VRLA battery fault prediction for data center based on random forest model and feature enhancement method

The Valve-Regulated Lead-Acid (VRLA) battery is an important part of data center power supply system. Battery failure will threaten the safe operation of the data center. How to predict the impending battery failure in advance is an urgent problem to be solved to ensure the operation safety of the data center. However, batteries in such precise data centers rarely fail. Few faulty samples cause extreme imbalance between normal samples and faulty samples. In the data center site, the battery is usually in a floating state and the battery charge-discharge cycle times are less. As a result, the obtained battery data has a single working condition. In addition, VRLA batteries are faced with the problems of limited observation information (large amount of data but low data dimension, only voltage, resistance and temperature collected directly from sensors). In this paper, a feature enhancement method is proposed by analyzing the working characteristics of VRLA batteries in the data center. This method extends the two-dimensional characteristics (voltage, resistance) of battery to nine-dimensional characteristics to solve the problem of limited observation information of VRLA battery. The problem of extreme imbalance between normal samples and faulty samples of battery is solved based on clustering undersampling method. Based on the above two methods, a VRLA battery fault classification prediction model is proposed. The nine-month operation data of 1000 VRLA battery were randomly selected from a data center and combined with the simulated fault samples to form a test set. The test results show that the F-score value of the model is increased from 54.5 % to 97.5 % after the clustering undersampling method and the feature enhancement method proposed in this paper. Compared with the VRLA battery replacement strategy recommended in IEEE STD 1188-2005 on this test set, the method can predict the impending battery failure at least 3 days in advance.