Gearbox Reliability Collaborative Gearbox 1 Failure Analysis Report: December 2010 - January 2011

Errichello, Robert; Müller, Julian-Steffen

doi:10.2172/1036039

Cited by 24 publications

(24 citation statements)

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“…The determination on which level the research should stay is depended on data sufficiency and the perspective. Thanks to the excellent work in [13], more failure information of gearbox subcomponents are provided. In [6] [11] and [3], the failure analysis focused on wind turbine components level due to the lack of field data in detail.…”

Section: Qualitative Analysis On Failure Modes Effects Main Causes mentioning

confidence: 99%

An Improved FMECA Method for Wind Turbines Health Management

Du¹,

Yi²,

Guo³

et al. 2017

EPE

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In this paper, an improved Failure Modes, Effects and Criticality Analysis (FMECA) method for wind turbine reliability investigation is proposed. According to previous work, the main deficiencies of Risk Priority Number (RPN) based criticality analysis are presented. For the qualitative analysis, failure modes and main causes of a gearbox are analyzed on subcomponents level with available data. After that, inspection approaches are provided and mitigation methods are recommended from both design and maintenance stage. For criticality analysis, another index, Probable Profit Loss (PPL) is created and all the parameters are carefully defined to overcome the limits of conventional RNP method. Moreover, to make PPL applicable for maintenance tasks, different maintenance types are considered for profit loss calculation. The criticality rank shows the efficiency of the index and can be used as reference for making maintenance schedules.

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Section: Qualitative Analysis On Failure Modes Effects Main Causes mentioning

confidence: 99%

An Improved FMECA Method for Wind Turbines Health Management

Du¹,

Yi²,

Guo³

et al. 2017

EPE

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“…Many of the internal gear and bearing components were damaged during these losses of oil events. A list of the different faults that occurred on the gearbox is provided in Table , and a more detailed description with pictures of the damaged components can be found in the failure report by Errichello and Muller …”

Section: Description Of Test‐bedmentioning

confidence: 99%

A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains

et al. 2013

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The ability to detect and diagnose incipient gear and bearing degradation can offer substantial improvements in reliability and availability of the wind turbine asset. Considering the motivation for improved reliability of the wind turbine drive train, numerous research efforts have been conducted using a vast array of vibration‐based algorithms. Despite these efforts, the techniques are often evaluated on smaller‐scale test‐beds, and existing studies do not provide a detailed comparison between the various vibration‐based condition monitoring algorithms. This study evaluates a multitude of methods, including frequency domain and cepstrum analysis, time synchronous averaging narrowband and residual methods, bearing envelope analysis and spectral kurtosis‐based methods. A full‐scale baseline wind turbine drive train and a drive train with several gear and bearing failures are tested at the National Renewable Energy Laboratory (NREL) dynamometer test cell during the NREL Gear Reliability Collaborative Round Robin study. A tabular set of results is presented to highlight the ability of each algorithm to accurately detect the bearing and gear wheel component health. The results highlight that the cepstrum and the narrowband phase modulation signal were effective methods for diagnosing gear tooth problems, whereas bearing envelope analysis could confidently detect most of the bearing‐related failures. Copyright © 2013 John Wiley & Sons, Ltd.

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“…Subsequently, the gearbox was removed from the turbine and shipped back to NREL. After conducting a limited set of condition monitoring tests in the NREL dynamometer, the gearbox was disassembled and inspected [7].…”

Section: Grc Damage Analysismentioning

confidence: 99%