RESUMEN
In this paper, an enhanced approach for sound localization is proposed, which fuses automatic extraction of array signal characteristic frequencies and adaptive weighting. The method refines the autoregressive power spectral estimation algorithm and improves density-based spatial clustering of applications with noise algorithm for characteristic frequency extraction. Adaptive weighting technique is introduced to alleviate the problem of frequency mismatch in the localization process. The initial weight of narrowband signals is calculated and normalized using the frequency domain amplitude integration of narrowband signals, followed by adaptive threshold correction to eliminate invalid narrowband signal weights. The adaptive weight vector improves the localization method's accuracy and interference suppression. The effectiveness and universality of the proposed method are demonstrated with test data from dry transformers and pumps, and its applicability is shown to extend to various spatial spectrum estimation algorithms and deep learning-based sound source localization techniques.
RESUMEN
This paper presents an acoustic imaging localization system designed to pinpoint common defects in dry-type transformers by analyzing the unique sounds they produce during operation. The system includes an optimized microphone array and an improved multiple signal classification algorithm. Sound signal characteristics of typical defects, such as foreign object intrusion, screw loosening, and partial discharge, are investigated. A 64-element, 8-arm spiral microphone array is designed using a particle swarm optimization algorithm. The multiple signal classification algorithm enhances acoustic imaging quality in field environments by transforming the input from time-domain to preprocessed frequency-domain signals. The power spectra of subarray and main array are combined, forming the optimization algorithm's output. Experimental results demonstrate the system's effectiveness and accuracy.
Asunto(s)
Acústica , Sonido , Diseño de Equipo , Algoritmos , Diagnóstico por ImagenRESUMEN
Self-healing materials can promote the sustainable reuse of resources. Poly (urea-formaldehyde) (PUF) microcapsules can be incorporated into dielectric materials for self-healing. However, the mechanical properties of PUF microcapsules need to be improved due to insufficient hardness. In this paper, PUF models incorporated with nano-SiO2 of different filler concentrations (0, 2.6, 3.7, 5.3, 6.7, 7.9 wt.%) were designed. The density, the fractional free volume, and the mechanical properties of the PUF-SiO2 models were analyzed at an atomic level based on molecular dynamics simulation. The interfacial interaction model of PUF on the SiO2 surface was also constructed to further investigate the interaction mechanisms. The results showed that the incorporation of nano-SiO2 had a significant effect on the mechanical properties of PUF. Density increased, fractional free volume decreased, and mechanical properties of the PUF materials were gradually enhanced with the increase of nano-SiO2 concentration. This trend was also confirmed by experimental tests. By analyzing the internal mechanism of the PUF-SiO2 interfacial interaction, it was found that hydrogen bonds play a major role in the interaction between PUF and nano-SiO2. Moreover, hydrogen bonds can be formed between the polar atoms of the PUF chain and the hydroxyl groups (-OH) as well as O atoms on the surface of SiO2. Hydrogen bonds interactions are involved in adsorption of PUF chains on the SiO2 surface, reducing the distance between PUF chains and making the system denser, thus enhancing the mechanical properties of PUF materials.
RESUMEN
This paper investigates the morphology, thermal, and electrical properties of LDPE (low-density polyethylene)-based nanocomposites after thermal aging. The FTIR (Fourier transform infrared spectroscopy) spectra results show that thermo-oxidative reactions occur in neat LDPE and LDPE/SiO2 nanocomposites when the aging time is 35 days and in LDPE/MgO nanocomposites when the aging time is 77 days. Specifically, LDPE/MgO nanocomposites delay the appearance of thermo-oxidative reactions, showing anti-thermal aging ability. Furthermore, nanocomposites present lower onset degradation temperature than neat LDPE, showing better thermal stabilization. With regard to the electrical properties, nanocomposites maintain the ability to suppress space charge accumulation after thermal aging. Additionally, in comparison with SiO2 nanocomposites and neat LDPE, the permittivity of LDPE/MgO nanocomposites changes slightly after thermal aging. It is concluded that LDPE/MgO nanocomposites have better insulation properties than neat LDPE after thermal aging, which may be caused by the interface introduced by the nanoparticles.