Predicting failure of remote battery backup systems

Uninterruptable Power Supply (UPS) systems have become essential to modern
industries that require continuous power supply to manage critical operations. Since a
failure of a single battery will affect the entire backup system, UPS systems providers
must replace any battery before it runs dead. In this regard, automated monitoring tools
are required to determine when a battery needs replacement. Nowadays, a primitive
method for monitoring the battery backup system is being used for this task. This thesis
presents a classification model that uses data mining cleansing and processing techniques
to remove useless information from the data obtained from the sensors installed in the
batteries in order to improve the quality of the data and determine at a given moment in
time if a battery should be replaced or not. This prediction model will help UPS systems
providers increase the efficiency of battery monitoring procedures.