The field of predictive maintenance has gained increasing interest recently for various reasons with the improvement of monitoring techniques and the increase of new methodologies and algorithms across different learning methods. There is an urgent need for the industry to detect faults accurately and in advance in the production environment, to minimize maintenance costs, prevent sudden failures and ensure optimum use of machines. Ideally, the process begins with collecting historical data from many sensors installed in different devices. In this paper, the available propulsion system data is used due to time limitation as the recording of historical data takes vast amount of time. Instead, the implementation of machine learning models using two popular algorithms are focused here. The evaluation of applied machine learning algorithms provides promising results to implement in the industry.