Condition Monitoring Of Hydraulic Pumps For Fluid Power Applications

Nowadays, condition monitoring is becoming crucial to increase productivity and reduce maintenance costs and asset downtime. However, one of the greatest limits is that to obtain a robust and reliable algorithm a large amount of data is required. Getting those data from the experiments is costly both in terms of time and money. Damaging components to measure faulty data is not always possible. In this thesis, a lumped parameter model has been used to generate different fault levels in an axial piston pump. In this way, a large number of faults and their combination has been available to train the condition monitoring model with a reduced computational effort. To validate this approach, both a healthy pump and an extremely damaged pump have been tested. The comparison concerning the simulated data has shown good precision. Furthermore, data from simulation have been used to find the minimum number of sensors to detect different types of faults: the number of sensors has been decreased by 65%. In future work, the lumped parameter model will be substituted with a more precise one, to further increase the condition monitoring algorithm accuracy. Part of this thesis has been done at Maha Fluid Power Research Lab of Purdue University in Lafayette, USA.