Predictive models for overall health of hydroelectric equipment based on multi-measurement point output

Abstract With the increased operating time and usage frequency of hydroelectric equipment, monitoring and predicting its health state has become increasingly important. Traditional health assessment methods often rely on single measurement point data, which has problems such as insufficient model precision and poor real-time performance. These methods are difficult to reflect the overall health state of the equipment fully and cannot accurately capture the complex dynamic characteristics of the equipment, lacking real-time monitoring capabilities. This article proposed a comprehensive health prediction model for hydroelectric equipment based on the multimeasurement point output, which realized real-time monitoring and prediction of the health state of hydroelectric equipment, optimized maintenance strategies, and reduced maintenance difficulty. By collecting temperature signal data, vibration signal data, pressure signal data, and lubrication degree signal data from three systems including upper guide bearing, thrust guide bearing system, and water guide bearing in hydroelectric equipment, they were used as original sensor signal data. Data preprocessing is performed on original sensor signal data, including handling missing and outliers, stabilizing nonstationary time series data, and filtering temporal noise to address the impact of diverse types and large differences in data scales, achieving more accurate predictions. This article used sequential Bayesian method (SBM), hypersphere algorithm, and long short-term memory (LSTM) network to construct a prediction model. These algorithms had different advantages and applicable scenarios and could complement each other to improve the precision and robustness of prediction models. To further improve prediction precision, the model parameters were optimized through cross validation to avoid overfitting and improve model performance. By comparing and analyzing the predictive performance, error results, and real-time prediction performance before and after model optimization, it was concluded that the prediction model constructed by SBM, hypersphere algorithm, and LSTM network had an overall average improvement of 23.7% in the prediction precision of 12 parameters, including temperature, vibration frequency, pressure, and lubrication degree, for the optimized upper guide bearing, thrust guide bearing, and water guide bearing systems. The overall average error has decreased by 55.6%, and the real-time prediction performance has improved by 10.4%. The optimized model not only improves accuracy but also significantly enhances real-time performance, which can better adapt to real-time prediction needs in dynamic environments.