Fast Fourier Transform Algorithm Research Articles

A short time series rolling bearing fault diagnosis method based on FMTF-CNN

Abstract The signal characteristics are extracted directly from the convolutional level when the Convolutional Neural Network (CNN) is used as a fault diagnosis method in most instances. Some scholars have proposed to use the Markov Transition Field (MTF) to extract the signal characteristics before convolution. However, for time-domain signals, the fault characteristics extraction is difficult, and the training process of the neural network parameters is slow. Therefore, in this paper, based on Fast Fourier Transform (FFT) and MTF algorithm, the Fourier Markov Transition Field (FMTF) characteristics extraction method in frequency domain is proposed, and a large number of experiments are carried on the public bearing data set of Jiangnan University. The final verification shows that the FMTF-CNN method has higher accuracy, faster training process and more accurate characteristics extraction than the traditional MTF-CNN diagnosis method.

Efficient Cyclostationary Spectrum Sensing Using Low Complexity FFT Algorithms

One of the major problems of cyclostationary spectrum sensing (CSS) system in cognitive radios (CR) are the implementation complexity. One possible way to reduce CSS complexity is to use efficient algorithms for performing Fast Fourier Transformation (FFT). Over the years, a lot of different FFT algorithms have been created. This includes the Split-Radix algorithm., the Fast Hartley Transform (FHT), and slide DFT. This paper investigates the suitable FFT algorithm among the aforementioned techniques, cyclostationary feature detection (CFD)-based spectrum sensing stands out. The methods have been thoroughly compared based on computational time, object size, code size, data dependence (real or complex), and the amount of mathematical operations involved in the computations. Simulation results show that slide FFT is the suitable frequency domain transformation algorithm to use in implementing cyclostationary spectrum sensing in cognitive radios as compared to the other considered algorithms where it provides a significant reduction in FFT stage computation complexity reach to 17% in SRFFT , 78% in FHT and 82% in SDFT while keeping the detection probability at satisfactory level.

Optimization of the Weight Processing Algorithm in Multichannel Doppler Filtering

Introduction. The use of non-equidistant pulse sequences as probing radar signals makes it possible to eliminate blind spots in speed and range. However, the implementation of multi-channel Doppler filtering (MDF) based on the classical fast Fourier transform (FFT) algorithm of non-equidistant signal samples in the signal detection problem is associated with energy losses. The use of modified FFT algorithms increases the efficiency of MDF against the background of white Gaussian noise, while reducing the efficiency of signal accumulation in the part of signal processing channels blocked by the narrow-band clutter. To eliminate this drawback, the authors previously proposed using combined classical and modified FFT algorithms. However, the use of the combined method does not lead to an optimal solution in terms of MDF efficiency.Aim. Optimization of weight processing of non-equidistant signals to improve the efficiency of MDF.Materials and methods. An MDF synthesis was carried out using optimization procedures, and the effectiveness of the algorithms was assessed using computer calculations.Results. The results show that the Kaiser Bessel window with a window parameter of 4.42 provides the highest signal-(clutter+noise) ratio improvement coefficient averaged over frequency channels equal to 30.06 dB and the highest probability of correct signal detection averaged over MDF channels equal to 0.5 at processing of non-equidistant pulse sequences. Optimization of the weight processing of MDF under the specified conditions increased the average efficiency characteristics used of up to 53.18 dB and 0.92, respectively.Conclusion. Separate optimization of weighting processing for each frequency channel can significantly improve the average efficiency characteristics of a multichannel Doppler filter and eliminate all the shortcomings of the classical and modified FFT algorithms when processing non-equidistant pulse sequences. However, these advantages are achieved at the cost of not using the FFT, i.e., implemented within the framework of the discrete Fourier transform (DFT) algorithm.

Lateral Heat Distribution Characteristics of CLP S275 Using Gaussian FFT Algorithm in Optical Thermographic Testing

In general, when using infrared thermography (IRT) techniques to excite a heat source on the surface of an inspection object, the heat source is focused on the center of the image of the infrared (IR) camera. If the object to be inspected is small, uniform excitation of the heat source is possible, but if the area is large, the heat source is concentrated locally, resulting in uneven heat distribution. Therefore, in this study, heat distribution was analyzed after inducing a non-uniform heat source by exciting the heat source at different locations. Additionally, the fast Fourier transform (FFT) algorithm with Gaussian filtering was applied to resolve the non-uniform distribution of the heat sources. Excellent results were obtained from the amplitude image, and the effectiveness of the FFT algorithm was verified using the Otsu algorithm. Finally, the signal-to-noise ratio (SNR) was calculated, and the detection ability according to each thinning rate was analyzed.

Numerical modeling of edge inhomogeneity in a macro cantilever beam

This study explores the inhomogeneity problem near the edge of a cantilever beam. Common in gears and turbine blades, inhomogeneities under local edge contact, coupled with macroscale bending effects, result in extreme and complex stress concentration. This model employs the Conjugate Gradient Method to combines the macroscopic beam theory and microscopic elasticity. The Eshelby inclusion method for simulating inhomogeneities. The Hetényi image method is used for address edge contacts in the beam. Enhanced by the Discrete Convolution fast Fourier Transform algorithm, the model’s accuracy and efficiency are validated with the Finite element method. Parametric analysis highlights the influence of distance between indenter and edge, stress components distribution and potential failure in cross-scale challenges.

Numerical Simulation of Flows Using the Fourier Pseudospectral Method and the Immersed Boundary Method

The present work proposes the application of a computational methodology based on the coupling of the Fourier Pseudospectral Method (FPSM) and the Immersed Boundary Method (IBM) for conducting flow simulations over slender airfoils. This methodology, termed IMERSPEC, leverages the benefits of both high accuracy and low computational cost inherent in pseudospectral methods, thanks to the utilization of the Fast Fourier Transform algorithm. IBM is employed to impose non-periodic boundary conditions in the Navier–Stokes equations, addressing the requirement of periodicity at boundaries for FPSM convergence and to accurately represent the immersed slender airfoil in the flow. The aerodynamic behavior of the analyzed profiles was assessed by calculating lift and drag coefficients, which were then compared with existing literature results. Consistently favorable outcomes were observed, particularly in flows at lower Reynolds numbers, demonstrating the effectiveness of the IMERSPEC methodology for simulating complex flows computationally. Additionally, weight functions, fundamental to IBM, are employed flexibly for aerodynamic force calculations. Specifically, within the same simulation, a Cubic function is utilized for drag calculation while a Hat function is employed for lift calculation, yielding results more closely aligned with the literature’s findings. This approach offers an alternative to previously proposed methods for IBM implementation.

Memory-Based Self-Ordering FFT for Efficient I/O Scheduling

A complex-valued self-ordering radix-2 memory-based Fast Fourier Transform (FFT) architecture suitable for low end Field Programmable Gate Arrays (FPGA) is presented. Employing a self-ordering algorithm within the data flow, both input and output data are kept in normal sequential order, not in digit-reversed-order. This way, with an appropriate scheduling, last stage of the FFT and I/O operations are performed in parallel with no wait states. Self-ordering FFT algorithms are generally designed for software implementations. We designed and implemented one on FPGA (hardware), showing that considerable number of clock cycle savings can be obtained compared to unordered FFT counterparts. The approach is implemented on various FPGAs. The results are compared with similar radix-2 architectures in terms of required clock cycles and resource usage, confirming the advantage of the approach.

Higher-order spectral representation method: New algorithmic framework for simulating multi-dimensional non-Gaussian random physical fields

This study derives a novel higher-order spectral representation method (HOSRM) to represent and simulate multi-dimensional fourth-order non-Gaussian random physical fields. The method primarily extends the traditional second-order spectral representation method (SRM) for simulating non-Gaussian random physical fields by introducing higher-order cumulant function tensors and trispectrum tensors, thereby accomplishing the modeling of fourth-order non-Gaussian random physical fields (symmetric nonlinear physical fields) from a frequency domain perspective. In an endeavor to enhance the simulation efficiency of this theoretical framework, the Fast Fourier Transform (FFT) algorithm is astutely amalgamated into the simulation. This integration contributes significantly to the augmentation of computational efficiency. Furthermore, exhaustive derivations and proofs are presented for the first-order, second-order, and fourth-order ensemble properties of simulated fourth-order non-Gaussian random physical fields. Subsequently, the reliability and accuracy of the proposed algorithm framework are validated through numerical simulations of two two-dimensional and two three-dimensional non-Gaussian random physical fields. The findings demonstrate that the simulated sample function effectively captures the probability characteristics of the random field, including mean, variance, and kurtosis. Finally, an in-depth analysis of numerical simulation results highlights the difference between the proposed method and the traditional second-order spectral representation method, which further underscores the distinctive attributes and potential superiority of the proposed method.

Millimeter wave-based patient setup verification and motion tracking during radiotherapy.

Position verification and motion monitoring are critical for safe and precise radiotherapy (RT). Existing approaches to these tasks based on visible light or x-ray are suboptimal either because they cannot penetrate obstructions to the patient's skin or introduce additional radiation exposure. The low-cost mmWave radar is an ideal solution for these tasks as it can monitor patient position and motion continuously throughout the treatment delivery. To develop and validate frequency-modulated continuous wave (FMCW) mmWave radars for position verification and motion tracking during RT delivery. A 77GHz FMCW mmWave module was used in this study. Chirp Z Transform-based (CZT) algorithm was developed to process the intermediate frequency (IF) signals. Absolute distances to flat Solid Water slabs and human shape phantoms were measured. The accuracy of absolute distance and relative displacement were evaluated. Without obstruction, mmWave based on the CZT algorithm was able to detect absolute distance within 1mm for a Solid Water slab that simulated the reflectivity of the human body. Through obstructive materials, the mmWave device was able to detect absolute distance within 5mm in the worst case and within 3.5mm in most cases. The CZT algorithm significantly improved the accuracy of absolute distance measurement compared with Fast Fourier Transform (FFT) algorithm and was able to achieve submillimeter displacement accuracy with and without obstructions. The surface-to-skin distance (SSD) measurement accuracy was within 8mm in the anterior of the phantom. With the CZT signal processing algorithm, the mmWave radar is able to measure the absolute distance to a flat surface within 1mm. But the absolute distance measurement to a human shape phantom is as large as 8mm at some angles. Further improvement is necessary to improve the accuracy of SSD measurement to uneven surfaces by the mmWave radar.

Impact of beam-coupling impedance on the Schottky spectrum of bunched beam

The Schottky monitors of the Large Hadron Collider (LHC) can be used for non-invasive beam diagnostics to estimate various bunch characteristics, such as tune, chromaticity, bunch profile or synchrotron frequency distribution. However, collective effects, in particular beam-coupling impedance, can significantly affect Schottky spectra when large bunch charges are involved. In such conditions, the available interpretation methods are difficult to apply directly to the measured spectra, thus preventing the extraction of beam and machine parameters, which is possible for lower bunch charges. To study the impact of impedance on such spectra, we introduce a method for building Schottky spectra from macro-particle simulations performed with the PyHEADTAIL code, applied to LHC beam conditions. In this case, the use of a standard Fast Fourier Transform (FFT) algorithm to recover the spectral content of the beam becomes computationally intractable memory-wise, because of the relatively short bunch length compared to the large revolution period. To circumvent this difficulty, a semi-analytical method was developed to efficiently compute the Fourier transform. The simulated Schottky spectrum is then compared against theoretical formulas and measurements of Schottky signals previously obtained with lead ion beams in the LHC where impedance effects are expected to be limited. Furthermore, this study provides preliminary interpretations of the impact of beam-coupling impedance on proton Schottky spectra by incorporating longitudinal and transverse resonator-like impedance models into the simulations. A theoretical framework is also introduced for the case of the longitudinal impedance, allowing the extension of the existing theoretical formalism.

Is Acoustic modal analysis a reliable substitution for Osstell® device in dental implant stability assessment? An experimental and finite element analysis study.

Different methods have been proposed to investigate the fixation stability of dental implants, each of which has its limitations. Among these methods, resonance frequency analysis (RFA) has been widely utilized to measure dental implant stability. This study aimed to assess dental implants with two non-destructive RFA and acoustic modal analysis (AMA) validated with a finite element simulation of the fundamental natural frequency (NF) of the bone analog-implant structure. A total number of 18 implants were inserted into two Polyurethane (PU) bone blocks with different densities (0.16 g/cc and 0.32 g/cc). AMA was used to measure NF; First, the sound originating from the axial tapping of the implant was recorded with a simple microphone. Secondly, a fast Fourier transformation algorithm was conducted to determine the NF of the implant-bone analog structure. In parallel, the ISQ (Implant Stability Quotient) value was measured using the Osstell® device. Finally, using finite element analysis (FEA), the implant-bone analog structure was modeled for validation. Doubling the bone analog density resulted in an average increase of 82% and 47% in the NF and ISQ using AMA and Osstell®, respectively (P-value<0.05). Furthermore, a strong linear relationship (R2= 0.93) was observed between the measured NF and ISQ values in the linear regression analysis. The NF of the dental implant predicted by FEA was overestimated by about 15.2% and 15.0% than those in the low- and high-density PUs, respectively. Moreover, the FEA predicted an increase of 83% in NF by increasing the bone analog density from 0.16 to 0.32 g/cc. Having required the minimum process combined with easily available equipment makes it an ideal method for fixation strength studies. The good correspondence between the ISQ values and NFs, in addition to the good accuracy and reliability of the later method, confirms its application for fixation stability assessment.

Reliability of a semi-tethered front crawl sprint performance test in adolescent swimmers.

This study aimed to evaluate the test-retest reliability of a sprint performance test with semi-tethered front crawl swimming to indirectly assess the current potential to perform at maximal anaerobic effort in adolescent swimmers. Eight adolescent swimmers participated in this study (gender: females (n = 4) aged 13.0 ± 0.8 years, body height 1.6 ± 0.0m, body mass 50.1 ± 4.5kg; and males (n = 4) aged 13.3 ± 1.3 years, body height 1.7 ± 0.1m, body mass 59.0 ± 8.2kg. The testing protocol consisted of two trials of 25m semi-tethered front crawl swimming with maximal effort and with 1kg resisted isotonic load. Velocity data were recorded automatically by the 1080 Sprint device for 15m (between 3m and 18m). The Fast Fourier Transform algorithm filtered raw instantaneous swimming velocity data in distance (time) function. A third-degree polynomial was used to extract the individual velocity profile, from which the following variables were chosen for test-retest reliability and the assessment of sprint performance: ttrial15, vmax, vmin, tvto max, tvat max, Dto vmax, Dat vmax, fatigue index. Parameters such as vmax, vmin, and ttrial15 were estimated from swimming velocity profiles and considered as reliable. The CV showed low variance <5%; while ICC2,1 demonstrated respectively good (ICC2,1: 0.88), very good (ICC2,1: 0.95), and excellent (ICC2,1: 0.98) rate of relative reliability; and the Bland-Altman index revealed an acceptable agreement (LoA ≤5%) between two measurements. The sprint performance test based on semi-tethered front crawl swimming confirmed that ttrial15, vmax, and vmin were reliable variables to indirectly indicate a potential to perform the maximal anaerobic effort among adolescent swimmers. The evaluation of the swimming velocity profiles allows coaches to monitor the adaptive changes of performance during the training process.

Seasonality of COVID-19 incidence in the United States.

The surges of Coronavirus Disease 2019 (COVID-19) appeared to follow a repeating pattern of COVID-19 outbreaks regardless of social distancing, mask mandates, and vaccination campaigns. This study aimed to investigate the seasonality of COVID-19 incidence in the United States of America (USA), and to delineate the dominant frequencies of the periodic patterns of the disease. We characterized periodicity in COVID-19 incidences over the first three full seasonal years (March 2020 to March 2023) of the COVID-19 pandemic in the USA. We utilized a spectral analysis approach to find the naturally occurring dominant frequencies of oscillation in the incidence data using a Fast Fourier Transform (FFT) algorithm. Our study revealed four dominant peaks in the periodogram: the two most dominant peaks show a period of oscillation of 366 days and 146.4 days, while two smaller peaks indicate periods of 183 days and 122 days. The period of 366 days indicates that there is a single COVID-19 outbreak that occurs approximately once every year, which correlates with the dominant outbreak in the early/mid-winter months. The period of 146.4 days indicates approximately 3 peaks per year and matches well with each of the 3 annual outbreaks per year. Our study revealed the predictable seasonality of COVID-19 outbreaks, which will guide public health preventative efforts to control future outbreaks. However, the methods used in this study cannot predict the amplitudes of the incidences in each outbreak: a multifactorial problem that involves complex environmental, social, and viral strain variables.

Analysis of the Mitochondrial Density and Longitudinal Distribution in Rat Live-Skeletal Muscle Fibers by Confocal Microscopy.

The mitochondrion is an organelle that can be elongated, fragmented, and renovated according to the metabolic requirements of the cells. The remodeling of the mitochondrial network allows healthy mitochondria to meet cellular demands; however, the loss of this capacity has been related to the development or progression of different pathologies. In skeletal muscle, mitochondrial density and distribution changes are observed in physiological and pathological conditions such as exercise, aging, and obesity, among others. Therefore, the study of the mitochondrial network may provide a better understanding of mechanisms related to those conditions. Here, a protocol for mitochondria imaging of live-skeletal muscle fibers from rats is described. Fibers are manually dissected in a relaxing solution and incubated with a fluorescent live-cell imaging indicator of mitochondria (tetramethylrhodamine ethyl ester, TMRE). The mitochondria signal is recorded by confocal microscopy using the XYZ scan mode to obtain confocal images of the intermyofibrillar mitochondrial (IMF) network. After that, the confocal images are processed by thresholding and binarization. The binarized confocal image accounts for the positive pixels for mitochondria, which are then counted to obtain the mitochondrial density. The mitochondrial network in skeletal muscle is characterized by a high density of IMF population, which has a periodic longitudinal distribution similar to that of T-tubules (TT).The Fast Fourier Transform (FFT) is a standard analysis technique performed to evaluate the distribution of TT that allows finding the distribution frequency and the level of their organization. In this protocol, the implementation of the FFT algorithm is described for the analysis of the longitudinal mitochondrial distribution in skeletal muscle.

Varieties of Systems of DEF Generated by Isomorphic Transformations

Despite more than a century of origin and development, the theory of discrete exponential function (DEF) systems continues to attract the attention of mathematicians and application specialists in various fields of science and technology. One of the most successful applications of the DEF systems is the spectral processing of discrete signals based on fast Fourier transform (FFT) algorithms in the DEF bases. The construction of structural schemes of FFT algorithms is preceded, as a rule, by the factorization of the DEF matrices. The main problem encountered when factorizing DEF matrices is that the elements of such matrices are the degrees of phase multipliers, which are complex-valued quantities. In this connection, the computational complexity of factorization of DEF matrices may be too large, especially when the number of components of the matrix order decomposition is large. In this paper, we propose a relatively simple method of mutually unambiguous transition from complex-valued DEF matrices to matrices whose elements are natural numbers equal to the degree indices of phase multipliers in the canonical DEF matrices. Through this bijective transformation, the factorization of DEF matrices becomes significantly more manageable, streamlining the overall process of factorization.

Moving Target Detection Algorithm for Millimeter Wave Radar Based on Keystone-2DFFT

Millimeter wave radar has the advantage of all-day and all-weather capability for detection, speed measurement. It plays an important role in urban traffic flow monitoring and traffic safety monitoring. The conventional 2-dimensional Fast Fourier Transform (2DFFT) algorithm is performed target detection in the range-Doppler domain. However, the target motion will induce the range walk phenomenon, which leads to a decrease in the target energy and the performance of the target detection and speed measurement. To solve the above problems, this paper proposes a moving vehicle detection algorithm based on Keystone-2DFFT for a traffic scene. Firstly, this paper constructs and analyzes the Frequency Modulated ContinuousWave (FMCW) moving target signal model under traffic monitoring scenario’s radar observation geometry. The traditional 2DFFT moving target detection algorithm is briefly introduced. Then, based on mentioned signal model, an improved moving vehicle detection algorithm based on Keystone-2DFFT transform is proposed. The method first input the echo, then the range walk is removed by keystone transformation. the keystone transformation is achieved via Sinc interpolation. Next is transform data into range-Doppler domain to perform detection and speed estimation. The algorithm is verified by simulation data and real data.

Stator current signal crossing for fault diagnosis of self-excited induction generators

This paper presents a novel method for modelling and diagnosis of electrical and mechanical faults in fixed-Speed Self-Excited Induction Generators (SEIGs) operating in autonomous mode in a small-scale wind energy system. The proposed method is validated using the finite element method. After the selection of the magnetising capacitors, the self-excitation process is performed under no-load conditions. Once the stator voltage is established, a symmetrical three-phase load is connected. The fault detection method introduced here is called Stator Current Signal Crossing (SCSC). The SCSC extracts a new signal from the stator currents, that enables the detection of stator inter turn shortcircuits, broken rotor bars, and dynamic eccentricity faults in SEIGs. A spectral analysis of SCSC using the Fast Fourier Transform (FFT) algorithm is used to precisely locate the induced fault components. What sets this fault-tracking method apart from its predecessors is its exceptional ability to detect faults of any magnitude by analysing the modulation of the SCSC signal. These faults are directly identified by the presence of distinct harmonics, each indicative of a specific type of fault. This study also focuses on the SEIG in a wind energy system, whereas previous works have mainly addressed the induction machine in motor mode. In contrast, previous methods involved analysing a single current signal and isolating specific harmonics from a wide frequency range. The effectiveness of the proposed fault detection method and the self-excitation process are illustrated by simulation results and spectral analysis.

A Short-term Load Curve Intelligent Prediction Method Based on FFT and GA Composite Algorithm and Its Application

In order to reduce the system operation cost of predicting short-term load curves and improve the feasibility of artificial intelligence prediction, the FFT and GA composite algorithm is designed as an artificial intelligence algorithm with the goals of low time complexity, small required sample data, and high flexibility. The algorithm uses mature Fast Fourier Transform and Genetic Algorithm, and improves the flexibility of the prediction method by dynamically adding kernel functions. It can be used for load curve prediction of 96 data points or smaller granularity, and can also reuse the calculation results of big data prediction methods for larger data granularity. By improving the feasibility of individual low-reward data predictions, it can improve the accuracy, sensitivity, and adaptability of energy management plans, accelerate the high-quality development of the power grid, improve the level of digitization, and promote demand response.

Validation of Proprietary and Novel Step-counting Algorithms for Individuals Ambulating With a Lower Limb Prosthesis

ObjectiveTo compare the accuracy and reliability of 10 different accelerometer-based step-counting algorithms for individuals with lower limb loss, accounting for different clinical characteristics and real-world activities. DesignCross-sectional study. SettingGeneral community setting (ie, institutional research laboratory and community free-living). ParticipantsForty-eight individuals with a lower limb amputation (N=48) wore an ActiGraph (AG) wGT3x-BT accelerometer proximal to the foot of their prosthetic limb during labeled indoor/outdoor activities and community free-living. InterventionsNot applicable. Main Outcome MeasuresIntraclass correlation coefficient (ICC), absolute and root mean square error (RMSE), and Bland Altman plots were used to compare true (manual) step counts to estimated step counts from the proprietary AG Default algorithm and low frequency extension filter, as well as from 8 novel algorithms based on continuous wavelet transforms, fast Fourier transforms (FFTs), and peak detection. ResultsAll algorithms had excellent agreement with manual step counts (ICC>0.9). The AG Default and FFT algorithms had the highest overall error (RMSE=17.81 and 19.91 steps, respectively), widest limits of agreement, and highest error during outdoor and ramp ambulation. The AG Default algorithm also had among the highest error during indoor ambulation and stairs, while a FFT algorithm had the highest error during stationary tasks. Peak detection algorithms, especially those using pre-set parameters with a trial-specific component, had among the lowest error across all activities (RMSE=4.07-8.99 steps). ConclusionsBecause of its simplicity and accuracy across activities and clinical characteristics, we recommend the peak detection algorithm with set parameters to count steps using a prosthetic-worn AG among individuals with lower limb loss for clinical and research applications.

Wireless Hybrid Vehicle Three-Phase Motor Diagnosis Using Z-Freq Due to Unbalance Fault

Online diagnostics of three phase motor rotor faults of hybrid vehicle can be identified using a method called machine learning. Unfortunately, there is still a constraint in achieving a high success rate because a huge volume of training data is required. These faults were represented on its frequency content throughout the Fast Fourier Transform (FFT) algorithm to observe data acquired from multi-signal sensors. At that point, these failure-induced faults studies were improved using an enhanced statistical frequency-based analysis named Z-freq to optimize the study. This analysis is an investigation of the frequency domain of data acquired from the turbine blade after it runs under a specific condition. During the experiment, the faults were simulated by equipment with all those four conditions including normal mode. The failure induced by fault signals from static, coupled and dynamic were measured using high sensitivity, space-saving and a durable piezo-based sensor called a wireless accelerometer. The obtained result and analysis showed a significant pattern in the coefficient value and distribution of Z-freq data scattered for all flaws. Finally, the simulation and experimental output were verified and validated in a series of performance metrics tests using accuracy, sensitivity, and specificity for prediction purposes. This outcome has a great prospect to diagnose and monitor hybrid electric motor wirelessly.