Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine

VIBRATION FEATURE EXTRACTION METHODS FOR GEAR FAULTS DIAGNOSIS -A REVIEW

The key point of condition monitoring and fault diagnosis of gearboxes is a fault feature extraction. The study of fault feature detection in rotating machinery from vibration analysis and diagnosis has attracted sustained attention during past decades. In most cases determination of the condition of a gearbox requires study of more than one feature or a combination of several techniques. This paper attempts to survey and summarize the recent research and development of feature extraction methods for gear fault diagnosis, providing references for researchers concerning with this topic and helping them identify further research topics. First, the feature extraction methods for gear faults diagnosis are briefly introduced, the usefulness of the method is illustrated and the problems and the corresponding solutions are listed. Then, recent applications of feature extraction methods for gear faults diagnosis are summarized, in terms of industrial gearboxes. Finally, the open problems of feature extraction methods for gear fault diagnosis are discussed and potential future research directions are identified. It is expected that this review will serve as an introduction summary of vibration feature extraction methods for gear faults diagnosis for those new to the concepts of its applications to gear fault diagnosis based on vibratio