Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition

Teng, Wei; Wang, Feng; Zhang, Kaili; Liu, Yibing; Ding, Xian

doi:10.5545/sv-jme.2013.1295

Cited by 36 publications

(16 citation statements)

References 23 publications

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“…C.Hatch [7] stressed the importance of Acceleration enveloping technique in wind turbine condition monitoring. Wei Teng et al [8] found out that Empirical Mode Decomposition is a more powerful technique than conventional demodulation techniques using Hilbert transform for identification of gear pitting failures in a wind turbine gearbox. James C. Robinson [9] found that analysis of stress waves proved to be an effective diagnostic tool for fault detection and severity assessment in gearboxes.…”

Section: Literature Survey On Various Condition Monitoring Techniquesmentioning

confidence: 99%

Assessment of sideband energy ratio technique in detection of wind turbine gear defects

Pattabiraman

Srinivasan

Malarmohan

2015

Case Studies in Mechanical Systems and Signal Processing

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A B S T R A C TGearbox failure is one of the highest risk events in wind turbines. In most of the wind turbines, planetary gearboxes are preferred over conventional gearboxes due to their significant advantages. But condition monitoring of planetary gearboxes present a huge challenge to the vibration analysts due to complex design and construction of its unit, vibration transducer type and locations, wide frequency range of the vibrations, resolution required to separate frequencies and dynamic range required to observe both low frequency and high frequency components in the spectrum.Due to strong Gear Mesh Frequency (GMF) signals, gear defect vibration characteristics can often be suppressed in the overall vibration signal. So there is a need to develop or utilize various special signal processing techniques in order to identify and monitor the progression of defects in gears more effectively.This paper focuses on one such technique namely Sideband Energy Ratio (SER) for monitoring of gear defect progression in wind turbine gearboxes. Theory behind SER is and its significance in gear defect monitoring is presented in this paper through three case studies. In all the three case studies, SER of 2XGMF were found to be more sensitive than 1XGMF towards gear defect progression.

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Section: Literature Survey On Various Condition Monitoring Techniquesmentioning

confidence: 99%

Assessment of sideband energy ratio technique in detection of wind turbine gear defects

Pattabiraman

Srinivasan

Malarmohan

2015

Case Studies in Mechanical Systems and Signal Processing

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“…Empirical mode decomposition (EMD) is a valuable time–frequency tool, especially for the non‐stationary vibration signals of wind turbines. An application of EMD on a wind turbine gearbox gear pitting fault is presented in [35]. EMD combined with energy separation can provide a framework for detecting planetary gearbox faults, as presented in [36].…”

Section: Vibration CM and Fault Detection In Wind Turbinesmentioning

confidence: 99%

Comparison of wind turbine gearbox vibration analysis algorithms based on feature extraction and classification

Koukoura

Carroll

McDonald

et al. 2019

IET Renewable Power Generation

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Health state assessment of wind turbine components has become a vital aspect of wind farm operations in order to reduce maintenance costs. The gearbox is one of the most costly components to replace and it is usually monitored through vibration condition monitoring. This study aims to present a review of the most popular existing gear vibration diagnostic methods. Features are extracted from the vibration signals based on each method and are used as input in pattern recognition algorithms. Classification of each signal is achieved based on its health state. This is demonstrated in a case study using historic vibration data acquired from operational wind turbines. The data collection starts from a healthy operating condition and leads towards a gear failure. The results of various diagnostic algorithms are compared based on their classification accuracy.

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“…3 has few fault feature. Teng et al [24] give the reason that the sensor of no. 1 is installed on the ribbed slab of the casing where it is not overly sensitive to the excitation of the high-speed stage.…”

Section: Two Wind Turbine Gear Fault Diagnosis Applicationmentioning

confidence: 99%

Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis

Wang

Zhao

Guo

et al. 2017

IET Renewable Power Generation

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The wind turbine gearbox, an important component of wind turbines, has a high failure rate and maintenance cost; therefore, several vibration sensors are installed to execute effective condition monitoring, fault diagnosis, and prediction, for reducing the downtime caused by faults. The sensor positions should directly and accurately reflect the gearbox operating status and provide several signals to the condition monitoring system. This article studies a gearbox model and the gear tooth faults, and builds a gearbox gear vibration model involving these faults. Using structure analysis, sensor configuration solutions rendering all the tooth faults detectable and all the faults isolable are, respectively, obtained, based on the gear vibration model. The sensor configuration solutions of the wind turbine gearbox can guide wind turbine gearbox sensor installation in practice and support common wind turbine gearbox sensor configuration solutions in theory.

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Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition

Cited by 36 publications

References 23 publications

Assessment of sideband energy ratio technique in detection of wind turbine gear defects

Assessment of sideband energy ratio technique in detection of wind turbine gear defects

Comparison of wind turbine gearbox vibration analysis algorithms based on feature extraction and classification

Numeric optimal sensor configuration solutions for wind turbine gearbox based on structure analysis

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