An Ensemble Prognostic Method of Francis Turbine Units Using Low-Quality Data under Variable Operating Conditions

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 2; p. 525
• Main Authors: Duan, Ran, Liu, Jie, Zhou, Jianzhong, Wang, Pei, Liu, Wei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2022; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Confidence intervals; Data Accuracy; Data integrity; Datasets; DBSCAN; degradation trend prediction; Environmental engineering; Fault diagnosis; Francis turbine unit; Gaussian mixture model; Genetic algorithms; Hydroelectric power; Information management; Methods; Normal Distribution; Optimization algorithms; Performance evaluation; performance state evaluation; Prognosis; prognostic; Reproducibility of Results; Turbines; prognostic; NSGA-II; performance state evaluation; Francis turbine unit; Gaussian process regression; DBSCAN; degradation trend prediction; Gaussian mixture model
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22020525

The prognostic is the key to the state-based maintenance of Francis turbine units (FTUs), which consists of performance state evaluation and degradation trend prediction. In practical engineering environments, there are three significant difficulties: low data quality, complex variable operation conditions, and prediction model parameter optimization. In order to effectively solve the above three problems, an ensemble prognostic method of FTUs using low-quality data under variable operation conditions is proposed in this study. Firstly, to consider the operation condition parameters, the running data set of the FTU is constructed by the water head, active power, and vibration amplitude of the top cover. Then, to improve the robustness of the proposed model against anomaly data, the density-based spatial clustering of applications with noise (DBSCAN) is introduced to clean outliers and singularities in the raw running data set. Next, considering the randomness of the monitoring data, the healthy state model based on the Gaussian mixture model is constructed, and the negative log-likelihood probability is calculated as the performance degradation indicator (PDI). Furthermore, to predict the trend of PDIs with confidence interval and automatically optimize the prediction model on both accuracy and certainty, the multiobjective prediction model is proposed based on the non-dominated sorting genetic algorithm and Gaussian process regression. Finally, monitoring data from an actual large FTU was used for effectiveness verification. The stability and smoothness of the PDI curve are improved by 3.2 times and 1.9 times, respectively, by DBSCAN compared with 3-sigma. The root-mean-squared error, the prediction interval normalized average, the prediction interval coverage probability, the mean absolute percentage error, and the R2 score of the proposed method achieved 0.223, 0.289, 1.000, 0.641%, and 0.974, respectively. The comparison experiments demonstrate that the proposed method is more robust to low-quality data and has better accuracy, certainty, and reliability for the prognostic of the FTU under complex operating conditions.