During the past years an increased focus on the development of autonomous solutions has resulted in driverless vehicles being used in numerous industries. Volvo Construction Equipment is currently developing the TA15, an autonomous hauler part of a larger transport solution. The transition to autonomous haulers have further increased the need for improved system condition monitoring in the strive for increased operational time. A method aiming to identify and isolate faults in the hydraulic steering system on the TA15 was therefore investigated in this thesis. Using fault tree analysis, five faults considered to be of importance regarding steering performance were selected. Two different methods for detecting the faults were compared to each other, data-driven and model based. Out of the two, data-driven was selected as the method of choice due to high modularity and relative simplicity regarding implementation. The data-driven approach consisted of Feed-Forward and Long Short Term Memory networks where the suitable inputs were decided to be a combination of pressure and position signals. Utilizing a simulation model of the steering system validated against the TA15, the selected faults were induced in the simulated system with various severity. Training the networks to classify and estimate fault severity in the simulated model resulted in satisfactory results using both networks. It was however concluded that in contrary to the Feed-Forward network, the LSTM network could achieve good performance using less amount of sensors. Although the diagnostic method showed promising result on a simulation model, test on the real TA15 needs to be performed in order to properly evaluate the method. The advantage of using a data-driven approach was specially noticeable when comparisons were made to the model based approach. The data-driven approach relies on labeling data rather than complete system knowledge. Meaning that the method developed therefore could be applied on practically any hydraulic system in construction equipment by changing the training data.