Online partial discharge (PD) monitoring is an im-portant means to detect insulation deterioration. However, it is dif-ficult to extract PD signal due to various interference in the field. Noisy PD signal is used to judge the status of insulation, which would affect the conclusion, therefore de-noising PD signal is a ma-jor task in online PD monitoring. Common methods for PD de-noising include the empirical mode decomposition (EMD) and wavelet transform; however, the de-noising results are highly de-pendent on the modal aliasing, the selection of mother wavelets and decomposition levels. This paper proposes a method to solve these problems. This method uses traditionally singular value transform (SVD) to reconstruct narrow-band interference and re-move it. Next, the empirical wavelet transform (EWT) is carried out for the PD signal which has residual white noise. Then, the noisy signal is decomposed into several modes corresponding to each spectrum segment. 3σ principle is used to de-noise the modes with large kurtosis, and the modes are combined into a reference signal. The start-end positions of PD signal are then obtained from the reference signal. Finally, the PD signal is obtained by time-do-main de-noising. The results from both simulated and actual field detection signals show the excellent performance of this method.

Due to complex disturbance environment, partial discharge (PD) monitor signal denoising is a necessary step to judge the state of equipment insulation. In the denoising process, it is difficult to suppress the white noise due to its wide frequency-domain distribution. To overcome these difficulties, most denoising algorithms need to artificially adjust some threshold parameters or select the number of decomposition layers according to the measured PD signals, which leads to the confusion of uncertainty, and the denoising performance depends on the parameter setting experience. Therefore, the denoising effect cannot be guaranteed in general. This article proposes a method without human intervention in choosing the threshold parameters or the decomposition layer numbers. First, the decomposition layers are directly determined from the spectral kurtogram of PD signal to obtain the reference signal. Second, the reference signal is denoised by 3σ threshold. At the start point obtained from the denoised signal and the end point obtained from the reference signal envelope, the reference signal is segmented in the time domain to obtain the final denoising PD signal. The denoising results of both the simulated and field-measured PD signals show that the proposed method can effectively remove the white noise and recover PD signal more accurately.

Foreign object debris (FOD) on airport runways can cause serious accidents and huge economic losses. FOD detection systems based on millimeter-wave (MMW) radar sensors have the advantages of higher range resolution and lower power consumption. However, it is difficult for traditional FOD detection methods to detect and distinguish weak signals of targets from strong ground clutter. To solve this problem, this paper proposes a new FOD detection approach based on optimized variational mode decomposition (VMD) and support vector data description (SVDD). This approach utilizes SVDD as a classifier to distinguish FOD signals from clutter signals. More importantly, the VMD optimized by whale optimization algorithm (WOA) is used to improve the accuracy and stability of the classifier. The results from both the simulation and field case show the excellent FOD detection performance of the proposed VMD-SVDD method.

Partial discharge (PD) is a primary factor leading to the deterioration of insulation in electrical equipment. However, it is hard for traditional methods to precisely extract PD signals in increasingly complex engineering environments. This paper proposes a new PD signal denoising method combining Aquila Optimizer–Variational Mode Decomposition (AO-VMD) and K-Singular Value Decomposition (K-SVD) algorithms. Firstly, the AO algorithm optimizes critical parameters of the VMD algorithm. For the PD signal overwhelmed by noise, the AO-VMD algorithm can decompose it and reconstruct it by using kurtosis. In this process, the majority of the noise is removed, and the characteristics of the original signal are shown. Subsequently, the K-SVD algorithm performs sparse decomposition on the signal after OA-VMD, constructs a learned dictionary, and captures the characteristics of the signal for continuous learning and updating. After the dictionary learning is completed, the best matching atoms from the dictionary are selected to precisely reconstruct the original noiseless signal. Finally, the proposed method is compared with three traditional algorithms, Adaptive Ensemble Empirical Mode Decomposition (AEEMD), SVD-VMD, and the Adaptive Wavelet Multilevel Soft Threshold algorithm, on the simulated signal and the actual engineering signal. The results both demonstrate that the algorithm proposed by this paper has superior noise reduction and signal extraction performance.

The main challenge of foreign objects debris (FOD) monitoring based on millimeter-wave (MMW) radar is the interference of strong ground clutter. The clutter-map constant false alarm rate (CM-CFAR) method is commonly utilized for FOD monitoring, but detection results are usually accompanied by many false alarms in the complex ground clutter environment. To solve this problem, this work proposed a false alarm elimination algorithm. Specifically, the CM-CFAR method is firstly used to separate the suspicious targets (including FOD and false alarms) from the background clutter. Then, the Duffing detection system is constructed to detect the sinusoidal frequencies associated with suspicious targets. Finally, according to the detection results of Duffing system, suspicious targets can be classified as FOD and false alarms. In addition, in order to detect the sinusoidal frequencies efficiently, a quantitative detection method based on Poincare section points analysis is proposed to identify the state transition of Duffing oscillator. The results from both simulation and field case show the excellent false alarm elimination performance of the proposed algorithm.

In recent years, optical fiber distributed acoustic sensing (DAS) technology has gained prominence in vehicle detection due to its low deployment cost, high spatial resolution, and ultra-long-distance measurement. However, various adverse factors, including strong environmental noise, DAS fading noise, lightweight vehicles, low speed, and poor coupling between the fiber cable and the ground, cause vehicle signals to weaken and become discontinuous. These issues limit the measurement sensitivity and reliability of DAS systems, making it challenging to accurately extract vehicle trajectories using existing algorithms. This paper introduces a Multi-Scale Context Pix2pix (MCPix2pix) generative network model embedded into the DAS signal measurement pipeline to enhance the system’s ability to detect and reconstruct weak and discontinuous quasi-static vehicle signals. Adversarial learning between the generator and discriminator, effectively repairs fragmented vehicle trajectories. Furthermore, multi-scale dilated convolutions and corresponding loss functions expand the receptive field and improve the capture of vehicle features across multiple spatial and temporal scales. Field experiments conducted with vehicles on campus demonstrated that applying the MCPix2pix model to DAS quasi-static signals can reliably extract discontinuous trajectories of low-speed, lightweight vehicles, thereby extending the functional sensitivity range of DAS and improving its effectiveness as a distributed sensing instrument in real-world applications.