequency and the related parameters. 2) The ensemble-kurtosis index is suggested to decompose the target signal and select the most representative intrinsic mode functions (IMFs). 3) The envelope spectrum analysis is performed using the selected IMFs to identify the characteristic features for bearing fault detection. The effectiveness of the proposed AVMD technique is examined by experimental tests under different bearing conditions, with the comparison of other related bearing fault techniques.

虚拟动态信号测试仪的实现与应用

利用虚拟仪器的概念和 FFT技术 ,以 PC机和通用的 D/ A、A/ D转换及数字I/ 0卡作为仪器硬件 ,开发出满足一般控制系统分析使用的动态范围 0 .1～ 2 0 0 0 Hz的虚拟动态信号测试仪器 ,并给出了利用该仪器对实际控制系统的测试结果。

A Quotient Space Approximation Model of Multiresolution Signal Analysis

In this paper, we present a quotient space approximation model of multiresolution signal analysis and discuss the properties and characteristics of the model. Then the comparison between wavelet transform and the quotient space approximation is made. First, when wavelet transform is viewed from the new quotient space approximation perspective, it may help us to gain an insight into the essence of multiresolution signal analysis. Second, from the similarity between wavelet and quotient space approximations, it is possible to transfer the rich wavelet techniques into the latter so that a new way for multiresolution analysis may be found.

Characteristics analyzing and parametric modeling of the arc sound in CO_2 GMAW for on-line quality monitoring

For on-line monitoring of welding quality, the characteristics of the arc sound signals in short circuit CO2 GMAW were analyzed in the time and frequency domains. The arc sound presents a series of ringing-like oscillations that occur at the end of short circuit i. e. the moment of arc re-ignition, and distributes mainly in the frequency band below 10 kHz. A concept of the arc tone channel and its equivalent electrical model were suggested, which is considered a time-dependent distributed parametric system of which the transmission properties depend upon the geometric and physical characteristics of the arc and surroundings, and is excited by the sound source results from the change of arc energy so that results in arc sound. The linear prediction coding （ LPC ） model is an estimation of the tone channel. The radial basis function （ RBF ） neural networks were built for on-line pattern recognition of the gas-lack in welding, in which the input vectors were formed with the LPC coefficients. The test results proved that the LPC model of arc sound and the RBF networks are feasible in on-line quality monitoring.

An Improved Singularity Computing Algorithm Based on Wavelet Transform Modulus Maxima Method

In order to reduce the hidden danger of noise which can be charactered by singularity spectrum, a new algorithm based on wavelet transform modulus maxima method was proposed. Singularity analysis is one of the most promising new approaches for extracting noise hidden information from noisy time series . Because of singularity strength is hard to calculate accurately, a wavelet transform modulus maxima method was used to get singularity spectrum. The singularity spectrum of white noise and aluminium interconnection electromigration noise was calculated and analyzed. The experimental results show that the new algorithm is more accurate than tradition estimating algorithm. The proposed method is feasible and efficient.

Identification and expression of the CEP gene family in apple (Malus×domestica)

Plant peptide hormones play important roles in plant growth and development. Among these hormones, the C-TERMINALLYENCODED PEPTIDE（CEP） belongs to a newly found peptide family that regulates root development in Arabidopsis as well as in other species. However, nothing is known about the CEP genes in apple（Malus×domestica, MdCEP）. In this study, a total of 27 apple CEP genes were identified through a genome-wide analysis and were phylogenetically divided into three classes（Ⅰ, Ⅱ and Ⅲ）. The predicted MdCEP genes were distributed across 10 of 17 chromosomes with different densities. Next, the gene structures and motif compositions of the MdCEP genes were analyzed. Subsequently, the expression analysis suggested that the MdCEP genes were highly activated in roots and that MdCEP23 may play an important role in regulating the growth and development of roots. Moreover, all of the MdCEP genes were responsive to multiple abiotic stresses, indicating that MdCEP genes may be involved with various aspects of physiological processes in apple. Nearly one-third of MdCEP genes had a significant response to low nitrogen treatment. Most of the MdCEP genes were up-regulated under stress, including mannitol, polyethylene glycol（PEG） and abscisic acid（ABA）, suggesting that MdCEP genes may be involved in the drought stress response. This study provides insight into the putative functions of the MdCEP genes using a genome-wide analysis of the CEP gene family.

Acoustic signal analysis of underwater elastic cylinder

The echoes of underwater elastic cylinder comprise two types of acoustic components: Geometrical scattering waves and elastic scattering waves. The transfer function is appropriate to characterize the echo of targets. And the discrete wavelet transform of amplitude spectrum is presented and used to identify the resonant components of underwater targets.