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This paper proposes a wind turbine planetary gearbox (PGB) fault diagnosis method based on a self-powered wireless sensor. The proposed wireless sensor, which consists of a piezoelectric energy harvester, a power management circuit, a microcontroller unit (MCU), a radio-frequency (RF) module, and an accelerometer, can acquire the vibration signals of wind turbine PGB by the accelerometer. The piezoelectric energy harvester utilizing vibration environment is optimized as a power supply for the proposed wireless sensor, including the MCU, RF module, and accelerometer. An ac–dc converter combined with a low-dropout voltage regulator is developed to provide stable dc voltage for the proposed wireless sensor. Stacked denoising autoencoder (SDAE) shows excellent performance in learning robust features from the noised signal. Thus, in this paper, the SDAE method is adopted to learn robust and distinguishable features from measured signals. Then, the least squares support vector machine (LSSVM) is employed to classify features extracted by the SDAE. Both the SDAE and LSSVM are optimized by quantum particle swarm optimization (QPSO). The experimental results show that the presented power supply can generate 3.3-V dc voltage, which ensures regular operation of the rest of the wireless sensor. The proposed wireless sensor can achieve a reliable communication distance of 40.8 m in the test environment. Furthermore, the SDAE approach and LSSVM show excellent performance in feature extraction and fault diagnosis, respectively. The experimental results indicate that the proposed method is effective in terms of fault diagnosis for the wind turbine PGB.

Self-Powered Wireless Sensor for Fault Diagnosis of Wind Turbine Planetary Gearbox