Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study,the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform(STFT) to obtain the corresponding time-frequency map.Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network(CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization,it was shown that,in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

Application of short-time Fourier transform to high-rise frame structural-health monitoring based on change of inherent frequency over time

The high-rise frame structure has become more and more widespread, like its damage from the complication of the environment. The traditional method of damage detection, which is only suitable for the stationary signal, does not apply to a high-rise frame structure because its damage signal is non-stationary. Thus, this paper presents an application of the short-time Fourier transform(STFT) to damage detection of high-rise frame structures. Compared with the fast Fourier transform, STFT is found to be able to express the frequency spectrum property of the time interval using the signal within this interval. Application of STFT to analyzing a Matlab model and the shaking table test with a twelve-story frame-structure model reveals that there is a positive correlation between the slope of the frequency versus time and the damage level. If the slope is equal to or greater than zero, the structure is not damaged. If the slope is smaller than zero, the structure is damaged, and the less the slope is, the more serious the damage is. The damage results from calculation based on the Matlab model are consistent with those from the shaking table test, demonstrating that STFT can be a reliable tool for the damage detection of high-rise frame structures.

Performance of Continuous Wavelet Transform over Fourier Transform in Features Resolutions

This study presents a comparative analysis of two image enhancement techniques, Continuous Wavelet Transform (CWT) and Fast Fourier Transform (FFT), in the context of improving the clarity of high-quality 3D seismic data obtained from the Tano Basin in West Africa, Ghana. The research focuses on a comparative analysis of image clarity in seismic attribute analysis to facilitate the identification of reservoir features within the subsurface structures. The findings of the study indicate that CWT has a significant advantage over FFT in terms of image quality and identifying subsurface structures. The results demonstrate the superior performance of CWT in providing a better representation, making it more effective for seismic attribute analysis. The study highlights the importance of choosing the appropriate image enhancement technique based on the specific application needs and the broader context of the study. While CWT provides high-quality images and superior performance in identifying subsurface structures, the selection between these methods should be made judiciously, taking into account the objectives of the study and the characteristics of the signals being analyzed. The research provides valuable insights into the decision-making process for selecting image enhancement techniques in seismic data analysis, helping researchers and practitioners make informed choices that cater to the unique requirements of their studies. Ultimately, this study contributes to the advancement of the field of subsurface imaging and geological feature identification.

Enhanced Fourier Transform Using Wavelet Packet Decomposition

Many domains, including communication, signal processing, and image processing, use the Fourier Transform as a mathematical tool for signal analysis. Although it can analyze signals with steady and transitory properties, it has limits. The Wavelet Packet Decomposition (WPD) is a novel technique that we suggest in this study as a way to improve the Fourier Transform and get beyond these drawbacks. In this experiment, we specifically considered the utilization of Daubechies level 4 for the wavelet transformation. The choice of Daubechies level 4 was motivated by several reasons. Daubechies wavelets are known for their compact support, orthogonality, and good time-frequency localization. By choosing Daubechies level 4, we aimed to strike a balance between preserving important transient information and avoiding excessive noise or oversmoothing in the transformed signal. Then we compared the outcomes of our suggested approach to the conventional Fourier Transform using a non-stationary signal. The findings demonstrated that the suggested method offered a more accurate representation of non-stationary and transient signals in the frequency domain. Our method precisely showed a 12% reduction in MSE and a 3% rise in PSNR for the standard Fourier transform, as well as a 35% decrease in MSE and an 8% increase in PSNR for voice signals when compared to the traditional wavelet packet decomposition method.

Adaptive Short-Time Fractional Fourier Transform Based on Minimum Information Entropy

Traditional short-time fractional Fourier transform(STFrFT)has a single and fixed window function,which can not be adjusted adaptively according to the characteristics of fre-quency and frequency change rate.In order to overcome the shortcomings,the STFrFT method with adaptive window function is proposed.In this method,the window function of STFrFT is ad-aptively adjusted by establishing a library containing multiple window functions and taking the minimum information entropy as the criterion,so as to obtain a time-frequency distribution that better matches the desired signal.This method takes into account the time-frequency resolution characteristics of STFrFT and the excellent characteristics of adaptive adjustment to window func-tion,improves the time-frequency aggregation on the basis of eliminating cross term interference,and provides a new tool for improving the time-frequency analysis ability of complex modulated sig-nals.

REAL PALEY-WIENER THEOREMS FOR THE SPACE-TIME FOURIER TRANSFORM

This paper presents an extension of certain forms of the real Paley-Wiener theorems to the Minkowski space-time algebra. Our emphasis is dedicated to determining the space-time valued functions whose space-time Fourier transforms(SFT) have compact support using the partial derivatives operator and the Dirac operator of higher order.

Quantum entangled fractional Fourier transform based on the IWOP technique

In our previous papers,the classical fractional Fourier transform theory was incorporated into the quantum theoretical system using the theoretical method of quantum optics,and the calculation produced quantum mechanical operators corresponding to the generation of fractional Fourier transform.The core function of the coordinate-momentum exchange operators in the addition law of fractional Fourier transform was analyzed too.In this paper,the bivariate operator Hermite polynomial theory and the technique of integration within an ordered product of operators(IWOP)are used to establish the entanglement fractional Fourier transform theory to the extent of quantum.A new function generating formula and an operator for generating quantum entangled fractional Fourier transform are obtained using the fractional Fourier transform relationship in a pair of conjugated entangled state representations.

Dual Image Cryptosystem Using Henon Map and Discrete Fourier Transform

This paper introduces an efficient image cryptography system.The pro-posed image cryptography system is based on employing the two-dimensional(2D)chaotic henon map(CHM)in the Discrete Fourier Transform(DFT).The proposed DFT-based CHM image cryptography has two procedures which are the encryption and decryption procedures.In the proposed DFT-based CHM image cryptography,the confusion is employed using the CHM while the diffu-sion is realized using the DFT.So,the proposed DFT-based CHM image crypto-graphy achieves both confusion and diffusion characteristics.The encryption procedure starts by applying the DFT on the image then the DFT transformed image is scrambled using the CHM and the inverse DFT is applied to get the final-ly encrypted image.The decryption procedure follows the inverse procedure of encryption.The proposed DFT-based CHM image cryptography system is exam-ined using a set of security tests like statistical tests,entropy tests,differential tests,and sensitivity tests.The obtained results confirm and ensure the superiority of the proposed DFT-based CHM image cryptography system.These outcomes encourage the employment of the proposed DFT-based CHM image cryptography system in real-time image and video applications.

GP Algorithm-Based Fourier Transform Infrared Spectrum Trend Term Removal Model

Trend term removal is a key step in Fourier transform infrared spectroscopy(FTIR)data pre-processing.The most commonly used least squares(LS)method,although satisfying the real-time requirement,has many problems such as highly correlated initial values of the expression parameters,the need to pre-estimate the trend term shape,and poor fitting accuracy at low signal-to-noise ratios.In order to achieve real-time and robust trend term removal,a new trend term removal method using genetic programming(GP)in symbolic regression is constructed in this paper,and the FTIR simulation interference results and experimental measurement data for common volatile organic compounds(VOCs)gases are analyzed.The results show that the genetic programming algorithm can both reduce the initial value requirement and greatly improve the trend term accuracy by 20%-30% in three evaluation indicators,which is suitable for gas FTIR detection in complex scenarios.

分数阶Fourier变换的测不准原理

该文参考Fourier变换的性质研究了离散分数阶Fourier变换的测不准原理以及连续分数阶Fourier变换在Lebesgue测度下的测不准原理,使得分数阶Fourier变换的测不准原理性质更一般化.

河渠附近潜水一维非稳定流模型Fourier快解求解

对垂向交换量影响下的河渠附近潜水一维非稳定流模型,采用Boussinesq方程的第一线性化方法,在利用Fourier变换求解时,随河渠水位f(t)的变化,需要复杂繁琐的积分变换过程。在f(t)不直接参与变换的前提下,将f(t)用运算符代替并运行于变换过程中,依据Fourier变换的微分性质和卷积定理等性质,建立这类模型普遍适用的含f(t)的理论通用解;依据理论通用解,给出f(t)为几个常用函数时的模型解析解;利用理论通用解的求解方法,可避免大部分繁杂的积分变换运算过程。针对不同的河渠水位变动与垂向补给组合条件,建立利用潜水位动态监测数据计算模型参数的拐点法和配线法;实例演示,模型参数的求算过程。

量化噪声对插值fast Fourier transform谐波估计的影响

插值fast Fourier transform(FFT)算法是对谐波频率、幅度和相位三参数进行精确估计的常见方法.信号中的噪声无疑会影响插值FFT算法的谐波估计结果.有文献讨论过信号中的白噪声对插值FFT算法谐波估计结果的影响,尚没有文献讨论过A/D(analog-to-digital)量化噪声对插值FFT算法谐波估计结果的影响.本文首先对插值FFT谐波估计算法做了简单介绍,接着对A/D量化噪声的特点进行了分析研究.在此基础上,仔细研究了量化噪声对插值FFT谐波估计方法的影响程度——给出了计算模型、进行了大量仿真计算、得出了量化噪声对谐波频率、幅度和相位3参数估计结果影响程度的结论.相信所得结论对电路设计中的A/D器件、信号调理器件等的选型有指导意义.

基于插值fast Fourier transform谐波分析的桥梁状态评估方法

桥梁的环境振动信号包含了各种环境噪声成分,如何从噪声影响中提取桥梁的真实自振频率,并进行桥梁状态评估是亟待解决的问题.本文对环境激励下的大跨桥梁响应信号进行了研究,根据环境激励在低频段近于平白噪声以及线性系统在环境激励下,各输出点测量信号之间相关函数与结构的脉冲响应函数具有相同的数学表达式——均是主频正弦函数的叠加这一特点,建立了插值fast Fourier transform(FFT)频谱校正技术分析各测点相关函数的方法.该方法能在白噪声背景下精确提取桥梁振动频率、幅度和相位等模态参数,简单实用,不仅能在线识别参数,而且对桥梁单点激励和多点同时激励均适用.将校正后的主频与三维有限元计算结果相对比,能对桥梁结构整体状态和工作性能进行评估.最后,一座实桥动力试验和计算结果证明了该方法的能有效用于运营条件下桥梁状态的评估.

Anti-aliasing nonstationary signals detecion algorithm based on interpolation in the frequency domain using the short time Fourier transform

To eliminate the aliasing that appeared during the measurement of multi-components nonstationary signals, a novel kind of anti-aliasing algorithm based on the short time Fourier transform (STFT) is brought forward. First the physical essence of aliasing that occurs is analyzed; second the interpolation algorithm model is setup based on the Hamming window; then the fast implementation of the algorithm using the Newton iteration method is given. Using the numerical simulation the feasibility of algorithm is validated. Finally, the electrical circuit experiment shows the practicality of the algorithm in the electrical engineering.

NYFR output pulse radar signal TOA analysis using extended Fourier transform and its TOA estimation

Nyquist folding receiver(NYFR) is a typical wideband analog-to-information architecture. Focusing on the noncooperative receiving, the pulse radar signal intercepted by the NYFR in time domain is analyzed. The NYFR outputs under different input conditions are investigated based on the extended Fourier transform(EFT) and the sampling theorem. Combining with the characteristic of the NYFR output in time domain, a new time of arrival(TOA) estimation method based on the energy envelope and the wavelet transform is proposed. The proposed estimation method can be adapted for the non-cooperative situation. It has no requirement for prior information to determine the threshold and is not necessary to transform the signal into baseband. Simulation results prove the correctness of the NYFR output expressions and show the efficacy of the proposed estimation method.

Digital watermarking for still image based on discrete fractional fourier transform

Presents a digital watermarking technique based on discrete fractional Fourier transform (DFRFT), discusses the transformation of the original image by DFRFT, and the modification of DFRFT coefficients of the original image by the information of watermark, and concludes from experimental results that the proposed technique is robust to lossy compression attack.

Realization of -bit semiclassical quantum Fourier transform on IBM's quantum cloud computer

To overcome the difficulty of realizing large-scale quantum Fourier transform(QFT) within existing technology, this paper implements a resource-saving method(named t-bit semiclassical QFT over Z_(2~n)), which could realize large-scale QFT using an arbitrary-scale quantum register. By developing a feasible method to realize the control quantum gate Rk, we experimentally realize the 2-bit semiclassical QFT over Z_(2~3) on IBM's quantum cloud computer, which shows the feasibility of the method. Then, we compare the actual performance of 2-bit semiclassical QFT with standard QFT in the experiments.The squared statistical overlap experimental data shows that the fidelity of 2-bit semiclassical QFT is higher than that of standard QFT, which is mainly due to fewer two-qubit gates in the semiclassical QFT. Furthermore, based on the proposed method, N = 15 is successfully factorized by implementing Shor's algorithm.

Fourier Transform Infrared Spectroscopic Studies in Flotation

Fourier transform infrared (FTIR) spectroscopy has been extensively employed In flotation research. The work done by the author and co-workers has been reported. A comparison has been made among the different FTIR specfroscopic techniques, e.g. , transmission FTIR spectroscopy, diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, and attenuated total reflectance (ATR) FTIR spectroscopy. FTIR spectroscopy has been used to study the mechanism of interaction between the collector and the surfaces of different minerals, the mechanism of action of the depressant in improving the selectivity of flotation, and the mechanism of adsorption of the polymeric modifying reagent on mineral surfaces. The interaction between particles in mineral suspension has also been studied by FTIR spectroscopy.

Vibration parameter measurement using the temporal digital hologram sequence and windowed Fourier transform

Real time digital recording and numerical reconstruction of a temporal digital hologram sequence have become feasible in recent years.They provide a new measurement method which enjoys the valuable advantages of being full-field,noncontact and high precision.In this paper,a combined method of temporal digital hologram sequence and windowed Fourier transform is proposed to measure the kinematic parameters of random vibration.A series of holograms are recorded by CCD camera and the original phase can be reconstructed by Fresnel reconstruction algorithm.The three-dimensional windowed Fourier transform is used to filter noise in phase and extract the instantaneous kinematic parameters of the specimen,such as the displacement,velocity and acceleration.An experiment is conducted on a chloroprene rubber latex membrane.Results demonstrate that the proposed method determines the vibration parameters precisely and enjoys many merits.