Bulb tubular unit working condition vibration swing early warning method based on big data

Abstract

This invention relates to the field of hydropower technology and discloses a method for early warning of vibration swing of bulb turbine units under different operating conditions based on big data. Through steps S1-S5, the method specifically performs intelligent early warning of vibration swing of the bulb turbine unit under different operating conditions. It combines data from multiple Chinese turbine models and the SVR method to fit the health status of the bulb turbine unit, i.e., historical data from the initial operation or after major overhaul. Furthermore, by using different operating condition standards, it achieves personalized judgment based on a single unit. The method compares the real-time vibration swing value of the unit with standard values, alarm values, and shutdown value ranges, providing real-time warnings of exceeding limits and shutdowns for interval operation. It also makes timely intelligent diagnoses, effectively improving the effectiveness of the vibration swing early warning system, reducing false alarms and misreporting, effectively helping to reduce the probability of unit failure, improving the stable operation frequency of the unit, and creating significant economic benefits.

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