Wind turbine gearbox fault diagnosis via adaptive IMFogram

Abstract

Wind turbines usually operate in mountainous, offshore and other field environments. As the wind speed is constantly changing, the gearboxes of wind turbines operate under variable operating conditions, resulting in a non-stationery and time-varying signal characteristic. For existing time–frequency analysis (TFA) methods, they undergo low time–frequency energy concentration as well as noise interference during the fault feature extraction. IMFogram, a recently proposed time–frequency representation (TFR) method, calculates local frequency and amplitude information of signals based on fast iterative filtering (FIF). Moreover, it is able to remove uncertainty on the time–frequency plane, allowing a long enough time window to reduce the local boundary effects associated with the signal decomposition methods. Therefore, it has potential advantages for TFA. However, the actual performance of IMFogram calculation largely depends on the decomposition effect provided by FIF and the selection of model parameters. With the purpose of obtaining a more distinct and focused TFR of the wind turbine vibration signal, a novel TFA method, namely Adaptive IMFogram (AIMFogram), is proposed in this paper. Firstly, a range of intrinsic mode functions (IMFs) are gained after the decomposition of vibration signals using an improved variational nonlinear chirp mode decomposition, which is treated as the input to AIMFogram to solve the unsatisfied result by traditional FIF under strong noise. Secondly, Renyi entropy-based particle swarm optimization is conducted to perform adaptive parameter optimization of the AIMFogram. Later, using the optimal parameter, the proposed AIMFogram achieves a high-resolution TFR of complex multi-component vibration signals. To verify whether the proposed AIMFogram is effective or not, the method was successfully employed to the fault diagnosis of the rolling bearing in an experimental bench and the gearbox in a 850-kW wind turbine.