A signal pre-processing algorithm designed for the needs of hardware implementation of neural classifiers used in condition monitoring

• Signal pre-processing algorithm designed for condition monitoring.
• Algorithm can be performed in real-time conditions.
• Implementation of the algorithm does not require many resources of FPGAs.
• Algorithm resistant to small speed fluctuations of monitored object.
• Features estimated by the algorithm can serve as an input to a neural network.

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.