In 2016 the average industry downtime cost was estimated to $260.000 every hour, and with Swedish industries being an important part of the national economy it would be desirable to reduce the amount of unplanned downtime to a minimum. There are currently many different solutions for system supervision for monitoring system health but none which analyse data with machine learning in an industrial gateway.   The aim for this thesis is to test, compare and evaluate three different algorithms to find a classifier suitable for a gateway environment. The evaluated algorithms were Random Forest, K-Nearest Neighbour and Linear Discriminant Analysis. Load imbalance detection was used as a case study for evaluating these algorithms. The gateway received data from a Modbus ATV32 frequency converter, which measured specific features from an induction motor. The imbalance was created with loads that were attached on a fly-wheel at different angles to simulate different imbalances. The classifiers were compared on their accuracy, memory usage, CPU usage and execution time. The result was evaluated with tables, confusion matrices and AUC- ROC curves.  Although all algorithms performed well LDA was best based on the criteria set.