This thesis evaluates standard statistical and machine learning models for early fault detection for Valve Regulated Lead-Acid (VRLA) batteries in uninterruptible power supply (UPS) units. Unexpected battery failures in emergency support systems throughout CERN can endanger working personnel. Thus the minimization of downtime of such battery systems is essential. Previous research conducted in a Tencent Datacenter using traditional regression models such as Gradient Boosting Decision Tree (GBDT) resulted in a 98% accurate prediction model that can predict 15 days in advance of a battery unit failure. The main features were the pack resistance's standard deviation, relative voltage and relative resistance.
In this study conducted at CERN, the goal was to get a similarly well-performing regression or machine learning model without the possibility to obtain the running average of internal resistance of the battery unit.
Throughout the study, different traditional regression algorithms were considered, but eventually, a predictive model using neural networks was selected. Neural networks provide a more comprehensive and more accurate when it comes to nonlinear fitting. The most fitting model for the given dataset was an Recurrent Neural Network (RNN) model with Adagrad compiler optimizer using Rectified Linear Unit (RELU), in combination with a sigmoid, activation function. However, even with the optimal neural network configuration, the model's overall accuracy does not perform sufficiently to conclude an overall positive result. The lack of internal resistance metric appears to be so significant that the precision of the model has lacked evidence of correctness. Thus, to get significant predictability of a VRLA battery, it is essential to measure the impedance of the battery units.