Axial piston variable displacement pumps (VDAP) are the heart of every hydraulic system, and are they commonly used in the industry for its high capacity, efficiency (volume and total), and good performance in the handling of high pressures and speeds. Faults are usually associated with wear and leakage processes, which cause significant decreases in performance. This paper discusses about the advantages to implement a Condition-based Maintenance (CBM) and the use of techniques of fault diagnosis in axial variable displacement piston pumps (VDAP), as they are: Neural Networks (NN), Support Vector Machine (SVM) and Fuzzy Logic (FL) and other hybrid models. The results of this investigation provide guidelines for the selection of the most suitable technique to prevent faults in VDAP in order to help reducing down time, increase productivity and competitiveness of companies that requires the use of fluid power systems by enabling the implementation of a non-intrusive fault diagnosis management system, which must be reliable, economical and easily accessible to the industry.