<p class="noindent">This paper presents a method for an economic condition monitoring implementation on axial piston pumps. The vision is to use only one “low-budget” sensor that records as much information as possible. It shows the detection of three typical faults – valve plate cavitation, slipper wear and axial slipper-piston clearance. A variety of sensors were used to collect data on efficiencies, flow rates, temperatures, and accelerations of the new and the faulty pump for 100 different operating conditions. Using a representative set of operating conditions with different speed, pressure and swash plate angle, the faults were predicted for two different machine learning algorithms for different sensors. The triaxial acceleration sensor achieves the best accuracy of 100% with a sampling rate of 16.4 kHz. To further reduce costs, the sampling rate is virtually downsampled until 4.1 kHz, resulting in a drop to 91% prediction accuracy. Moreover, the prediction accuracy for different sets of operating conditions with constant speed, pressure and swash plate angle are compared to each other showing best results for remaining the speed constant.</p><p class="indent">This paper shows, that it is possible to achieve accuracies over 98% on fault detection with only using the y-component of an acceleration sensor with a sampling rate of 4.1 kHz.</p>