Wind industry experiences a tremendous growth during the last few decades. As of the end of 2016, the worldwide total installed electricity generation capacity from wind power amounted to 486,790 MW, presenting an increase of 12.5% compared to the previous year. Nowadays wind turbine manufacturers tend to adopt new business models proposing total health monitoring services and solutions, using regular inspections or even embedding sensors and health monitoring systems within each unit. Regularly planned or permanent monitoring ensures a continuous power generation and reduces maintenance costs, prompting specific actions when necessary. The core of wind turbine drivetrain is usually a complicated planetary gearbox. One of the main gearbox components which are commonly responsible for the machinery breakdowns are rolling element bearings. The failure signs of an early bearing damage are usually weak compared to other sources of excitation (e.g., gears). Focusing toward the accurate and early bearing fault detection, a plethora of signal processing methods have been proposed including spectral analysis, synchronous averaging and enveloping. Envelope analysis is based on the extraction of the envelope of the signal, after filtering around a frequency band excited by impacts due to the bearing faults. Kurtogram has been proposed and widely used as an automatic methodology for the selection of the filtering band, being on the other hand sensible in outliers. Recently, an emerging interest has been focused on modeling rotating machinery signals as cyclostationary, which is a particular class of nonstationary stochastic processes. Cyclic spectral correlation and cyclic spectral coherence (CSC) have been presented as powerful tools for condition monitoring of rolling element bearings, exploiting their cyclostationary behavior. In this work, a new diagnostic tool is introduced based on the integration of the cyclic spectral coherence (CSC) along a frequency band that contains the diagnostic information. A special procedure is proposed in order to automatically select the filtering band, maximizing the corresponding fault indicators. The effectiveness of the methodology is validated using the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking dataset which includes various faults with different levels of diagnostic complexity.

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