Abstract
This paper proposes a signal processing approach for wind turbine gearbox vibration signals based on employing multiple analysis pipelines. These so-called pipelines consist of combinations of various advanced signal processing methods that have been proven to be effective in literature when applied to wind turbine vibration signals. The performance of the pipelines is examined on vibration data containing different wind turbine gearbox faults in the planetary stages. Condition indicators are extracted from every pipeline to evaluate the fault detection capability for such incipient failures. The results indicate that the multipronged approach with the different pipelines increases the reliability of successfully detecting incipient planetary stage gearbox faults. The type, location, and severity of the fault influences the choice for the appropriate processing method combination. It is therefore often insufficient to only utilize a single processing pipeline for vibration analysis of wind turbine gearbox faults. Besides investigating the performance of the different processing techniques, the main outcome and recommendation of this paper is thus to employ a diversified analysis methodology which is not limited to a sole method combination, to improve the early detection rate of planetary stage gearbox faults.
Highlights
According to various reliability studies in the wind turbine literature, the gearbox is one of the most critical components in terms of maintenance
This section demonstrates results obtained through the signal processing pipelines as proposed in Section 3, applied on real wind turbine vibration signals with confirmed drivetrain faults
The frequency of collection depends on the operator but it can be weekly for older turbines or daily for newer installed turbines and condition monitoring systems
Summary
According to various reliability studies in the wind turbine literature, the gearbox is one of the most critical components in terms of maintenance. Condition monitoring systems enable the detection of faults at an early stage, which can reduce unplanned and unscheduled repair costs. Vibration signal analysis is the most predominantly used method for wind turbine drive train condition monitoring [2]. A wind turbine gearbox consists of different stages of gears and bearings. Considering this variety in components, their fault behaviour and the wide frequency range, different vibration analysis methods have been developed that address different faults, such as Time-Synchronous Averaging (TSA), Cepstrum and Envelope spectrum [3]. Various gear fault detection algorithms and condition indicators are analysed and compared in [4, 5]. A robust multicomponent fault detection approach still remains a challenge though
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