Fast Fourier Transform Amplitude Research Articles

Influence of high temperature during tensile test for stainless steel using acoustic emission

AbstractThis paper explores the application of acoustic emission (AE) monitoring in mechanical and materials studies, specifically focusing on the detection of plastic deformation, fracture, and crack growth. However, the influence of high temperature on AE signals and its effects on mechanical properties are not fully understood due to the limitations of sensors in high‐temperature environments. To address this gap, the study investigates the impact of high temperatures on the mechanical properties of stainless steel SS304 using AE techniques during tensile testing. Two temperature conditions were tested: 600 and 700°C. AE sensors connected to specimens captured AE activity using a designated waveguide, and a Continuous Creep Monitoring Instrument (CCMi) recorded the responses. Sensitivity testing using the pencil‐lead break (PLB) technique ensured the sensor's capability. The proposed frequency‐based analysis method, fast Fourier transform amplitude (FFTA) analysis, was employed to effectively analyze the AE signals. The results revealed significant AE features and frequency distributions associated with elastic deformation, plastic deformation, and fracture regions. These findings establish a foundation for utilizing AE techniques in crack detection and monitoring under high‐temperature loading, contributing to equipment reliability and structural integrity.

Development and Analysis of Multifeature Approaches in SPR Sensor Development

This paper presents the development and signal analysis of surface plasmon resonance (SPR)-based sensors in D-shaped polymer optical fibers (POFs). A gold-palladium (Au-Pd) coating was applied to the D-shaped region to obtain the SPR signal in the transmitted spectrum of the POFs, where different samples were fabricated using the same methods and parameters. In this case, the transmitted spectra of three sets of samples were compared, which indicated variations in the SPR signature that can influence the sensors’ application and reproducibility. Then, the intensity of and wavelength shift in the SPR signals were analyzed as a function of the refractive index variation, where it was possible to observe differences in the sensors’ sensitivities and the linearity of the different samples. In this regard, additional features, namely the area below the curve and the peak amplitude of the fast Fourier transform (FFT) applied to the transmitted spectra, were used to enhance the sensors’ accuracy and precision. To verify the use of such additional features in the sensor analysis, an unsupervised approach based on k-means clustering was used considering a single dataset with the results of all the sensors. The results showed clustering with the number of different refractive indices tested, which motivated the use of these features (intensity, wavelength, area and FFT amplitude) in the refractive index assessment. In this context, random forest was the supervised algorithm with the smallest root mean squared error (RMSE) among the algorithms tested, where an RMSE of 0.0057 was obtained considering all the datasets. For the analysis of each sensor (considering the three sets of sensor samples), the mean RMSE using random forest applied to the multifeature approach returned relative errors below 9%, considering the entire tested range of refractive index variation.

Numerical Gas–Liquid Two-Phase Flow Regime Identification in a Horizontal Pipe Using Dynamic Pressure Data

Gas–liquid two-phase flow is very common in industrial pipelines. Flow regime identification is the first step to design, analyze, and operate the gas–liquid system successfully. The purpose of this study is to develop a methodology for identification of a two-phase flow regime using post signal processing techniques, namely Fast Fourier Transform (FFT) and Probabilistic Density Function (PDF). Three different flow regimes were simulated in a 6 m horizontal pipe with a 0.050 m inner diameter. A Level-Set (LS) method coupled with Volume of Fluid (VOF) method is used to model the air–water interface. After validation of the numerical method, dynamic pressure readings were collected with the intent to identify the associated flow regimes by post-processing of these signals. It was concluded that dynamic pressure signals of different flow regimes show different characteristics (like dominant frequency, FFT amplitude, PDF location and PDF magnitude) in the time and frequency domains. These characteristics can be potentially used as differentiating factors to distinguish different flow regimes. This research is limited to stratified, slug, and annular flow in the horizontal pipe. This paper uses a new approach to identify the flow regime in a horizontal pipe by Fast Fourier Transform and Probability Density Function of dynamic pressure readings obtained by using numerical simulation.

Study of RSSI Accuracy for Outdoor Sensor Localization

The movement characteristics that nodes exhibit will bring serious problems in a Wireless Sensor Network (WSN) because most of the protocols do not adapt to node movement. This requires a mobility-aware evaluation technique to locate nodes, forecast communication quality, trace the movement trajectory and formulate thresholds to initiate future handover. One off-the-shelf technology using the Received Signal Strength Indicator (RSSI) of wireless radio is extensively adopted because of the small expense. A node can compute the relative distance between it and its partner by using RSSI readings. However, the measurement results of RSSI fluctuate significantly due to the effects of shadowing and fading. This paper is oriented to investigate the localization accuracy of RSSI in an outdoor ambient condition. To do this, some dynamic and stationary tests are carried out, a calibration line displaying the one-to-one match between RSSI and distance is mapped, five filtering approaches are developed to alleviate the fluctuation of movement curves, and the alleviation effect is measured by calculating the Root Mean Square Error (RMSE) values and by converting the time-domain filtering results into the Fast Fourier Transform (FFT) spectrum. Although the optimized RMSE is reduced to 0.86 and the noise FFT amplitude is less than 1[Formula: see text]dBm, it may still have a single RSSI value corresponding to more than one distance. Moreover, these distances can differ as much as 2.8[Formula: see text]m. Since the evaluation error is not acceptable for most cases, it is inaccurate for a mobile node to measure the distance from its partner rely on RSSI readings under outdoor scenarios.

Low-cost MEMS accelerometer network for rotating machine vibration diagnostics

In this paper a wireless sensor network (WSN) with low-cost nodes (less than $US 30.00) is presented. The nodes are based on microelectromechanical systems (MEMS) accelerometers and a highly-integrated microcontroller with built-in antenna for Wi-Fi and Bluetooth Low-Energy (BLE). The system was specially designed to analyze unbalance, load, and rotor obstructions in a rotating machine. Two sensor nodes were installed on an apparatus: one in the shaft and the other on the support table. The acceleration signals were used to analyze the machine vibration in the frequency domain by its Fast Fourier Transform (FFT). This spectrum was pre-analyzed on the sensor node, and the most significant features were sent to a cloud platform. These nodes were mounted to acquire vibration from an electric bicycle motor. Three situations were simulated: unbalance, using weights at the endings of rim spokes; mechanical load, using neodymium magnets, through the principle of eddy currents; and rotor obstruction, using an object made with a nylon cable. The solution presented was used to measure vibration and calculate its spectrum and send it to the cloud. The signals were analyzed using three strategies: an FFT amplitude level comparison, logistic regression, and neural network (NN). The analyses were carried out using the signals of each sensor independently and as a sensor network, this last showing better results. It was possible to diagnose each type of fault inserted in the tests, proving that the device developed can be used in industries as a low-cost alternative to monitor the health of rotating machines.

A laser arrays scan thermography (LAsST) for the rapid inspection of CFRP composite with subsurface defects

Laser arrays scan thermography (LAsST) as a nondestructive testing and evaluation (NDT&E) tool is presented to detect the subsurface delamination or abnormally of carbon fiber reinforced polymer (CFRP) composite. A series of woven CFRP specimens with artificial bottom flat holes (BFHs) are prepared for NDT&E by LAsST. The thermal response signal was reconstructed and in turn appeared the analogous thermal responsible behavior similar to pulsed thermography (PT). The thermal response signal was processed by using of Fast Fourier transform (FFT) and principal component analysis (PCA), and FFT amplitude and phase images, PCA image were formed. The signal noise ratios (SNRs) of defects were calculated and employed to evaluate the defect detectability of different post processing algorithms. A likeness optical flow (LOF) approach was proposed to reduce the interruption of lateral thermal diffusion and improve the defect detectability. The experimental results indicate that FFT phase based image shows a relatively high SNR, and the LOF method effectively enlarged the SNRs of defects.

Optical characterization of porous silicon monolayers decorated with hydrogel microspheres

The optical response of porous silicon (pSi) films, covered with a quasi-hexagonal array of hydrogel microspheres, to immersion in ethanol/water mixtures was investigated. For this study, pSi monolayers were fabricated by electrochemical etching, stabilized by thermal oxidation, and decorated with hydrogel microspheres using spin coating. Reflectance spectra of pSi samples with and without deposited hydrogel microspheres were taken at normal incidence. The employed hydrogel microspheres, composed of poly-N-isopropylacrylamide (polyNIPAM), are stimuli-responsive and change their size as well as their refractive index upon exposure to alcohol/water mixtures. Hence, distinct differences in the interference pattern of bare pSi films and pSi layers covered with polyNIPAM spheres could be observed upon their immersion in the respective solutions using reflective interferometric Fourier transform spectroscopy (RIFTS). Here, the amount of reflected light (fast Fourier transform (FFT) amplitude), which corresponds to the refractive index contrast and light scattering at the pSi film interfaces, showed distinct differences for the two fabricated samples. Whereas the FFT amplitude of the bare porous silicon film followed the changes in the refractive index of the surrounding medium, the FFT amplitude of the pSi/polyNIPAM structure depended on the swelling/shrinking of the attached hydrogel spheres and exhibited a minimum in ethanol-water mixtures with 20 wt% ethanol. At this value, the polyNIPAM microgel is collapsed to its minimum size. In contrast, the effective optical thickness, which reflects the effective refractive index of the porous layer, was not influenced by the attached hydrogel spheres.PACS81.05.Rm; 81.16.Dn; 83.80Kn; 42.79.Pw

Analysis of plethysmographic waveform changes induced by beach chair positioning under general anesthesia

During shoulder surgery, patients typically are placed in the beach chair position. In rare cases, this positioning has resulted in devastating outcomes of postoperative cerebral ischemia (Cullen and Kirby in APSF Newsl 22(2):25-27, 2007; Munis in APSF Newsl 22(4):82-83, 2008). This study presents a method to noninvasively and continuously hemodynamically monitor patients during beach chair positioning by using the photoplethysmograph signal recorded from a commercial pulse oximeter. Twenty-nine adults undergoing shoulder surgery were monitored before and after beach chair positioning with electrocardiogram, intermittent blood pressure, end tidal carbon dioxide, and photoplethysmograph via Nellcor finger pulse oximeter. Fast Fourier transform (FFT) was used to perform frequency-domain analysis on the photoplethysmograph (PPG) signal for data segments taken 80-120s before and after beach chair positioning. The amplitude density of respiration-associated PPG oscillations was quantified measuring the height of the FFT peak at respiratory frequency. Results were reported as (median, interquartile range) and statistical analysis was performed using Wilcoxon sign rank test. Data were also collected when vasoactive drugs phenylephrine and ephedrine were used to maintain acceptable mean arterial pressure during a case. With beach chair positioning, all subjects who did not receive vasoactive drugs showed an increase in the FFT amplitude density of respiration-associated PPG oscillations (p<0.0001) without change in pulse-associated PPG oscillations. The PPG was more accurate at monitoring the change to beach chair position than blood pressure or heart rate. With vasoactive drugs, pulse-associated PPG oscillations decreased only with phenylephrine while respiration-associated oscillations did not change. Frequency domain analysis of the PPG signal may be a better tool than traditional noninvasive hemodynamic parameters at monitoring patients during beach chair position surgery.

Automatic Calibration of Control Parameters based on Merit Function Spectral Analysis

The number of actuations influencing the combustion is increasing, and, as a consequence, the calibration of control parameters is becoming challenging. One of the most effective factors influencing performance and efficiency is the combustion phasing: for gasoline engines control variables such as Spark Advance (SA), Air-to-Fuel Ratio (AFR), Variable Valve Timing (VVT), Exhaust Gas Recirculation (EGR) are mostly used to set the combustion phasing.The optimal control setting can be chosen according to a cost function, taking into account performance indicators, such as Indicated Mean Effective Pressure (IMEP), Brake Specific Fuel Consumption (BSFC), pollutant emissions, or other indexes inherent to reliability issues, such as exhaust gas temperature, or knock intensity.The paper proposes the use of the extremum seeking approach during the calibration process. The main idea consists in changing the values of each control parameter at the same time, identifying its effect on the monitored cost function, allowing to shift automatically the control setting towards the optimum solution throughout the calibration procedure. Obviously, the nodal point is to establish how the various control parameters affect the monitored cost function and to determine the direction of the required variation, in order to approach the optimum. This task is carried out by means of a spectral analysis of the cost function: each control variable is varied according to a sine wave, thus its effect on the cost function can be determined by evaluating the amplitude of the Fast Fourier Transform (FFT) of the cost function, for the given excitation frequency. The FFT amplitude is representative of the cost function sensitivity to the control variable variations, while the phase can be used to assess the direction of the variation that must be applied to the control settings in order to approach the optimum configuration. Each control parameter is excited with a different frequency, thus it is possible to recognize the effect of a single parameter by analyzing the spectrum of the cost function for the given excitation frequency.The methodology has been applied to data referring to a PFI engine, trying to maximize IMEP, while limiting the knock intensity and exhaust gas temperature, using SA and AFR as control variables. The approach proved to be efficient in reaching the optimum control setting, showing that the optimal setting can be achieved rapidly and consistently.

An intelligent procedure for watermelon ripeness detection based on vibration signals.

In this paper, an efficient procedure for ripeness detection of watermelon was presented. A nondestructive method was used based on vibration response to determine the internal quality of watermelon. The responses of samples to vibration excitation were optically recorded by a Laser Doppler (LD) vibrometer. Vibration data was collected from watermelons of two qualities, namely, ripe and unripe. Vibration signals were transformed from time-domain to frequency-domain by fast Fourier transform (FFT). Twenty nine features were extracted from the FFT amplitude and phase angle of the vibration signals. K-nearest neighbor (KNN) analysis was applied as a classifier in decision-making stage. The experimental results showed that the usage of the FFT amplitude of the vibration signals gave the maximum classification accuracy. This method allowed identification at a 95.0% level of efficiency. Hence, the proposed method can reliably detect watermelon ripeness.

Speech Processing System Using a Noise Reduction Neural Network Based on FFT Spectrums

This paper proposes a speech processing system based on a model of the human auditory system and a noise reduction neural network with fast Fourier transform (FFT) amplitude and phase spectrums for noise reduction under background noise environments. The proposed system reduces noise signals by using the proposed neural network based on FFT amplitude spectrums and phase spectrums, then implements auditory processing frame by frame after detecting voiced and transitional sections for each frame. The results of the proposed system are compared with the results of a conventional spectral subtraction method and minimum mean-square error log-spectral amplitude estimator at different noise levels. The effectiveness of the proposed system is experimentally confirmed based on measuring the signal-to-noise ratio (SNR). In this experiment, the maximal improvement in the output SNR values with the proposed method is approximately 11.5 dB better for car noise, and 11.0 dB better for street noise, when compared with a conventional spectral subtraction method.