Research on Fault Prediction and Health Status Assessment Methods for Power Equipment Integrating Generalized Linear Models and Time Series Analysis

Abstract

: With the deepening integration of renewable energy and smart grid technologies, the accurate prediction of power equipment failures and assessment of their health status have become increasingly critical. Current methodologies often operate in isolation: generalized linear models excel in static health assessment but fail to capture temporal dynamics, while time series models forecast trends but lack mechanistic insight into failures. This paper bridges this gap by proposing a comprehensive hybrid model that fuses the interpretability of GLMs with the predictive power of time series analysis. Our approach begins with rigorous data cleaning and preprocessing of historical sensor data. We then implement a two-stage strategy: Stage 1 employs a Poisson regression model to estimate a foundational health index, translating static operational conditions into an interpretable score. In Stage 2, this health score sequence is treated as a new time series and modeled jointly by an ARIMA component for linear trend analysis and an LSTM network for learning non-linear patterns, resulting in a robust fault trend prediction. Experimental results on real operational datasets confirm that our model achieves superior performance compared to all baseline models, showing significant reductions in RMSE and improvements in F1-score and AUC. The framework effectively provides earlier warnings of sub-healthy states, offering a practical and innovative solution for intelligent, data-driven maintenance strategies in the power industry.