Search Results (82)

Rolling-element bearings are a leading cause of unplanned downtime in continuous-process manufacturing, and the migration toward Industry 4.0 condition-based maintenance (CBM) has intensified the need for diagnostic methods evaluated on real in-service assets. This paper reports an exploratory, longitudinal single-asset industrial field demonstration of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based Hilbert spectral analysis for rolling-element bearing condition monitoring. An in-service bearing on a critical production machine was monitored over eight measurements spanning approximately four months and analyzed with the Hilbert–Huang Transform, using CEEMDAN in place of the classical Empirical Mode Decomposition to suppress mode mixing. The Hilbert spectra tracked the evolution of the bearing’s vibration signature as a growing concentration of vibration amplitude in a stable band of the 0–400 Hz analysis window (approximately 280–380 Hz); because the analysis characterizes the distribution of amplitude within the band rather than resolving discrete defect lines, this is reported as band-amplitude trending, and the band is treated as compatible with bearing-related excitation rather than attributed to a specific kinematic fault frequency. At the final pre-replacement measurement (M8), a bounded consistency cross-check against a conventional single-sided Fast Fourier Transform (FFT) amplitude spectrum computed from the same exported waveform recovered co-located dominant content. This establishes cross-method consistency for the measurement examined, but does not independently validate diagnostic correctness or the full campaign trend. Interpretability of the representation is argued qualitatively and remains to be tested. The contribution is therefore the documented field application and its explicit consistency cross-check procedure; applicability beyond this asset and its operating conditions requires multi-asset evaluation. Full article

Gearbox fault diagnosis under noisy operating conditions remains a critical yet unsolved challenge for industrial condition monitoring. The primary challenge originates from a fundamental conflict: fault signatures are present within certain frequency ranges, yet standard deep learning models process raw vibration signals without recognizing which frequencies are relevant. Moreover, noise affects the entire spectrum uniformly. Existing transformer-based methods for vibration analysis treat time steps as tokens and therefore fail to capture cross-sensor dependencies, while conventional denoising approaches apply fixed filters that cannot adapt to the varying spectral characteristics of different fault types and noise levels. We propose the Adaptive Frequency-Aware Inverted Transformer (AF-iTransformer), a lightweight transformer framework that lets the model learn which frequencies to attend to on a per-sample basis. In particular, we propose a learnable spectral filter that transforms the input signal to the frequency domain via FFT. Then it predicts a soft frequency mask conditioned on signal statistics and applies it before reconstructing the filtered signal through iFFT, allowing the model to suppress noise bands while preserving fault-relevant spectral content dynamically. After adaptive filtering, the architecture employs channel-level tokenization to uniformly represent heterogeneous channels as input tokens, relying on cross-channel attention to automatically learn their distinct contributions to fault diagnosis. Feature-wise linear modulation is introduced to inject signal-level statistics at every encoder layer. Furthermore, the framework utilizes residual attention propagation to stabilize deep training, and an auxiliary spectrum prediction head provides spectral regularization during training. On the UConn Gearbox dataset with nine fault categories, AF-iTransformer achieves 99.63% accuracy on clean data and maintains 95.1% at 0 dB signal-to-noise ratio, substantially outperforming all baselines under noisy conditions. On the SEU gearbox dataset with five fault categories, AF-iTransformer achieves 99.74% clean accuracy. On 2 GB edge GPUs, AF-iTransformer achieves a per-window inference latency of 7.3–13 ms with peak memory below 17 MB, and 1.4–4.7 ms on modern CPUs, confirming its viability for real-time industrial deployment. Full article

Accurate identification of electromagnetically induced stator vibration frequency components is essential for the online condition monitoring of hydro-generators, particularly for assessing the dynamic state of the stator core under normal operating conditions. In engineering practice, the fast Fourier transform (FFT) is widely used for vibration spectrum analysis; however, because the measured vibration response is simultaneously affected by electromagnetic excitation, mechanical rotation, hydraulic disturbance, and external harmonic interference, FFT-based spectra often contain multiple frequency components whose structural relevance is difficult to determine directly. To address this issue, this paper proposes a coupled continuous wavelet transform and power spectral density transmissibility (CWT-PSDT) method for identifying key vibration frequency components with stable time-frequency energy and inter-sensor transmissibility in hydro-generator stator vibration signals. In the proposed framework, the analytic Morlet wavelet is first employed to localize dominant energy bands in the time-frequency domain, and PSDT is then used to screen frequency components with relatively stable inter-sensor transmissibility characteristics, thereby reducing the ambiguity caused by excitation-dominated spectral components. A clamped-clamped beam model is first used for numerical validation, and the maximum identification error of the first five natural frequencies is 4.22%. Experiments on a Francis turbine-generator test rig under five operating conditions further show that the proposed method can distinguish the mechanical rotational component near 10.3 Hz from the electromagnetic-related component near 50.8 Hz, while retaining higher-order electromagnetic-related components around 150 Hz and 250 Hz. The results demonstrate that the proposed CWT-PSDT method provides a physically interpretable and data-efficient approach for extracting stator-core-related spectral features, and offers a theoretical basis for spectrum-based online monitoring and future abnormal-condition comparison of hydro-generator stator responses. Full article

Generator-side transient overvoltage may occur in DC 600 V AC–DC–AC railway power supply systems because switching-induced common-mode voltage generated by the front-end rectifier can propagate in reverse through cable distributed parameters, grounding impedance, and generator parasitic capacitance. Although AZSVPWM has been widely studied as a common-mode voltage reduction technique, its application to the suppression of generator-side reverse transient overvoltage in railway DC 600 V power supply systems has not been sufficiently investigated. In this paper, AZSVPWM is applied to the front-end active rectifier as a source-side suppression strategy. A high-frequency electromagnetic transient model is developed by considering the generator equivalent impedance, cable distributed parameters, grounding path, and generator winding-to-ground parasitic capacitance. The model is validated by comparing simulated and measured generator terminal voltages under AZSVPWM operation. Based on this model, the common-mode voltage excitation mechanism, reverse propagation path, and overvoltage suppression effect of AZSVPWM are analyzed. The results show that, compared with conventional SVPWM under identical active-rectifier conditions, AZSVPWM reduces the representative peak transient voltage at the generator terminals from 751.37 V to 557.71 V under the 8 m cable condition, corresponding to a reduction of approximately 25.77%. In addition, AZSVPWM-based active rectification improves the low-frequency voltage quality compared with conventional thyristor rectification, and the THD is estimated to decrease from approximately 14.8% to 0.8% based on the FFT spectrum. Parametric analysis further shows that AZSVPWM maintains stable suppression performance for cable lengths of 2–15 m and generator parasitic capacitances of 5–20 nF, with the maximum peak-voltage deviation caused by parasitic capacitance variation being approximately 1.15%. These results indicate that AZSVPWM provides a practical and robust source-side suppression strategy for generator-side transient overvoltage in railway DC 600 V power supply systems. Full article

In response to the current problems of asynchronous sampling, spectrum leakage, and the fence effect, this paper proposes a combination of FFT and wavelet transform to accurately analyze the steady-state and transient components in the signal, improving detection accuracy. Firstly, we use Newton interpolation to quasi-synchronize the original sampling sequence, achieving consistency between the sampling period and the actual period. Secondly, the modulus maximum method is used on the original signal to detect the presence of transient disturbances. The results indicate that quasi-synchronization can accurately correct the collected signals with high restoration and synchronization accuracy. The improved windowed interpolation FFT achieves a detection accuracy of 0.3% for steady-state harmonics. The wavelet divides the frequency band and uses modulus maxima to detect signal singularity, accurately extracting transient disturbance signals with an accuracy of up to 5%. Similarly, for complex power signals, the analysis of hybrid algorithms also meets practical engineering needs. Full article

Resource-constrained electromagnetic interference (EMI) management requires a frequency-domain feedback path, while FFT-based full-spectrum processing introduces redundant computation, storage, and data movement for decision tasks. This paper proposes a limit-aware sparse frequency-domain decision engine for internal EMI risk feedback. The engine redefines EMI analysis from spectrum reconstruction to selective exceedance verification and uses randomized spectral reordering, flat-window bucket aggregation, and folded sampling to compress the length-N spectral search into bucket-level observations. Then, by comparing bucket-level amplitude envelopes with local limit envelopes, the method excludes risk-negative buckets, and only uncertain buckets are further refined through phase localization and sequential verification. Degradation experiments involving continuous background uplift, main-harmonic sidebands, and parasitic resonance clusters clarify the applicability boundary of the proposed method, and measured GaN power-converter spectra acquired through an in situ EMI sensing chain remain inside the empirical usable region. RTL evaluation at 100 MHz shows that the proposed design achieves an average decision latency of 6.031 ms. Compared with two FFT baseline implementations, it reduces BRAM usage by 95.17% and 97.59%, dynamic power by 54.0% and 83.0%, and per-decision dynamic energy by 46.3× and 33.3×, respectively. The results show that the proposed decision engine reduces hardware overhead for frequency-domain EMI risk feedback in resource-constrained systems. Full article

GNSS signals are extremely weak at the Earth’s surface and are highly vulnerable to in-band interference, particularly high-dynamic linear frequency-modulated (LFM) jamming, which may lead to receiver loss of lock. Existing anti-jamming techniques struggle to balance real-time constraints with high-precision frequency estimation. This paper proposes a Newton-iteration-enhanced three-spectral-line RIFE algorithm implemented on a heterogeneous FPGA platform (Zynq-7000 SoC). The method performs coarse frequency estimation using the three-spectral-line RIFE to mitigate FFT fence effects, followed by Newton-based quadratic refinement, enabling high estimation accuracy with reduced FFT size. A fast–slow loop architecture is adopted, where the FPGA (PL) performs real-time interference suppression and the ARM (PS) handles system control and parameter updates. Experimental results show that, under static interference, the proposed method achieves a 10.9 dB improvement over direct estimation algorithms. Under chirp interference, it significantly outperforms both direct estimation and conventional iterative methods. In GNSS closed-loop tests, the proposed approach extends the anti-jamming margin to 82 dB J/S. Overall, the proposed method effectively balances estimation accuracy and processing latency, providing a practical solution for GNSS anti-jamming in high-dynamic environments. Full article

Co-channel interference (CCI) remains a critical factor affecting link reliability in narrowband wireless systems, especially in scenarios with intensive frequency reuse, overlapping coverage, and dense terminal access. Existing interference detection methods are either computationally simple but insufficiently sensitive to short-term spectral variations, or highly accurate but dependent on labeled data and nontrivial inference resources. To address this issue, this paper proposes a lightweight CCI detection method in the FFT domain based on spectrum-jump analysis. The proposed method does not rely on absolute power growth as the primary interference indicator. Instead, it tracks the temporal inconsistency of dominant spectral-bin indices across consecutive FFT frames and converts recurrent peak-bin migration into an interference decision through a short-window counting mechanism. The method is computationally efficient, interpretable, and suitable for real-time deployment without offline model training. SDR-based measurements are combined with controlled repeated experiments to assess detector performance under varying signal-to-noise ratio (SNR), interference-to-signal ratio (ISR), carrier-frequency offset (CFO), multi-peak ambiguity, and two-path Rayleigh fading conditions. On the measured SDR record, the proposed method captures all interference-positive windows after the marked onset, while the controlled SNR/ISR experiments yield an overall detection probability of 96.0% over 250 CCI trials with no false alarms over 250 normal trials. ROC and precision–recall analyses further show that the selected threshold lies within a broad validation plateau. The results also reveal clear applicability boundaries: when the CFO approaches zero, when the interference is very weak, or when multiple stationary peaks have nearly equal power, dominant-bin migration may be weak or ambiguous. Therefore, the proposed approach is a low-complexity online detector for CCI cases that induce observable FFT-bin instability, and it can also serve as a front-end trigger for more advanced interference analysis modules. Full article

This paper aims to present a non-destructive, optimized variational mode decomposition (VMD)-based ground-penetrating radar (GPR) method developed for identifying void defects in reinforced concrete (RC) structures. This study also presents an enhanced framework for defect detection in RC by integrating advanced spectrum analysis with deep learning techniques. A GPR investigation was conducted on an RC bridge deck with known structural defects to generate a representative dataset reflecting both intact and void-defective conditions. In addition to conventional spectral techniques such as fast Fourier transform (FFT), spectrogram, and scalogram, an optimized variational mode decomposition (VMD) method was implemented. The VMD approach decomposes GPR signals into intrinsic mode functions, enabling refined feature extraction beyond traditional spectral methods and allowing clear differentiation between intact and defective signals. The limited availability and quality of GPR small datasets have restricted the application of a functional 1D-CNN which generally requires at least several hundred datasets. To address this challenge, a data augmentation strategy is adopted. FFT-based features were successfully utilized to train a one-dimensional convolutional neural network (1D-CNN) for automated defect identification. The results demonstrate that both the advanced spectrum-based approach and the hybrid framework combining spectral analysis with deep learning significantly improve defect detection performance. Overall, the proposed methodology provides an effective and intelligent solution to support timely, data-driven decision-making for maintenance and safety assurance of bridge infrastructure. Full article

Background: The early detection of autism spectrum disorder (ASD) is imperative for enhancing long-term developmental outcomes. Nevertheless, conventional screening methods depend on time-consuming, expert-driven behavioral assessments and are characterized by limited scalability. Automated video-based analysis provides a noninvasive and objective approach for the extraction of behavioral biomarkers from naturalistic recordings. Methods: A modular multimodal framework was developed that integrates motion-based video analysis and facial feature extraction for the purpose of ASD versus typically developing (TD) classification. The system is capable of processing RGB videos, skeleton/stickman representations, and motion trajectory streams. A comprehensive set of kinematic features was extracted, encompassing joint trajectories, velocity and acceleration profiles, posture variability, movement smoothness, and bilateral asymmetry. The repetitive stereotypical behaviors exhibited by the subjects were characterized using frequency-domain analysis via FFT within the 0.3–7.0 Hz band. Facial expression features derived from normalized face crops and landmark-based morphological descriptors were integrated as complementary modalities. The feature-level fusion process was executed subsequent to z-score normalization, and the classification procedure was conducted using a Random Forest model with stratified 5-fold cross validation. The implementation of GPU acceleration was instrumental in facilitating near real-time inference. Results: The motion-based ComplexVideos pipeline demonstrated a cross-validated accuracy of 94.2 ± 2.1% with an area under the ROC curve (AUC) of 0.93. Skeleton-based KinectStickman inputs demonstrated moderate performance, with an accuracy range of 60–80%. In contrast, facial-only models exhibited an accuracy of approximately 60%. The integration of multiple modalities through feature fusion has been demonstrated to enhance the robustness of classification algorithms and mitigate the occurrence of false negative outcomes, thereby surpassing the performance of single-modality models. The mean inference time remained below one second per video frame under standard operating conditions. Conclusions: The experimental results demonstrate that the integration of multimodal cues, including motion and facial features, facilitates the development of effective and efficient video-based screening methods for autism spectrum disorder (ASD). The proposed framework is designed to offer a scalable, extensible, and computationally efficient solution that can support early screening in clinical and remote assessment settings. Full article

Under the combined effects of vibrations from train operations and wind loads, the dynamic response monitoring data of masonry partition walls in subway stations are often contaminated with high-frequency noise, which hinders the accurate identification of the structure’s true dynamic characteristics. To tackle this problem, this paper proposes employing a Butterworth low-pass filter to process the on-site monitoring data. The paper initially elaborates on the monitoring theory grounded in the pulsation method, followed by a detailed explanation of the rationale for selecting the Butterworth filter, as well as data processing techniques such as Fast Fourier Transform (FFT) and self-power spectrum analysis. By incorporating a field monitoring case from a subway station in Guangzhou, the paper compares and analyzes the acceleration time-history curves before and after filtering. Additionally, finite element analysis is performed to assess the mechanical response of the masonry wall under wind loads, train-induced vibrations, and their combined effects. The results demonstrate that after applying a 4th-order Butterworth low-pass filter with a 46 Hz cutoff frequency, the high-frequency noise in the data is effectively suppressed, thereby accentuating the main trend and low-frequency vibration characteristics of the signal. This provides a reliable data foundation for subsequent precise analysis of the dynamic response and fatigue performance of the masonry walls. Full article

This paper systematically reviews the key spectral information extraction methods in Brillouin microscopy, aiming to address the core challenge of accurately extracting material mechanical parameters from raw spectra. Based on technical principles, the methods are categorized into three types for elaboration: Spontaneous Brillouin Scattering (SpBS) is characterized by low signal-to-noise ratio (SNR) and strong background interference, and its processing relies on high-precision spectrometers and complex preprocessing procedures to mitigate noise and background effects; Stimulated Brillouin Scattering (SBS) operates on the mechanism of optical gain/loss, which achieves significantly improved data SNR and thereby enables more robust and accurate Lorentzian fitting for spectral analysis; Impulsive Stimulated Brillouin Scattering (ISBS) retrieves the frequency spectrum by inverting time-domain oscillating signals, and the core of its processing lies in super-resolution algorithms such as Fast Fourier Transform (FFT) and the Matrix Pencil Method, which are tailored to match its high-speed data acquisition capability. The paper further compares the advantages and disadvantages of various methods, outlines future development trends of intelligent processing technologies such as deep learning and multi-modal data fusion, and provides a clear guide for selecting the optimal data processing strategy in different application scenarios. Full article

The timely identification of rock fractures is crucial in deep subterranean engineering. However, it remains necessary to identify reliable warning indicators and establish effective warning levels. This study introduces the Acoustic Emission Transformer for FRActure Prediction (AET-FRAP) multi-input time series forecasting framework, which employs acoustic emission feature parameters. First, Empirical Mode Decomposition (EMD) combined with Fast Fourier Transform (FFT) is employed to identify and filter periodicities among diverse indicators and select input channels with enhanced informative value, with the aim of predicting cumulative energy. Thereafter, the one-dimensional sequence is transformed into a two-dimensional tensor based on its predominant period via spectral analysis. This is coupled with InceptionNeXt—an efficient multiscale convolution and amplitude spectrum-weighted aggregate—to enhance pattern identification across various timeframes. A secondary criterion is created based on the prediction sequence, employing cosine similarity and kurtosis to collaboratively identify abrupt changes. This transforms single-point threshold detection into robust sequence behavior pattern identification, indicating clearly quantifiable trigger criteria. AET-FRAP exhibits improvements in accuracy relative to long short-term memory (LSTM) on uniaxial compression test data, with R² approaching 1 and reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). It accurately delineates energy accumulation spikes in the pre-fracture period and provides advanced warning. The collaborative thresholds effectively reduce noise-induced false alarms, demonstrating significant stability and engineering significance. Full article

This study presents a comparative harmonic analysis of shaft voltage waveforms in large-scale steam turbine generators, emphasizing the influence of excitation system topology and generator design on spectral behavior. Using high-resolution Fast Fourier Transform (FFT) analysis of healthy-state data from five hydrogen-cooled turbo-generators (600–846 MW), this work identifies consistent harmonic patterns and their diagnostic value. Generators with brushless excitation systems exhibit dominant harmonics at 150 Hz (3rd), 250 Hz (5th), and 400 Hz (8th), whereas static-excited units show a 150 Hz (3rd) and 450 Hz (9th) pattern. These findings confirm that excitation architecture, rather than OEM design, governs the shaft voltage harmonic “fingerprint.” The persistent 150 Hz component across all machines serves as a stable indicator of generator condition. The results provide a practical reference for establishing harmonic-based baselines to enhance early fault detection and predictive-maintenance strategies in power station generators. This work contributes new comparative insights linking excitation topology to harmonic behavior, enabling improved condition monitoring across diverse generator fleets. This study establishes harmonic profiles defined as the amplitude, frequency, and relative proportion of key harmonic components in the shaft voltage spectrum obtained via FFT analysis to serve as spectral fingerprints representing the generator’s health condition. Full article

This article aims to improve the real-time monitoring accuracy of the loss rate for grain combine harvesters by optimizing the sensor-sensitive plate structure, thereby addressing the problem of low detection efficiency in existing equipment. Based on Kirchhoff’s thin plate theory, COMSOL 6.0 software was utilized to conduct modal analysis and single-grain impact tests on rectangular and circular sensing plates fabricated from three materials: stainless steel, aluminum alloy, and cupronickel. The circular stainless steel sensing plate was identified as the optimal structure, whose natural frequency and sensitivity significantly outperform those of traditional rectangular plates. By integrating a signal processing strategy based on FFT (Fast Fourier Transform) spectrum analysis (band-pass filtering: 1.0~3.0 kHz, voltage threshold: 3.5 V) and a high-level duration counting algorithm, the system effectively distinguishes between grains and impurities and resolves the counting errors caused by multi-grain impacts and secondary rebounds. Field experiments demonstrate that the developed sensor exhibits strong anti-interference ability and high measurement accuracy, providing reliable technical support for reducing harvesting losses. Full article