State Perception and Health Prediction of Key Equipment in Power Metering Production

Abstract

Combining intelligent sensing technology with an improved grey wolf optimizer (GWO) algorithm, this article innovatively studied state perception and health prediction methods for key power metering equipment. A state perception network was constructed, utilizing intelligent sensing for real-time collection and monitoring of equipment operation. Gabor transform was applied for signal denoising, followed by empirical mode decomposition to extract operational features. An improved GWO algorithm enhanced search efficiency and global optimization, avoiding local optima. A combination prediction model was established to improve prediction accuracy and reliability. Results showed average data collection and processing times of 0.053 and 0.196 seconds, with 93.84% accuracy, and fault warning and false alarm rates of 93.37% and 6.63%, respectively. This method effectively monitors equipment state and predicts health, aiding proactive maintenance planning and reducing fault costs.