An Envelope Time Synchronous Averaging for Wind Turbine Gearbox Fault Diagnosis

Vibration-based condition monitoring techniques are widely used for diagnosing faults in rotating machines. These techniques are implemented in the time domain, the frequency domain, or both. However, the composite and noisy nature of the raw data collected requires a preprocessing stage such as filtering and decomposition using in-depth processing techniques. Moreover, these methods require good frequency resolution and involve examining a broad frequency range to discern both healthy and faulty cases. In this work, we introduce a simple and fast diagnostic scheme for wind turbine gear teeth wear based on time domain analysis. The proposed method is based on the local minima interpolation of a filtered version of the vibration signal following time synchronous averaging (TSA) technique. Given tachometer signal, the TSA of the vibration data is performed using MTALAB software. Then, local minima of the filtered signal are interpolated using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) function. The variance of the interpolated curve built a gear fault index. The derived fault index resulting of the proposed technique allows a substantial distinction between the healthy and faulty cases. Its efficiency is validated using 10 real-world datasets of vibration stemmed from a wind turbine planetary gearbox. The proposed method boasts a low computation time and ease of interpretation, specifically beneficial for gearbox fault diagnosis purposes.