Dynamic Condition Adversarial Adaptation for Fault Diagnosis of Wind Turbine Gearbox.

While deep learning has found widespread utility in gearbox fault diagnosis, its direct application to wind turbine gearboxes encounters significant hurdles. Disparities in data distribution across a spectrum of operating conditions for wind turbines result in a marked decrease in diagnostic accuracy. In response, this study introduces a tailored dynamic conditional adversarial domain adaptation model for fault diagnosis in wind turbine gearboxes amidst cross-condition scenarios. The model adeptly adjusts the importance of aligning marginal and conditional distributions using distance metric factors. Information entropy parameters are also incorporated to assess individual sample transferability, prioritizing highly transferable samples during domain alignment. The amalgamation of these dynamic factors empowers the approach to maintain stability across varied data distributions. Comprehensive experiments on both gear and bearing data validate the method's efficacy in cross-condition fault diagnosis. Comparative outcomes demonstrate that, when contrasted with four advanced transfer learning techniques, the dynamic conditional adversarial domain adaptation model attains superior accuracy and stability in multi-transfer tasks, making it notably suitable for diagnosing wind turbine gearbox faults.

A high sensitivity cross-capacitive sensor for water droplets and bubble detection in hydraulic oil.

The presence of moisture and air in hydraulic oil will seriously affect the reliability of machines. This paper proposes a new cross-capacitive oil pollution detection sensor, which is based on the Thompson and Lampard theorem. The sensing unit consists of four identical copper electrodes with infinitesimally small gaps. The sensor can effectively distinguish water droplets and air bubble pollutants mixed in the oil through the pulse direction of the signal. Compared with traditional capacitive sensors, the sensor has a significant improvement in detection accuracy and detection throughput. In this paper, the relationship between the cross-capacitance value with the dielectric constant and the frequency in an alternating electric field was deduced, and the best excitation frequency was chosen as 1.9 MHz. Experiments show that the sensor can effectively detect water droplets of 140-160 µm and bubbles of 170-190 µm and has good linearity for detecting water droplets and air bubbles of different sizes. The sensor provides a new method for machine condition monitoring of hydraulic systems.