Abstract: This article aims to explore a power equipment operation status monitoring and fault warning system based on big data technology. Taking a 220 kV substation of a power company in Henan Province as a pilot, more than 3000 sensor nodes were deployed to collect real-time data including current, voltage, temperature, vibration, etc. Utilize big data platforms to clean, store, analyze, and mine collected data, establish equipment health status assessment models and fault warning models. The actual operation results of the system show that the data processing speed reaches 5000 pieces per second, the fault detection accuracy is 98%, the false alarm rate is 2%, the missed alarm rate is 1.5%, and the average warning lead time is 30 minutes.