Data correlation model for hydraulic fluid filter condition monitoring

Anton Jokinen, Olof Calonius, Jagan Gorle, Matti Pietola

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

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In fluid power systems, one of the most common causes of failure is contamination of the hydraulic fluid. Without filtering the fluid gets contaminated with harmful particles over time, which will cause excessive wear of components or even block motion of parts in flow control valves. In order to avoid machine downtime, it is important to monitor that adequate technical performance level of the fluid is maintained at all times.
This study contributes to condition-based maintenance of hydraulic fluid filter units by establishing a correlation equation, based on comprehensive laboratory tests and incorporated in a simulation model, relating the pressure drop over the filter unit with the main variables describing the operating conditions of the fluid system as well as with filter operating time.
The paper describes how the correlation equation and the simulation model was constructed. The results indicate that good correlation was obtained (R-square value 0.98) with the constructed equation between the physical variables and the temporal development of the pressure drop over the filter. The model can be used as a building block for a smart filter unit that can predict its lifetime.
Original languageEnglish
Title of host publication16th Scandinavian International Conference on Fluid Power, SICFP 2019
EditorsKalevi Huhtala
PublisherTampereen yliopisto
Number of pages9
ISBN (Electronic)978-952-03-1126-1, 978-952-03-1302-9
ISBN (Print)978-952-03-1125-4
Publication statusPublished - 22 May 2019
MoE publication typeA4 Conference publication
EventScandinavian International Conference on Fluid Power - Tampere-talo, Tampere, Finland
Duration: 22 May 201924 May 2019
Conference number: 16


ConferenceScandinavian International Conference on Fluid Power
Abbreviated titleSICFP
Internet address


  • hydraulic fluid filter
  • correlation model
  • condition monitoring


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