No abstract

This paper employs empirical mode decomposition (EMD) and fast Fourier transform (FFT) to analyze the oilleakage fault signal of the gearbox of wind turbines. K-nearest neighbors (KNN) is used on automatic fault recognition. First, both normal and faulty oil-leakage gearboxes are considered. Second, EMD is applied on analyzing the intrinsic mode function (IMF) of the current signals, and FFT is used to get the IMF spectrum. Finally, the features of the spectrum are extracted, and KNN is used on fault recognition of wind turbine gearboxes. The result indicates that it can effectively recognize the oil-leakage fault of gearboxes.

This study proposes a new automatic bicycle-borne measurement system to quantify the pressing force on the pedals, detect the pedal cranks' rotations, compute the rear wheel's tr aveling distance and speed, calculate the accumulated calories burned and the equivalent fat loss incurred within the rider through cycling and, in addition, render a travel clock. The proposed system, with a built-in microprocessor, processes the measured data and displays the computed output, including the time, the traveling distance and speed, the accumulated calories burned and the equivalent weight (fat) loss during the cycling. via an LCD panel. The computation methods are detailed. The proposed system will convert a regular bicycle into a unique double-purpose equipment for botb traveling and fitness exercise, usable both indoors and outdoors on botb sunny and rainy days. Tbe test results of this study's prototype are also listed and compared with standard or reference data in this paper.

No abstract