Search Results (10)

Machining efficiency and stability are crucial for achieving high-quality manufacturing outcomes. One of the primary challenges in machining is the suppression of chatter, which negatively impacts surface finish, tool longevity, and overall process reliability. This study proposes a machine learning-based approach to optimize feed rate in machining operations by integrating a genetic algorithm (GA)-calibrated cutting force model with vibration analysis. A theoretical cutting force dataset is generated under varying machining conditions, followed by frequency-domain analysis using Fast Fourier Transform (FFT) to identify feed rates that minimize chatter. These optimal feed rates are then used to train an Extreme Gradient Boosting (XGBoost) regression model, with Bayesian optimization employed for hyperparameter tuning. The trained model achieves an R² score of 0.7887, indicating strong prediction accuracy. To verify the model’s effectiveness, robotic milling experiments were conducted using a UR10e manipulator. Surface quality evaluations showed that the model-predicted feed rates consistently resulted in better surface finish and reduced chatter effects compared to conventional settings. These findings validate the model’s ability to enhance machining performance and demonstrate the practical value of integrating simulated dynamics and machine learning for data-driven parameter optimization in robotic systems. Full article

Online monitoring of partial discharge (PD) is a complex task traditionally requiring specialized expertise. However, recent advancements in signal processing and machine learning have facilitated the development of automated tools to identify and categorize PD patterns, aiding those without extensive experience. This paper aims to identify PD types and estimate the density distribution of frequency characteristics for three PD types, internal PD, surface PD, and corona PD, using verified PD data. The proposed method employs a findpeaks algorithm based on Fast Fourier Transform (FFT) to extract frequency key features, denoted as f₁ and f₂, from the frequency spectrum. These features are used to estimate model parameters for each PD type, enabling the representation of their frequency density distributions in a 2D map (f₁, f₂) via Gaussian Mixture Models (GMMs). The optimal number of Gaussian components, determined as five using the Bayesian Information Criterion (BIC), ensures accurate modeling. For PD identification, log-likelihood and softmax functions are applied, achieving an evaluation accuracy of 96.68%. The model also demonstrates robust performance in identifying unknown PD data, with accuracy ranging from 78.10% to 95.11%. This approach enhances the distinction between PD types based on their frequency characteristics, providing a reliable tool for PD signal analysis and identification. Full article

The harsh working environment of hydrogen refueling stations often causes equipment failure and is vulnerable to mechanical noise during monitoring. This limits the accuracy of equipment monitoring, ultimately decreasing efficiency. To address this issue, this paper presents a motor bearing vibration signal diagnosis method that employs a Bayesian optimization (BOA) residual neural network (ResNet). The industrial noise signal of the hydrogenation station is simulated and then combined with the motor bearing signal. The resulting one-dimensional bearing signal is processed and transformed into a two-dimensional signal using Fast Fourier Transform (FFT). Afterwards, the signal is segmented using the sliding window translation method to enhance the data volume. After comparing signal feature extraction and classification results from various convolutional neural network models, ResNet18 yields the best classification accuracy, achieving a training accuracy of 89.50% with the shortest computation time. Afterwards, the hyperparameters of ResNet18 such as InitialLearnRate, Momentum, and L2Regularization Parameter are optimized using the Bayesian optimization algorithm. The experiment findings demonstrate a diagnostic accuracy of 99.31% for the original signal model, while the accuracy for the bearing signal, with simulated industrial noise from the hydrogenation station, can reach over 92%. Full article

High-speed maneuvering target inverse synthetic aperture radar (ISAR) imaging is always a hot topic in signal processing. High-speed maneuvering targets often have high-order maneuvering characteristics, such as translational and rotational characteristics, which destroy the signal structure of stationary targets and make basic imaging processing methods such as range-Doppler (RD) algorithm no longer suitable. In this paper, a high-resolution imaging method for high-speed maneuvering targets is proposed, which uses the fast sparse Bayesian learning (SBL) algorithm and the minimum entropy algorithm for ISAR high-resolution imaging and motion parameter estimation, respectively. Because SBL makes full use of the characteristics of the target and the environment, it can obtain an ISAR high-resolution image of the maneuvering target. However, the high computational complexity caused by matrix inversion and some matrix operations in SBL iteration limit the practical application of SBL in ISAR imaging. In view of the special structure of the matrix required to be inverted, we propose a fast SBL algorithm, which uses a new decomposition method to obtain the decomposition formula of the inverse matrix. Based on the decomposition factors, the multiplication operation involving the inverse matrix can be quickly calculated using fast Fourier transform (FFT), which greatly improves the computational efficiency. Image entropy represents the sharpness and focusing degree of an image, and so the minimum entropy algorithm can estimate the motion parameters of maneuvering targets more accurately. We combine the minimum entropy algorithm with the fast SBL algorithm to realize phase error correction and high-resolution imaging, which has better noise sensitivity and can obtain the best focusing degree image. Finally, simulation results prove the effectiveness of this algorithm. Full article

In a distributed frequency-modulated continuous waveform (FMCW) radar system, the echo data collected are not continuous in the azimuth direction, so the imaging effect of the traditional range-Doppler (RD) algorithm is poor. Sparse Bayesian learning (SBL) is an optimization algorithm based on Bayesian theory that has been successfully applied to high-resolution radar imaging because of its strong robustness and high accuracy. However, SBL is highly computationally complex. Fortunately, with FMCW radar echo data, most of the time-consuming SBL operations involve a Toeplitz-block Toeplitz (TBT) matrix. In this article, based on this advantage, we propose a fast SBL algorithm that can be used to obtain high-angular-resolution images, in which the inverse of the TBT matrix can be transposed as the sum of the products of the block lower triangular Toeplitz matrix and the block circulant matrix by using a new decomposition method, and some of the matrix multiplications can be quickly computed using the fast Fourier transform (FFT), decreasing the computation time by several orders of magnitude. Finally, simulations and experiments were used to ensure the effectiveness of the proposed algorithm. Full article

Many engineering structures are made of metal composite materials. External load information is a key issue for the design and condition monitoring of the structures. Due to the limitation of measurement technology and the external environment, it is difficult to directly measure dynamic loads on structures in many circumstances. This paper focuses on evaluating the external load applied on a structure with unknown dynamic properties. We proposed a novel dynamic load identification method that is based on the Bayesian principle coupled with the extended Kalman filter method. Firstly, the modal parameters are identified under ambient excitation using the Bayesian fast Fourier transform method (FFT). The posterior probability density function (PDF) and covariance of the modal parameters are obtained by the Fourier transform of the response data, and then the modal parameters of the structure are obtained based on unconstrained optimization. Next, the extended Kalman filter method in the modal space is used to update the modal parameters and identify the time-domain information of dynamic loads. The accuracy of the proposed theory was evaluated experimentally using a Bernoulli−Euler beam. The results showed that the method is feasible and efficient. Full article

Although widely used in various fields due to its powerful capability of signal processing, empirical mode decomposition has to decompose signals separately, which limits its application for multivariate data such as the structural monitoring data recorded by multiple sensors. In order to avoid this shortcoming, a multivariate extension of empirical mode decomposition is proposed to deal with the multidimensional signals synchronously by employing a real-valued projection on hyperspheres. This study presents a hybrid modal identification method combining the multivariate empirical mode decomposition with stochastic subspace identification and fast Bayesian FFT methods to more conveniently and accurately identify structural dynamic parameters from multi-sensor vibration measurements. Deployed as a preprocessing tool, the multivariate signals are decomposed into several aligned intrinsic mode functions, which contain only a dominant component in the frequency domain. Then, the modal parameters can be identified by advanced fast Bayesian FFT and stochastic subspace identification directly. The combined method is first validated by a numerical illustration of a frame structure and then is applied in a shaking table test and a full-scale measurement under nonstationary earthquake excitation. Compared with the finite element method, the peak–pick, the half-power bandwidth methods, and Hilbert–Huang transform method, the results show that this hybrid method is more robust and reliable in the modal parameters identification. The main contribution of this paper is to develop a more effective integrated approach for accurate modal identification with the output-only multi-dimensional nonstationary signal. Full article

For millimeter-wave (MMW) imaging security systems, the image resolution promisingly determines the performance of suspicious target detection and recognition. Conventional synthetic aperture radar (SAR) imaging algorithms only provide limited resolution in active MMW imaging, which is limited by the system. In terms of enhancing the resolution of a region of interest (ROI) image containing suspicious targets, super-resolution (SR) imaging is adopted via Bayesian compressive sensing (BCS) implemented by fast Fourier transform (FFT). The spatial sparsity of MMW ROI images is well exploited with BCS to achieve resolution enhancement without computational cost. Both simulated and measured experiments confirm that the proposed scheme effectively improves the resolution of ROI images. Full article

Passive sonar systems are used to detect the acoustic signals that are radiated from marine objects (e.g., surface ships, submarines, etc.), and an accurate estimation of the frequency components is crucial to the target detection. In this paper, we introduce sparse Bayesian learning (SBL) for the frequency analysis after the corresponding linear system is established. Many algorithms, such as fast Fourier transform (FFT), estimate signal parameters via rotational invariance techniques (ESPRIT), and multiple signal classification (RMUSIC) has been proposed for frequency detection. However, these algorithms have limitations of low estimation resolution by insufficient signal length (FFT), required knowledge of the signal frequency component number, and performance degradation at low signal to noise ratio (ESPRIT and RMUSIC). The SBL, which reconstructs a sparse solution from the linear system using the Bayesian framework, has an advantage in frequency detection owing to high resolution from the solution sparsity. Furthermore, in order to improve the robustness of the SBL-based frequency analysis, we exploit multiple measurements over time and space domains that share common frequency components. We compare the estimation results from FFT, ESPRIT, RMUSIC, and SBL using synthetic data, which displays the superior performance of the SBL that has lower estimation errors with a higher recovery ratio. We also apply the SBL to the in-situ data with other schemes and the frequency components from the SBL are revealed as the most effective. In particular, the SBL estimation is remarkably enhanced by the multiple measurements from both space and time domains owing to remaining consistent signal frequency components while diminishing random noise frequency components. Full article

This paper presents a novel Inverse Synthetic Aperture Radar Imaging (ISAR) algorithm based on a new sparse prior, known as the logarithmic Laplacian prior. The newly proposed logarithmic Laplacian prior has a narrower main lobe with higher tail values than the Laplacian prior, which helps to achieve performance improvement on sparse representation. The logarithmic Laplacian prior is used for ISAR imaging within the Bayesian framework to achieve better focused radar image. In the proposed method of ISAR imaging, the phase errors are jointly estimated based on the minimum entropy criterion to accomplish autofocusing. The maximum a posterior (MAP) estimation and the maximum likelihood estimation (MLE) are utilized to estimate the model parameters to avoid manually tuning process. Additionally, the fast Fourier Transform (FFT) and Hadamard product are used to minimize the required computational efficiency. Experimental results based on both simulated and measured data validate that the proposed algorithm outperforms the traditional sparse ISAR imaging algorithms in terms of resolution improvement and noise suppression. Full article