Fast Fourier Transform Technique Research Articles

A Comparison of Hilbert and Fast Fourier Transform Techniques for Reciprocating Motors

Human existence has been significantly impacted by the growth of information and technology, particularly in relation to the requirement for effective mobility. But as a car ages, symptoms that affect its performance and safety start to show. These symptoms are a serious worry. Interference with car parts, such coils, is one issue that can have a major effect on driving safety and performance. Vibration analysis techniques have been successful in addressing this problem by detecting damage to different car parts. With the intention of comprehending piston motor vibration characteristics at different rotational speeds. The piston motor was used to collect vibration data at three distinct speeds: 900 rpm, 1500 rpm, and 3500 rpm. Both the Hilbert approach, which offers profound insight into frequency energy, and an FFT analyzer, which produces clear frequency spectrum information, were used in the analysis. The findings demonstrated that vibration characteristics changed in response to variations in reciprocating motor speed. On the other hand, the average frequency energy tends to remain constant across a range of speeds, suggesting that the features of reciprocating motors may be complicated and not very affected by variations in speed. According to this study, different perspectives on the vibration characteristics of piston motors operating at different speeds can be gained by utilizing vibration analysis techniques like FFT and Hilbert. Improved comprehension of this phenomenon could have a significant impact on maintenance initiatives and performance analysis of vehicles. To fully understand the complexity of vibration characteristics in reciprocating motors at various speed conditions, more research will be needed

On the use of machine learning and data-transformation methods to predict hydration kinetics and strength of alkali-activated mine tailings-based binders

The escalating production of mine tailings (MT), a byproduct of the mining industry, constitutes significant environmental and health hazards, thereby requiring a cost-effective and sustainable solution for its disposal or reuse. This study proposes the use of MT as the primary ingredient (≥70%mass) in binders for construction applications, thereby ensuring their efficient upcycling as well as drastic reduction of environmental impacts associated with the use of ordinary Portland cement (OPC). The early-age hydration kinetics and compressive strength of MT-based binders are evaluated with an emphasis on elucidating the influence of alkali activation parameters and the amount of slag or cement that are used as minor constituents. This study reveals correlations between cumulative heat release and compressive strengths at different ages; these correlations can be leveraged to estimate the compressive strength based on hydration kinetics. Furthermore, this study presents a random forest (RF) model—in conjunction with fast Fourier and direct cosine transformation techniques to overcome the limitations associated with limited volume and diversity of the database—to enable high-fidelity predictions of time-dependent hydration kinetics and compressive strength of MT-based binders in relation to mixture design. Overall, this study demonstrates a sustainable approach to upcycle mine tailings as the primary component in low-carbon construction binders; and presents both analytical and machine learning-based approaches for accurate a priori predictions of hydration kinetics and compressive strength of these binders.

An empirical model for the tropical Indian region using in-situ and space-borne observations taking into account of tropical oscillations

An empirical model of temperature is developed using long-term observations of radiosonde (52 years), M-100 Rocket (17 years), and Sounding of the Atmosphere by Broadband Emission Radiometry (SABER) (22 years) data. The dominant oscillations such as Annual Oscillation (AO), Semi-Annual Oscillation (SAO), Terr-Annual oscillation (TAO), and Quasi-Biennial Oscillation (QBO) variability has been incorporated into the temperature to obtain the empirical atmosphere. The monthly mean of temperature obtained from radiosonde from 0 to 25 km, M-100 rocket from 26 to 80 km and SABER from 26 to 109 km has been subjected to the Fast Fourier Transform technique to retrieve the amplitudes and phases of AO, SAO, TAO, and QBO. The amplitudes and phases obtained are put in an empirical formula along with the temperature obtained from the observations to retrieve the empirical temperature. The empirical model is developed for two epochs (1971–1990, and 2002–2023). The amplitude is similar in both epochs for the TAO, and it is high in the first compared to the second epoch for SAO, AO, and QBO. The peak in the amplitude (3–4 K) is obtained between 80 and 90 km for TAO, and between 70 and 80 km (2–3 K) for SAO. The amplitude is less than 1 K for AO in both the epochs. The peaks in the QBO are observed between 20 and 30 km (2–3 K), another between 60 and 70 km (3–4 K) in the first epoch and above 80 km (2–3 K). Downward phase propagation is dominant for all the oscillations observed below 50 km, and upward propagation above. The empirical temperature obtained compares well with the observations below 20 km (RMSE <1 K) and shows differences above it (RMSE ∼ 4–6 K). This is due to the fact, that the amplitude of the oscillation is more in the stratosphere and mesosphere which results in perturbation of the background temperature.

Analysis of material incision in milling machine against workpiece by vibration method

The Frais Machine is a piece of equipment used in manufacturing. To reach the specified production capacity, the machining process' time requirements must be more effectively managed. The goal of this study is to use the Fast Fourier Transform (FFT) technique to ascertain how changes in workpiece and spindle speed affect vibration. The milling machine utilized in this investigation is the Lushan Model ZX32, and the measuring device is a vibration analyzer coupled to an accelerometer sensor. Sengon Wood, PVC, and Aluminum 5052 are the workpiece varieties used. At spindle speeds of 95 and 150 rpm, the study's findings on aluminum material produced Y axis personal frequencies of 118 Hz with an amplitude of 15.12 mm/s2 and 144 Hz with an amplitude of 9.657 mm/s2. While PVC produced personal frequencies on the Y axis of 71 Hz with amplitudes of 12.11 mm/s2 and 40 Hz with amplitudes of 7.025 mm/s2 at spindle speeds of 95 and 150 rpm. Personal frequencies at 118 Hz with an amplitude of 10.9 mm/s2 and 74 Hz with an amplitude of 7.97 mm/s2 in the X axis are seen in wood materials with spindle speeds of 95 and 150 rpm. The findings demonstrate that differences in spindle rotation and workpiece have an impact on vibration; smaller vibrations are produced at higher rotation rates. Additionally, differences in the workpiece can impact tool life

An efficient method for simulating fluctuating wind speed fields in two-spatial dimensions based on a frequency-dependent acceptance-rejection scheme

The simulation of fluctuating wind speed fields holds paramount importance in the wind-resistant design of significant structures such as high-rise buildings, long-span bridges, and wind turbines. To address the computational challenges posed by the original spectral representation method (SRM), a novel approach has emerged. This approach, rooted in the frequency-wavenumber spectrum (FWS), offers a solution to the complexities associated with the original SRM. An enhanced non-uniform discrete technique based on the acceptance-rejection (A-R) criterion was introduced to improve the efficiency of the FWS-based SRM. Nonetheless, this technique currently faces certain constrains, including its demand for a substantial allocation of random access memory (RAM) and its inability to be seamlessly integrated with the fast Fourier transform (FFT) algorithm. Given this scenario, a notably more effective strategy for selecting representative points emerges -the frequency-dependent acceptance-rejection (A-R) scheme. This innovative scheme holds the advantage of diminishing RAM utilization, transforming wind field modeling from a supercomputers-exclusive task to one feasible on personal computers. Moreover, its seamless integration with FFT technology bolsters simulation efficiency. The provided numerical simulation instances of two-spatial dimensional fluctuating wind speed fields underscore the proficiency of this proposed frequency-dependent A-R scheme. The outcomes demonstrated the combined A-R and FFT technique’s efficiency and precision, in simulating two-spatial dimensions fluctuating wind speed fields.

Design and Development of Experimental Test Rig for Fault Diagnosis of Ball Bearing Using Fuzzy Logic Concept

Rolling element bearings are frequently employed in industry. Many machine-related issues are linked to bearing failures. To minimize downtime and preserve product quality in a highly automated production, an online detection system is required. Condition-based monitoring for deep groove ball bearings is becoming more common. All rotating machinery uses these bearings extensively to accommodate both static and dynamic loads. Techniques for condition-based monitoring can be utilized to diagnose bearing defects to prevent this failure. So, it is important to study these faults present in the machines. The techniques for fault detection will be described in this paper. We are discussing Fast Fourier Transform (FFT) technique. In FFT, we obtain frequency-relationship graphs, and based on peak frequencies, we predict faults. A detailed analysis using the FFT Methodology is done to find out the possible faults, and finally validate with MATLAB software. For the aim of bearing diagnostics, the system performs vibration analysis. More advanced diagnostic systems use fuzzy logic and classification methods to identify the state of the machinery. These techniques enable the creation of more automatic and trustworthy diagnostic systems.

Predictive maintenance of planetary gearboxes using FFT and machine learning technique

Planetary gearboxes are widely used in manufacturing processes, and non-destructive assessment is becoming increasingly important for monitoring their state. We outlined a fine-tuned random decision tree (FT-RDT) in this study for classifying and fault-finding the gearbox via signals generated by vibrations. This approach concentrates on the identification of worn gears, consequently distinct classes—healthy gears, ringed gears containing damaged tooth faces, and planetary gears featuring damaged tooth faces—were established. Each of the categories consists of 150 specimens, divided into two separate sets of 50 specimens for the testing data and 100 specimens for the data used for training. The Fast Fourier Transform (FFT) was used to convert the temporal signals to frequencies. The next step was to gather 24 statistical characteristics from frequency data. The retrieved characteristic was fed into the fault classification procedure (FT-RDT). Combining these methods yields rates of classification accuracy across train and test data of 92.75% and 91.50%, demonstrating the excellent reliability and capability of the problem identification solution that is created.

Experimental Comparative Investigations to Evaluate Cavitation Conditions within a Centrifugal Pump Based on Vibration and Acoustic Analyses Techniques

Cavitation is an essential problem that occurs in all kinds of pumps. This cavitation contributes highly towards the deterioration in the performance of the pump. In industrial applications, it is very vital to detect and decrease the effect of the cavitation in pumps. Using different techniques to analysis and diagnose cavitation leads to increase in the reliability of cavitation detection. The use of various techniques such as vibration and acoustic analyses can provide a more robust detection of cavitation within the pump. In this work therefore, focus is put on detecting and diagnosing the cavitation phenomenon within a centrifugal pump using vibration and acoustic techniques. The results obtained from vibration and acoustic signals in time and frequency domains were analysed in order to achieve better understanding regarding detection of cavitation within a pump. The effect of different operating conditions related to the cavitation was investigated in this work using different statistical features in time domain analysis (TDA). Moreover, Fast Fourier Transform (FFT) technique for frequency domain analysis (FDA) was also applied. Furthermore, the comparison and evaluation system among different techniques to find an adequate technique incorporating for accuracy and to increase the reliability of detection and diagnosing different levels of cavitation within a centrifugal pump were also investigated.

Experimental Analysis of Streamer Currents Under Lightning Impulse Voltage Using Fast Fourier and Discrete Wavelet Transform Methods

This paper presents an experimental comparative study of streamer currents propagating in mineral oil, natural esters (olive oil and rapeseed oil) and synthetic ester (tetra-ester) under both positive and negative lightning impulse voltages. Because of the similarity in the shapes of streamer currents in investigated liquids, it is difficult to differentiate between the current signals corresponding to each liquid. For that purpose, two techniques are used for the analysis of these signals. The first one is the Fast Fourier Transform (FFT) technique, which is based on the frequency spectrum analysis. The second one is the Discrete Wavelet Transform (DWT) method, which has demonstrated its ability for the analysis of the non-stationary signals. The obtained experimental results show spectral signature specific to each liquid, thus making it possible to differentiate the various streamer currents. Overall, the two techniques show good performance. However, the accuracy and effectiveness of the DWT method is better than those of the FFT technique.

Training FFT to Select Beams in Massive MIMO

In this letter, we propose new trainable Fast Fourier Transform (FFT) structures to increase the accuracy of beamspace transformation in the multi-user (MU) mode of Massive Multiple Input Multiple Output (MIMO) receiver. The FFT is a widely employed beamspace transformation technique, thanks to its low transforming complexity. Unfortunately, there is a significant performance loss of MU signal detection in the FFT beamspace, as default FFT beams are not co-directed with angles of arrivals. We address this issue with trainable FFT that outperforms not only fixed FFT approaches, but also other trainable FFT techniques.

Flows for the masses: A multi-fluid non-linear perturbation theory for massive neutrinos

Velocity dispersion of the massive neutrinos presents a daunting challenge for non-linear cosmological perturbation theory. We consider the neutrino population as a collection of non-linear fluids, each with uniform initial momentum, through an extension of the Time Renormalization Group perturbation theory. Employing recently-developed Fast Fourier Transform techniques, we accelerate our non-linear perturbation theory by more than two orders of magnitude, making it quick enough for practical use. After verifying that the neutrino mode-coupling integrals and power spectra converge, we show that our perturbation theory agrees with N-body neutrino simulations to within 10% for neutrino fractions Ω ν,0 h 2 ≤ 0.005 up to wave numbers of k = 1 h/Mpc, an accuracy consistent with ≤ 2.5% errors in the neutrino mass determination. Non-linear growth represents a > 10% correction to the neutrino power spectrum even for density fractions as low as Ω ν,0 h 2 = 0.001, demonstrating the limits of linear theory for accurate neutrino power spectrum predictions. Our code FlowsForTheMasses is avaliable online at github.com/upadhye/FlowsForTheMasses.

"THERMOELASTIC DISPLACEMENT AND TEMPERATURE RISE IN A HALF-SPACE DUE TO A STEADY-STATE HEAT FLUX "

Due to model complexity, classical contact mechanics theory assumes isothermal contact processes, involving bodies with uniform temperatures and no heat transmitted or generated through or near the contact interface. This paper addresses the problem of frictional heating in non-conforming or rough contacts by investigating the thermoelastic behaviour of asperities. The heat generated in a sliding contact by interfacial friction leads to thermoelastic distortion of the contact surface, further modifying contact parameters such as pressure, gap or temperature. The thermal expansion of the contacting bodies must therefore be accounted for when solving the contact problem. The thermoelastic displacement is computed with the aid of the half-space theory and of fundamental solutions for point sources of heat located at the free surface, derived in the literature of heat conduction in solids. The linearity of conduction equations encourages the use of superposition principle in the same way as for the elastic displacement. As the thermoelastic displacement is expressed mathematically as a convolution product, methods derived in contact mechanics for elastic displacement calculation are adapted to the heat conduction equations. The influence coefficients needed to efficiently compute the convolution products are derived, and the Discrete Convolution Fast Fourier Transform technique is applied to improve the algorithm computational efficiency. A similar method is then advanced for the temperature rise on the contact interface due to arbitrary heat input. The predictions of the newly advanced computer programs are tested against existing closed-form solutions for uniform circular or ring heat sources, and a good agreement is found.

A model for fretting contact of layered materials with interfacial cracks

Surfaces of tightly fitted joints such as splines and dovetails in aero-engines are expected in nominally static contact but experience small amplitude oscillations in practice, leading to fretting fatigue due to nucleation and propagation of micro-cracks. Modern surface coatings are designed to improve the anti-friction/wear capabilities but result in severe stress concentrations across the bonding interfaces due to property mismatch, which may generate interfacial cracks and lead to delamination damage. In this study, a numerical model is developed to investigate the fretting behavior of layered materials with mixed-mode I and II cracks at the layer/layer/substrate interfaces in plane-strain conditions. The layer-type impurities are assumed as a bunch of inclusions, whilst the crack-type ones are simulated as discrete edge dislocations. The governing equations are established based on the analysis of stress state, and the impurity-induced displacement is taken into account for an accurate description of the fretting contact. The intensive interactions in the contact system are determined by a multi-loop algorithm, and the calculation efficiency is improved by using the Fast Fourier Transform (FFT) technique. The model can be easily handled without refining meshes near the crack tips, and the calculation time is far less than that of the finite element model to achieve comparable solutions. The model is also capable of simulating cracks with their faces in contact by disabling the loop that determines the mode I crack solution. Details are discussed on the surface traction and subsurface stress influenced by layer films and embedded cracks. The solutions to crack-face displacement are also presented to reveal the potential crack patterns enforced by the time-dependent contact loading. The conclusions are expected to provide insights into enhancing the damage resistance of surfaces suffering from fretting.

Rolling mill health states diagnosing method based on multi-sensor information fusion and improved DBNs under limited datasets

Due to the harsh working conditions and high cost of data acquisition in the actual environment of modern rolling mills, the resulting limited datasets issue leading in performance collapse of traditional deep learning (DL) methods has been plaguing researchers and needs to be urgently addressed. Hence, an improved single-sensor Deep Belief Network (IDBN) is first proposed to repetitively extract valuable information from hidden features and visible features of the previous improved Restricted Boltzmann Machine (IRBM) to alleviate this issue. Next, the multi-sensor IDBNs (MSIDBNs) are applied to obtain complementary and enriched health state features from different multi-sensor data to cope with limited datasets more effectively. Then, the Fast Fourier Transform (FFT) technique is adopted for the multi-sensor information to further enhance the effectiveness of feature extraction. Most importantly, the redefined pretraining and finetuning stages are designed for the MSIDBNs. Meanwhile, the optimal placement of multiple sensors is fully discussed to obtain the most efficient information about health content. Finally, two limited datasets are conducted to validate the superiority of the proposed MSIDBNs. Results show that the proposed MSIDBNs are capable of extracting valuable features from multi-sensor information and achieving more remarkable performance compared with the state-of-the-art (SOTA) methods under limited datasets.

Monitoring evolution of debris-filled damage using pre-modulated wave and guided wave ultrasonic testing (GWUT)

Metals are essentially used to construct structures of high economic importance, but their service life is shortened by damage such as crack, corrosion, cavity, and dents. Damage to structure causes gradual degradation of its strength and compromises its load-carrying capacity. Some damage could be designed against but pitting corrosion, especially when it is filled with debris that would hide it and deepen its perforation activities. In this work, monitoring the evolution of debris-filled damage in structure is studied using pre-modulated wave and guided wave ultrasonic testing (GWUT). The structural responses in the varying percentage of debris-filled damage were processed using Fast Fourier Transform (FFT), Hilbert Transform (HT) and correlation techniques. The regression analysis of debris-filled damage resulted in empirical predictive models that could detect the damage and percentage of debris that might have filled it with average percentage errors of 1.34 and 0.21, respectively. The debris in the damage caused an overmodulation of the high-frequency signal. The intensity of the lower sideband (LSB) was observed to be very sensitive to variations in the damage than the upper sideband (USB). The transmission power efficiency of modulation sidebands increased proportionally with debris-filled damage percentage and could serve as a damage index.

Unsteady Flow Oscillations in a 3-D Ventilated Model Room with Convective Heat Transfer

Improving indoor air quality and energy consumption is one of the high demands in the building sector. In this study, unsteady flow oscillations in a 3-D ventilated model room with convective heat transfer have been studied for three configurations of an empty room (case 1), a room with an unheated box (case 2) and a room with a heated box (case 3). Computational results are validated against experimental data of airflow velocity, temperature and turbulence kinetic energy. For each case, flow unsteadiness is presented by the time history of airflow velocity and temperature at prescribed monitor points and further analyzed using the Fast Fourier Transform technique. For case 1, the flow oscillation is irregular and less dependent on the monitor points. For case 2, the flow oscillation is still irregular but with increased frequency, possibly due to enhanced flow recirculation around the corners of the unheated box. For case 3, a dominant frequency exists, and thermal energy oscillating is higher than flow kinetic energy. Among the three cases, case 3 has the highest dominant frequency in a range of 4.3–4.6 Hz, but the kinetic energy is the lowest at 1.25 m2⁄s. The unsteady flow oscillation is likely due to a high Grashof number and corner flow recirculation for cases 1 and 2, and a combination effect of a high Grashof number, corner flow recirculation and thermal instability (induced by the formation and movement of the thermal plume) for case 3.

SFOC: A NOVEL MULTI-DIRECTIONAL AND MULTI-SCALE STRUCTURAL DESCRIPTOR FOR MULTIMODAL REMOTE SENSING IMAGE MATCHING

Abstract. Accurate matching of multimodal remote sensing (RS) images (e.g., optical, infrared, LiDAR, SAR, and rasterized maps) is still an ongoing challenge because of nonlinear radiometric differences (NRD) between these images. Considering that structural properties are preserved between multimodal images, this paper proposes a robust matching method based on multi-directional and multi-scale structural features, which consist of two critical steps. Firstly, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed to address severe NRD, which combines the first- and second-order gradient information by using the steerable filters to depict multi-directional and multi-scale structural features of images. Meanwhile, SFOC is further enhanced by performing the dilated Gaussian convolutions with different dilated rates on it, which can capture multi-level context structural features and improve the ability to resist noise. Then, a fast similarity measure, called Fast Normalized Cross-Correlation (Fast-NCCSFOC), is established to detect correspondences by a template matching scheme, which employs the Fast Fourier Transform (FFT) technique and the integral image to improve the matching efficiency. The performance of the proposed SFOC has been evaluated with many different kinds of multimodal RS images, and experimental results show its superior matching performance compared with the state-of-the-art methods.

A robust multimodal remote sensing image registration method and system using steerable filters with first- and second-order gradients

Co-registration of multimodal remote sensing (RS) images (e.g., optical, infrared, LiDAR, and SAR) is still an ongoing challenge because of nonlinear radiometric differences (NRD) and significant geometric distortions (e.g., scale and rotation changes) between these images. In this paper, a robust matching method based on the Steerable filters is proposed consisting of two critical steps. First, to address severe NRD, a novel structural descriptor named the Steerable Filters of first- and second-Order Channels (SFOC) is constructed, which combines the first- and second-order gradient information by using the steerable filters with a multi-scale strategy to depict more discriminative structure features of images. Then, a fast similarity measure is established called Fast Normalized Cross-Correlation (Fast-NCCSFOC), which employs the Fast Fourier Transform (FFT) technique and the integral image to improve the matching efficiency. Furthermore, to achieve reliable registration performance, a coarse-to-fine multimodal registration system is designed consisting of two pivotal modules. The local coarse registration is first conducted by involving both detection of interest points (IPs) and local geometric correction, which effectively utilizes the prior georeferencing information of RS images to address global geometric distortions. In the fine registration stage, the proposed SFOC is used to resist significant NRD, and to detect control points (CPs) between multimodal images by a template matching scheme. The performance of the proposed matching method has been evaluated with many different kinds of multimodal RS images. The results show its superior matching performance compared with the state-of-the-art methods. Moreover, the designed registration system also outperforms the popular commercial software (e.g., ENVI, ERDAS, and PCI) in both registration accuracy and computational efficiency. Our system is available at https://github.com/yeyuanxin110/SFOC-Multimodal_Remote_Sensing_Image_Registration_System.

Laser speckle image velocimetry by Fast Fourier Transform technique

In this paper, laser image velocimetry, a unique optical method for the velocity measurement of fluid flow has been validated. A laser sheet is illuminated on microscopic seeded particles to produce the speckle pattern at the recording plane. Double frame-single exposure speckle images are captured in such a way that the second speckle image is shifted exactly in a known direction. The auto-correlation method has an ambiguity to identify the direction of flow. To sort out this problem, the partial shift of the second image has been premeditated. Cross-correlation of sub interrogation areas of the speckle patterns is attained by the Fast Fourier Transform technique. Segmented four sub-windows are processed to acquire the velocity information with a vector map precisely.

Response of a Tandem-Staged Compressor to Circumferential Inflow Distortion

Abstract Tandem rotor, as a concept, promises a higher diffusion capacity than a single rotor. However, it is reported to have a lower operating range than the conventional rotor. Inflow distortion can lead to significant changes in the flow dynamics of the axial-flow compressor. Further, a highly loaded tandem blade is likely to be severely affected due to the inflow distortions. In this study, circumferential distortion is imposed at the inlet of a tandem stage and its response is studied, using steady and unsteady probes. The performance characteristics and other aerodynamics parameters under the circumferential distortion are compared with the clean inflow case. As expected, the tandem stage experiences a significant drop in the total pressure rise and operating range under circumferential distortion. A large deficit in the total pressure, efficiency, and axial velocity is observed in the distorted region. The casing pressure traces, Morlet wavelet analysis, and Fast Fourier Transform techniques are used to analyze the unsteady data. The stall is incepted in the form of a low-intensity spike, which later evolved into a fully grown stall within 2.5 rotor revolutions. Further, the amplitude of the stall cell under the circumferential distortion is found to be higher than the clean flow.