Optimal filtering of gear signals for early damage detection based on the spectral kurtosis

Combet, François; Gelman, Leonid M.

doi:10.1016/j.ymssp.2008.08.002

Cited by 176 publications

(112 citation statements)

References 28 publications

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“…Techniques and filtering methods based on spectral kurtosis are aimed at finding the optimal frequency band for recovering the impulsive fault signature that could be hidden in the raw vibration waveform. A brief review of the calculation procedure and the results of this study are provided, and the interested reader is referred to the work by Antoni et al [30] and Combet et al [34] for a more detailed discussion on the use of spectral kurtosis for filtering vibration signals. The initial step in this algorithm is to calculate the short time Fourier Transform of the vibration signal, denoted by H(t,f).…”

Section: Spectral Kurtosis Filteringmentioning

confidence: 99%

Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

Sheng¹

2012

112

107

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NOTICEThis report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States government or any agency thereof. Executive SummaryUtility-scale wind turbines have historically experienced premature component failures, which subsequently increase the cost of energy. The majority of these failures are caused by faults in the drivetrain, led by the main gearbox. To understand the possible causes for gearbox failures and recommend practices for improvement, the National Wind Technology Center (NWTC), at the National Renewable Energy Laboratory (NREL), started a project called the Gearbox Reliability Collaborative (GRC). Condition Monitoring (CM) is one research area under the GRC. It is a method to assess a system's health, which enables proactive maintenance planning, reduces downtime and operations and maintenance costs, and, to some extent, increases safety.To understand the dynamic responses of wind turbine gearboxes under different loading conditions, the GRC tested two identical gearboxes. One was tested on the NWTC's 2.5 MW dynamometer and the other was first tested in the dynamometer, and then field tested in a turbine in a nearby wind plant. In the field, the test gearbox experienced two oil loss events that resulted in damage to its internal bearings and gears. Since the damage was not catastrophic, the test gearbox was removed from the field and retested in the NWTC's dynamometer before it was disassembled. During the dynamometer retest, various condition monitoring systems, e.g., vibration and oil debris, collected data along with testing condition information. The vibrationbased condition monitoring system and the test condition data enabled NREL to launch a Wind Turbine Gearbox Condition Monitoring Round Robin project, as described in this report. The main objective of this project is to evaluate different vibration analysis algorithms used in wind turbine CM and determine whether typical practices are effective. With the involvement of both academic researchers and industrial partners, the Round Robin provides cutting edge research results to industry stakeholders.Under this project, the collected vibration and testing condition data, along with the test gearbox configuration information, were shared with partners who signed memoranda of understand...

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Section: Spectral Kurtosis Filteringmentioning

confidence: 99%

Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

Sheng¹

2012

112

107

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“…The increase in signal amplitude is greater in the case of operation of these teeth with the pinion teeth starting from No. 4 (4)(5)(6)(7)(8)(9)(10). Recalling the results of numerical experiment conducted above, it may be concluded that due to the gradual growth of this phenomenon this is probably caused by the growing damage to the tooth resulting in changes in its rigidity resulting in disturbances in subsequent parts of the signal.…”

Section: Local Meshing Plane In Diagnosing Tooth Crackmentioning

confidence: 71%

“…According to Combet and Gelman [5], there is high industry demand for methods of early pitting diagnosis when only 5-10% of the tooth surface is damaged. It is mainly related to the development of particularly heavy-duty aircraft power trains (e.g., transmissions in helicopters) and wind turbine gears [6].…”

Section: Introductionmentioning

confidence: 99%

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Model-based detection of local defects in gears

Mączak¹,

Jasiński²

2017

Arch Appl Mech

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The paper is presenting a methodology for developing model-based method of gear fault diagnostics. First the simulation model of the helical gearbox is discussed allowing analysis of the phenomena taking place during teeth mating in the presence of manufacturing and assembly errors. It includes observation of influence of errors on the generated signals. The model was initially used to analyze the teeth contact in the presence of pitch errors and later to verify the sensitiveness of proposed diagnostic methods and their availability to detect the fatigue damages of teeth. The common feature of discussed approach is the direct use of time signal processing algorithms, and in contrary to the methods based on spectral analysis it allows precise localization of gear defects like pitting and tooth fracture associating them to the particular pinion or gear teeth. Their advantage is the simplicity and speed of action that is of great significance for the implementation in the autonomous transmission diagnostic systems and diagnostic systems working online. Presented methods of signal processing were first tested on a simulation model of the gear assembly and later verified during the experiments on a back-to-back test stand.

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“…The filtering procedure based on SK for denosing the signal was proposed in [10]. However, in case of many real complex data the SK is not a sufficient.…”

Section: Introductionmentioning

confidence: 99%

Long term vibration data analysis from wind turbine -statistical vs energy based features

Ignasiak¹,

Gomolla²,

Kruczek³

et al. 2017

Vib. proced.

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Abstract. Wind turbines are operating in varying conditions. Therefore, the recorded signal is highly nonstationary. The typical approach for damage detection in long term data is based on the energy and spectral analysis. This method, suffer for several drawbacks, especially for the signals with high contamination. Thus, the alternative approach is the application of statistical parameters that may indicate the damage. In order to indicate the frequency band corresponding to the damage the proper statistics are used. In this paper, the well know spectral kurtosis and another statistic are applied to the long-term vibration data from wind turbine. Their performance is compared with the standard energy based methods. It is showed that selectors are able to track the damage development and distinguish between healthy and unhealthy case.

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Optimal filtering of gear signals for early damage detection based on the spectral kurtosis

Cited by 176 publications

References 28 publications

Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

Wind Turbine Gearbox Condition Monitoring Round Robin Study - Vibration Analysis

Model-based detection of local defects in gears

Long term vibration data analysis from wind turbine -statistical vs energy based features

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