Feiyan Zhou^1,*, Hui Yin¹, Chen Luo², Haixin Tong², Kun Yu², Zewen Li², Xiangjun Zeng²

Energy Engineering, Vol.120, No.9, pp. 1979-1990, 2023, DOI:10.32604/ee.2023.029480 - 03 August 2023

Abstract The load types in low-voltage distribution systems are diverse. Some loads have current signals that are similar to series fault arcs, making it difficult to effectively detect fault arcs during their occurrence and sustained combustion, which can easily lead to serious electrical fire accidents. To address this issue, this paper establishes a fault arc prototype experimental platform, selects multiple commonly used loads for fault arc experiments, and collects data in both normal and fault states. By analyzing waveform characteristics and selecting fault discrimination feature indicators, corresponding feature values are extracted for qualitative analysis to explore… More > Graphic Abstract

Fawad Nasim^1,2,*, Sohail Masood^1,2, Arfan Jaffar^1,2, Usman Ahmad¹, Muhammad Rashid³

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3175-3190, 2023, DOI:10.32604/csse.2023.034550 - 03 April 2023

Abstract An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is… More >