Abstract
Suspension of wear particles in gear oil with respect to the diversity of particle size combined with filter mechanisms has been analyzed. Coupling of wear modes from tribology is combined with particle size bins to show how a mathematical model can be expanded to include information gained from sensors that can segment particles into size bins. In order to establish boundary conditions for the model based on real data, a filtration test is included.
Finally, the model is fitted to data from a gear in operation and differences between real data and the model are discussed.
The findings show that particles less than 14 μm dominate the wear. Hence, it is concluded that abrasion dominate the wear, for the gear in operation, and it is concluded to be in quasi-stationary mode. The distribution of the particles is observed in conjunction with the particle quantity to determine a basis for normal operation.
Limitations to the model in lack of fitting to large and frequent signal spikes are suggested to be caused by measurement equipment and/or model constraints.
Predicting the transition from quasi-stationary (normal) mode to break-down mode is made possible by particle quantity detection as well as concentration distribution.
Finally, the model is fitted to data from a gear in operation and differences between real data and the model are discussed.
The findings show that particles less than 14 μm dominate the wear. Hence, it is concluded that abrasion dominate the wear, for the gear in operation, and it is concluded to be in quasi-stationary mode. The distribution of the particles is observed in conjunction with the particle quantity to determine a basis for normal operation.
Limitations to the model in lack of fitting to large and frequent signal spikes are suggested to be caused by measurement equipment and/or model constraints.
Predicting the transition from quasi-stationary (normal) mode to break-down mode is made possible by particle quantity detection as well as concentration distribution.
Original language | English |
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Journal | Wear |
Volume | 324-325 |
Issue number | February |
Pages (from-to) | 140-146 |
Number of pages | 7 |
ISSN | 0043-1648 |
DOIs | |
Publication status | Published - 1. Feb 2015 |