Abstract
Gearboxes have a significant influence on the durability and reliability of a power transmission
system. Currently, extensive research studies are being carried out to increase
the reliability of gearboxes working in the energy industry, especially with a focus on
planetary gears in wind turbines and bucket wheel excavators. In this paper, a signal
pre-processing algorithm designed for condition monitoring of planetary gears working
in non-stationary operation is presented. The algorithm is dedicated for hardware implementation
on Field Programmable Gate Arrays (FPGAs). The purpose of the algorithm is
to estimate the features of a vibration signal that are related to failures, e.g. misalignment
and unbalance. These features can serve as the components of an input vector for a neural
classifier. The approach proposed here has several important benefits: it is resistant to
small speed fluctuations up to 7%, it can be performed in real-time conditions and its
implementation does not require many resources of FPGAs.
Original language | English |
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Journal | Measurement: Journal of the International Measurement Confederation |
Volume | 73 |
Pages (from-to) | 576–587 |
ISSN | 0263-2241 |
DOIs | |
Publication status | Published - 2015 |
Keywords
- Condition monitoring
- Planetary gears
- Neural networks
- FPGA
- Signal processing