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

Dariusz Dabrowski, Zahra Hashemiyan, Jan Adamczyk

    Research output: Contribution to journalJournal articleResearchpeer-review

    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 languageEnglish
    JournalMeasurement: Journal of the International Measurement Confederation
    Volume73
    Pages (from-to)576–587
    ISSN0263-2241
    DOIs
    Publication statusPublished - 2015

    Keywords

    • Condition monitoring
    • Planetary gears
    • Neural networks
    • FPGA
    • Signal processing

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