Machine diagnostics is usually done via conditioned monitoring (CM). This approach analyses certain thresholds or patterns for diagnostic purposes. This approach can be costly and time consuming for industries. A larger downside is the difficulty in generalizing CM to a wider set of machines.There is a new trend of using a Machine learning (ML) approach to diagnose machines states. An ML approach would implement an autonomous system for diagnosing machines. It is highly desirable within industry to replace the manual labor performed when setting up CBM systems. Often the ML algorithms chosen are novelty/anomaly based. It is a popular hypothesis that detecting anomalous measurements from a system is a natural byproduct of a machine in a faulty state.The purpose of this thesis is to help CombiQ with an implementation strategy for a fault detection system. The idea of the fault detection system is to make prediction outcomes for machines within the system. More specifically, the prediction will inform whether a machine is in a faulty state or a normal state. An ML approach will be implemented to predict anomalous measurements that corresponds to a faulty state. The system will have no previous data on the machines. However, data for a machine will be acquired once sensors (designed by CombiQ) have been set up for the machine.The results of the thesis proposes an unsupervised and semi-supervised approach for creating the ML models used for the fault detection system. The unsupervised approach will rely on assumptions when selecting the hyperparameters for the ML. The semi-supervised approach will try to learn the hyperparameters through cross validation and grid search. An experiment was set up check whether three ML algorithms can learn optimal hyperparameter values for predicting rotational unbalance. The algorithm known as OneClassVM showed the best precision results and hence proved more useful for CombiQ’s criterium.