Fast Fourier Research Articles

An Automated Sparse Channel Estimation Framework for MU‐MIMO‐OFDM System With Adaptive Extreme Learning and Heuristic Mechanism

ABSTRACTCompressed sensing is used for channel estimation in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems, but large‐scale networks face challenges regarding the antenna elements and spatial non‐stationarities. To enhance spectral efficiency in Multi User‐MIMO (MU‐MIMO) systems, essential signals are collected by Quadrature Phase Shift Keying (QPSK) in the transformer phase. Further, the modulated signals are provided to the “Pulse Shaping Algorithm (PSA)” for neglecting the inter‐symbol interference rate along with inter‐carrier interference. Subsequently, in the transmitter phase, the “Inverse Fast Fourier Transforms (IFFT)” technique is performed to map the symbols and then provide the signal to the receiver side. The spare channel is validated using a semi‐blind sparse algorithm, with parameters tuned using the Opposition Mud Ring Algorithm (OMRA). Then, the estimated sparse channel values are used for generating the new data, and the Adaptive Extreme Learning Model (AELM) is trained to predict the spare channel outcome. The main objective is to reduce the Minimum Mean Square Error (MMSE) in the channel. Thus, the spare channel outcome is predicted automatically using the AELM. Then, diverse evaluations are executed in the suggested spare channel estimation approach over the different mechanisms to observe their effectualness rate.

An application of the DEM-FFT method to predict the thermal conductivity of high burnup fragmented fuel

Understanding Fuel Fragmentation, Relocation and Dispersal (FFRD) phenomena is a major item to predict the behavior of fuel rods in the event of a Pressurized Water Reactor loss-of-coolant hypothetical scenario. In order to introduce an effective medium for fragmented fuel that could be used in fuel performance codes, the heat transfer properties of UO2 granular beds in He-Kr-Xe mixes was studied numerically. The Discrete Element Method was first used to compute the spatial arrangement of powder only submitted to gravity with a representative granulometric distribution. These generated geometries were then transformed into a 3D grid (voxelized) to perform numerical homogenization and compute their effective properties using a Fast Fourier Transform solver for heat transfer. Different hypotheses were made in the voxelation process about how the treatment of gas-solid and solid-solid interfaces, which in turn provided different estimates of the effective properties. Due to limits on the discretization and extended particle size distributions, a two-scale scheme was introduced and numerically tested against direct computations. For several beds, computed thermal conductivities were compared to analytical models for granular materials. Some heat transfer properties of fragmented fuel at several burnups were then computed, taking into account approximations for Knudsen and radiation size effects.

Analysis of the impact of misfiring on the throttle body in ayla vehicles using the fast fourier transform method

An essential component of an automobile's air intake system, the throttle body controls airflow during combustion by acting as an idle speed control mechanism. Making ensuring the throttle body is operating properly is crucial to keeping the engine running at its best. The Fast Fourier Transform vibration analysis is one technique used for this (FFT). A an LCGC Ayla 1000 cc vehicle's throttle body and engine underwent vibration testing with rotational rates set to 750 rpm, 1000 rpm, 1500 rpm, and 2000 rpm. Vibration responses were measured using an accelerometer sensor coupled to an FFT analyzer, and Matlab was used for analysis. The throttle body had anomalous frequency readings at 1977 Hz with an amplitude of 0.03171 m/s2, according to the test results. On the other hand, the frequency value at 1410 Hz with an amplitude of 0.03435 m/s2 was observed under normal circumstances. Similar to this, abnormal frequency values with an amplitude of 0.02378 m/s2 were found on the engine at 1493 Hz, whereas normal frequency values with the same amplitude were found at 1712 Hz. These results point to incomplete combustion as the cause of the increased vibration

An improved framework for mapping and assessment of dynamics in cropping pattern and crop calendar from NDVI time series across a heterogeneous agro-climatic region.

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003-2004 to 2021-2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19years, with an RMSE of 9days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment.

Comparative Evaluation of EEG signals for Mild Cognitive Impairment using Scalograms and Spectrograms with Deep Learning Models

Electroencephalography (EEG) is a valuable tool for studying brain function and identifying neurological disorders. This study aimed to analyze EEG data using various techniques for feature extraction and classification. The data was preprocessed by applying filters and dividing it into epochs. Feature extraction techniques, including Fast Fourier Transform (FFT) in the frequency domain and Continuous Wavelet Transform (CWT) in the time-frequency domain, were applied to convert the EEG signals into scalograms and spectrograms. The primary objective was to classify individuals with Mild Cognitive Impairment (MCI) and Healthy Controls (HC) using the scalograms and spectrograms with 2D Convolutional Neural Networks (CNN) and 2D Convolutional Recurrent Neural Networks (CRNN). The classification results obtained from epochs of different durations (5 seconds and 2 seconds) were compared. The analysis revealed that the 2D CRNN model incorporating scalograms achieved the highest classification accuracy of 87.79% for 5 sec epochs and 88.25% for 2 sec epochs. This demonstrates the effectiveness of using scalograms and spectrograms in combination with deep learning models for accurately classifying individuals with MCI and HC with EEG data.

Comparison of Short Fast Fourier Transform and Continuous Wavelet Transform in Study of Stride Interval

Neurodegenerative diseases (NDD) are a heterogeneous group of complex diseases characterized by neuronal loss and progressive degeneration of different areas of the nervous system. Gait analysis presents an early recognition system for NDD which is important to increase the patient’s awareness of their health conditions. However, it is very difficult to identify and formulate suitable digital biomarkers from the data collected from gait experiments such as stride interval and swing. The objective of this paper is to compare the result of Short - Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) on the collected stride interval of healthy young people and healthy old people. In this paper, STFT and CWT are performed on the collected stride interval and from the result of the STFT and CWT, further features are extracted like instantaneous RMS and maximum RMS value. STFT is performed on the collected stride interval from a window length of 64 to 512 while CWT is performed on the collected stride interval from the scale of 128 to 2048. The processing time of the STFT and CWT with varied window lengths and scales respectively are collected. Besides, the actual maximum time from the time - frequency plot derived from STFT and CWT is also collected. Both STFT and CWT show that the young group has a higher maximum RMS, an indication of higher stride interval than the old group and higher variance, an indication of higher gait complexity. The suitable window lengths for STFT in analyzing the stride interval are 64 and 128 while the scale for CWT should be set to the lowest scale. Overall, STFT with a window length of 64 and 128 is better in analyzing the stride interval due to low processing time at the expense of slightly less accurate time - frequency representation.

Stabilization system for solar loading thermography applied on cultural heritage objects exposed outdoors: the contribution of advanced algorithms and dual-branch U-Net

AbstractThis study investigates the use of solar loading thermography (SLT) for thermal non-destructive testing (TNDT) and image stabilization of cultural heritage objects, specifically focusing on a century-old ancient book. The irregular contours and deteriorated areas of the book posed significant challenges for feature extraction due to non-uniform temperature variations. To address these challenges, a convolutional neural network (CNN) based dual-branch network of U-Net was used to stabilize the dataset across three degrees of freedom with the ancient book. The stabilization process involved tracking feature lines across each frame of the time-domain datasets, correcting for frame misalignment caused by sample movement during prolonged data acquisition. The effectiveness of this stabilization technique was evaluated by comparing the results of principal component analysis (PCA), fast Fourier transform (FFT), and fast iterative filtering (FIF) algorithms before and after stabilization. Significant improvements were observed, particularly in the clarity and accuracy of defect detection, indicating that this technique provides a robust foundation for further analysis and processing of SLT datasets in cultural heritage preservation. This research demonstrates the potential of combining advanced image processing techniques with SLT to enhance the quality and reliability of NDT in preserving valuable historical artifacts.

Determination of creep function coefficients of viscoelastic pipes using a transient-guided machine learning model

ABSTRACT This study introduces a novel framework for the estimation of creep function coefficients in viscoelastic pipelines, employing a combination of machine learning (ML) and transient hydraulics. Specifically, the efficacy of eXtreme Gradient Boosting (XGBoost) as an advanced regression method is evaluated within this framework. A transient simulation model, utilizing the method of characteristics, is formulated to create the reservoir-pipe-valve (RPV) scenarios under diverse boundary conditions. After conducting model calibration, the model is used to generate datasets with the transient hydraulic responses at the measurement points. The fast Fourier transform (FFT) is then applied to transform the generated samples into the frequency domain. Feature selection is accomplished through principal component analysis (PCA) to identify optimal input variables for XGBoost. In estimating the creep function, six coefficients are employed, with the feature selection analysis indicating that each coefficient is associated with a specific signal. Importantly, it is demonstrated that attempting to estimate all six coefficients using a single set of signals is unfeasible. The results affirm the accuracy of the proposed ML-based framework in determining creep function coefficients.

RECONSTRUCTION OF THE SURFACE PROFILE OF OPTICAL-MECHANICAL STRUCTURE WITH WHITE LIGHT INTERFERENCE BY DEEP LEARNING U-NET MODEL

This research proposes a method to accurately determine the location of the interference signal peak in white light interferometry. This improves the accuracy and resolution of the measurement, enabling precise measurement and reconstruction of three-dimensional microstructures on optical surfaces. By using a deep learning algorithm with a U-net neural network architecture to generate a predicted signal that closely matches the reference signal, combined with Fast Fourier Transform to filter noise and fit the proposed function, the exact location of the envelope signal peak is determined, providing information about the height of the point. This method can achieve an accuracy of around 0.9 nanometers at a noise level of 50 dB, which is over 40% more accurate than the traditional Fourier transform method at various noise levels. The persistent issues of the traditional Fourier transform method, such as determining the location of the interference signal peak at a signal position and the accuracy being heavily influenced by noise and the fitting signal, are effectively addressed.

Micro- to nano-sized solid inclusions in magnetite record skarn reactions

Abstract. Magnetite is a widespread ore mineral in skarn systems and usually hosts a wide variety of inclusions. Micro- to nano-sized solid inclusions in magnetite are unique tools to track the evolutionary processes of its host mineral and, subsequently, to constrain the timing of the mineralization event. In this study, we characterize micro- to nano-sized solid inclusions in magnetite from the La Víbora magnesian skarn (Málaga, Spain) using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). TEM energy-dispersive X-ray spectrometry (EDS) analyses and compositional mapping expose two types of nano-inclusions oriented along the (111) of magnetite: type 1 includes dolomite, spinel, and Mg–Fe–Al silicate, and type 2 is made up of Mg–Fe–Al silicates enveloping the Mg-bearing amorphous silica phase. High-resolution transmission electron microscopy (HRTEM), selected-area electron diffraction (SAED), and fast Fourier transform (FFT) patterns reveal that the majority of the solid inclusions display non-oriented matrices compared to the host magnetite, precluding the possibility of sub-solidus processes. Instead, these inclusions are thought to preserve skarn mineral assemblages that were entrapped during the growth of magnetite. However, the local supersaturation of fluids trapped in the boundary layer of crystallizing magnetite is evidenced by coherent lattice orientation of precipitated and host magnetite and by the occurrence of an Mg-bearing amorphous silica phase. Our findings reveal that skarn reactions observed at field and microscopic scales are also recorded in nano-sized inclusions within magnetite. These observations underscore the significance of micro- to nano-scale solid inclusions in magnetite to decipher overprinted skarn reactions as well as constraining the timing of Fe mineralization events in skarns.

Research on frequency shift signal detection algorithm for jointless track circuits based on improved relaxation algorithm

The accuracy of ZPW-2000 frequency shift signal demodulation is related to the safety and efficiency of high-speed trains. This paper proposed a kind of ZPW-2000 frequency-shift signal detection algorithm based on improved Relaxation algorithm (ZFSD-IR) to address the issue of low accuracy in detecting ZPW-2000 frequency shift signal under strong noise interference, as the traditional fast Fourier transform (FFT) spectrum analysis methods are susceptible to fence effects and spectrum leakage effects. Firstly, the principle of ZPW-2000 jointless track circuit was analyzed, and then the ideal and simplified models of ZPW-2000 frequency shift signal were established, respectively. Finally, the simulation experiments were conducted on ZPW-2000 frequency shift signal detection under different sampling durations and signal-to-noise ratios (SNR). The effectiveness of the proposed algorithm was verified through comparative analysis with the traditional algorithms. The research results indicated that the ZFSD-IR algorithm has better accuracy in carrier-frequency and low-frequency estimation than traditional algorithms, and can achieve accurate detection of ZPW-2000 frequency shift signal under the lower SNR conditions. It has high detection accuracy and good anti-interference ability, ensuring the safe operation of high-speed trains.

Simulation and experimental analysis of traveling wave resonance of flexible spiral bevel gear system

Considering the traveling wave resonance (TWR) phenomenon of the spiral bevel gear system in the aero engine accessory casing, the flexible spiral bevel gear model is established by three-dimensional (3D) shell element and beam element. The dynamic model of flexible bevel gear system considering the change of meshing teeth pairs in the operating process is established by component mode synthesis (CMS) method and rotational modal projection method. The proposed model is verified by finite element (FE) solid model and experiment test results. On this basis, the TWR phenomenon of the bevel gear system is studied in combination with dynamic response and vibration stress experimental test. The results show that the bevel gear system has 3 nodal diameter resonance speeds within the actual operating speed range, which are one 1st nodal diameter resonance of pinion and two 1st nodal diameter resonance of gear. When the bevel gear system operates at TWR state, the amplitude corresponding to fm ± m·fr (fm is meshing frequency, m is nodal diameter number, fr is shaft rotational frequency) is larger than that corresponding to fm in the Fast Fourier transform (FFT) spectrum of the of the gear foundation vibration stress, and the vibration stress cloud map of the gear foundation shows the corresponding nodal diameter vibration shape. The research results provide a theoretical basis for feature recognition and influence law evaluation of TWR of bevel gear system.

Artificial Neural Network-Based Fault Detection on Nigerian 330kv Power Transmission Line

In Nigeria, there will inevitably be ongoing difficulties with 330kv power transmission lines. This drop in insulation strength between the phase conductors and the earthed screen encircling the conductors may be the cause of the difficulties that have emerged on the electric transmission circuit. Many scholars have approached these problems in different ways. Fast Fourier Transform (FFT), Wavelet Transform, Fourier Transform, S-Transform, and Fourier Series are a few of the techniques utilized to address these transmission line problems. It has not been widely and thoroughly adopted to solve problems in Power System Engineering, according to numerous study studies on artificial neural networks (ANNs). Consequently, the 330kV Power Transmission Line problems have been addressed in this research by using artificial neural networks (ANNs). the training efficacy of any ANN challenges diagnosis system. The MATLAB/Simulink R2018a was utilized to conduct simulations on an ANN challenges detector. The ANN detector was trained with pre-fault and fault signals as inputs, allowing for the identification of different types of line difficulties. Results indicated that at Mean Square Error (MSE) of 1.6856e-5, the best training performance for the challenges was attained. At Regression, it approaches zero (9.8958e-1). Because it met the requirements, the simulation's outcome is acceptable. Keywords: Power System, Transmission Line, Matlab/Simulink, Three Phase, Artificial neural networks, fault detection.

Spike by spike frequency analysis of amperometry traces provides statistical validation of observations in the time domain

Amperometry is a commonly used electrochemical method for studying the process of exocytosis in real-time. Given the high precision of recording that amperometry procedures offer, the volume of data generated can span over several hundreds of megabytes to a few gigabytes and therefore necessitates systematic and reproducible methods for analysis. Though the spike characteristics of amperometry traces in the time domain hold information about the dynamics of exocytosis, these biochemical signals are, more often than not, characterized by time-varying signal properties. Such signals with time-variant properties may occur at different frequencies and therefore analyzing them in the frequency domain may provide statistical validation for observations already established in the time domain. This necessitates the use of time-variant, frequency-selective signal processing methods as well, which can adeptly quantify the dominant or mean frequencies in the signal. The Fast Fourier Transform (FFT) is a well-established computational tool that is commonly used to find the frequency components of a signal buried in noise. In this work, we outline a method for spike-based frequency analysis of amperometry traces using FFT that also provides statistical validation of observations on spike characteristics in the time domain. We demonstrate the method by utilizing simulated signals and by subsequently testing it on diverse amperometry datasets generated from different experiments with various chemical stimulations. To our knowledge, this is the first fully automated open-source tool available dedicated to the analysis of spikes extracted from amperometry signals in the frequency domain.

Measurement of plasma electron energy distribution functions by Langmuir probe using a modified AC modulation method

Abstract In the diagnosis of vacuum discharge plasma using the Langmuir probe, the electron energy distribution function(EEDF) is closely related to the second derivative of current to voltage (d2I/dV2) on the Langmuir probe. While d2I/dV2 is very sensitive to probe noise, its accuracy directly affects the measurement results of the electron energy distribution function. In this paper, a modified AC modulation method is proposed to improve the measurement accuracy of the electron energy distribution function. First, two small AC signals of different amplitudes are modulated on the measured DC scanning voltage of the Langmuir probe, and the probe current signal is measured. Then, the fundamental amplitudes of the two AC signals are corrected by spectral analysis and using the all-phase Fast Fourier Transformation - Fast Fourier Transformation(apFFT-FFT) amplitude correction algorithm. Then the accurate first-order derivative of the probe current to voltage (dI/dV) is obtained using the AC modulation derivative correction algorithm, and finally the electron energy distribution function is obtained by numerically differentiating dI/dV to obtain d2I/dV2. The method of this paper is compared with the traditional data processing method through experiments, and the results show the superiority and effectiveness of the method.

Aluminum dust concentration detection based on LSTM-Kalman filter

Industrial dust emissions present serious hazards, including respiratory issues and explosion risks. Traditional dust concentration detection methods are often compromised by environmental factors. This study introduces a novel FFT-KF-LSTM-NET model to predict high-concentration aluminum powder levels, utilizing 128 sets of electrostatic induction measurement data. By applying the Fast Fourier Transform (FFT) to eliminate low-frequency interference, integrating Long Short-Term Memory (LSTM) networks for advanced time series analysis, and using the Kalman Filter (KF) for rapid model convergence, this approach significantly enhances prediction accuracy. The model achieves an 82% improvement in Mean Squared Error (MSE), reducing it to 0.1336, outperforming traditional methods. Furthermore, by modeling the voltage signal generated by charged dust particles in the electrostatic induction sensor's sensing area and using the first derivative of the voltage signal as a learning feature, the model's prediction speed is increased from 1.5 s-2 s–0.5 s, with improved anti-interference capabilities. These advancements position the FFT-KF-LSTM-NET model as a crucial tool for real-time, reliable dust concentration detection in industrial environments.

Low Thermal Resistance of Diamond‐AlGaN Interfaces Achieved Using Carbide Interlayers

AbstractThis study investigates thermal transport across nanocrystalline diamond/AlGaN (aluminum gallium nitride) interfaces, crucial for enhancing thermal management in AlGaN‐based electronic devices. Chemical vapor deposition growth of diamond directly on AlGaN resulted in a disordered interface with a high thermal boundary resistance (TBR) of 20.6 m2‐KGW−1. Sputtered carbide interlayers of boron carbide (B4C), silicon carbide (SiC), and a mixture of boron carbide and silicon carbide (B4C/SiC) are employed to reduce thermal boundary resistance in diamond/AlGaN interfaces. The carbide interlayers resulted in record‐low thermal boundary resistance values of 3.4 and 3.7 m2‐KGW−1 for Al0.65Ga0.35N samples with B4C and SiC interlayers, respectively. STEM imaging of the interface reveals interlayer thicknesses between 1.7 and 2.5 nm, with an amorphous structure. Additionally, Fast‐Fourier Transform (FFT) characterization of sections of the STEM images displayed sharp crystalline fringes in the AlGaN layer, confirming it is properly protected from damage from hydrogen plasma during the diamond growth. In order to accurately measure the thermal boundary resistance we develop a hybrid technique, combining time‐domain thermoreflectance and steady‐state thermoreflectance fitting, offering superior sensitivity to buried thermal resistances. The findings underscore the efficacy of interlayer engineering in enhancing thermal transport and demonstrate the importance of innovative measurement techniques in accurately characterizing complex thermal interfaces. This study provides a foundation for future research in improving thermal properties of semiconductor devices through interface engineering and advanced measurement methodologies.

A novel machine learning framework for impact force prediction of foam-filled multi-layer lattice composite structures

Numerical simulations can provide valuable insights for the optimization of design and operational management; however, they are often impractical and computationally intensive. Machine learning methods are appealing to these problems due to their sufficient efficiency and accuracy. In this study, a novel framework for predicting the impact responses of foam-filled multi-layer lattice composite structures (FMLCSs) was proposed by combining the accurate finite element (FE) analyses, surrogate models, fast Fourier transform (FFT) method, and inverse FFT (IFFT) method. Firstly, reliable FM models were established to simulate the crashworthiness of the five FMLCSs under impact loading, including an analysis of energy transformation. Subsequently, surrogate models, namely radial basis function (RBF), polynomial response surface (PRS), Kriging (KRG), and back propagation neural network (BPNN), combined with methods of FFT and IFFT, were employed to predict the impact force-time series of the FMLCSs. More than 1000 frequency points were employed for each type of FMLCS, and all the R-square (R2) values of the established surrogate models exceeded 0.95, indicating that the proposed framework accurately predicted the impact duration and impact responses in the frequency domain. In addition, parameter sensitivity analysis revealed that a high peak impact force was accompanied by a short impact duration. Moreover, increasing the lattice-web height resulted in a significant increase in the impact duration.

Feature Vector Effectiveness Evaluation for Pattern Selection in Computational Lithography

Pattern selection is crucial for optimizing the calibration process of optical proximity correction (OPC) models in computational lithography. However, it remains a challenge to achieve a balance between representative coverage and computational efficiency. This work presents a comprehensive evaluation of the feature vectors’ (FVs’) effectiveness in pattern selection for OPC model calibration, leveraging key performance indicators (KPIs) based on Kullback–Leibler divergence and distance ranking. Through the construction of autoencoder-based FVs and fast Fourier transform (FFT)-based FVs, we compare their efficacy in capturing critical pattern features. Validation experimental results indicate that autoencoder-based FVs, particularly augmented with the lithography domain knowledge, outperform FFT-based counterparts in identifying anomalies and enhancing lithography model performance. These results also underscore the importance of adaptive pattern representation methods in calibrating the OPC model with evolving complexities.

Identification of Disc Cutter Wear via Operation Parameters Combined With Vibration Data: A Case Study

ABSTRACTThis paper proposed an approach to estimate disc cutter wear utilizing a combination of multiple operational parameters and vibration data collected during shield tunneling operations. The incorporation of vibration signals, notably those originating from acceleration sensors mounted on the back plate of the soil chamber, has markedly enhanced the accuracy of the model. Time‐frequency domain features were extracted through analysis methods such as Fast Fourier Transform (FFT), Short‐Time Fourier Transform (STFT), and Continuous Wavelet Transform (CWT). A predictive model utilizing vibration and shield operation parameters was developed using the XGBoost algorithm, and a deep GoogLeNet Convolutional Neural Network (CNN) was trained on time‐frequency graphs from the CWT. In addition, this study also investigated the impact of signal duration on wavelet image information and model accuracy. In the Huang‐Shang Intercity Railway Project, the approach effectively assessed disc cutter wear during tunneling operations and dynamically optimized the operational parameters of the shield tunnel machine through predictive analysis.