Mother wavelet selection in the discrete wavelet transform for condition monitoring of wind turbine drivetrain bearings

Abstract

Although the discrete wavelet transform has been used for diagnosing bearing faults for two decades, most work in this field has been done with test rig data. Since field data starts to be made more available, there is a need to shift into application studies. The choice of mother wavelet, ie, the predefined shape used to analyse the signal, has previously been investigated with simulated and test rig data without consensus of optimal choice in literature. Common between these investigations is the use of the wavelet coefficients' Shannon entropy to find which mother wavelet can yield the most useful features for condition monitoring. This study attempts to find the optimal mother wavelet selection using the discrete wavelet transform. Datasets from wind turbine gearbox accelerometers, consisting of enveloped vibration measurements monitoring both healthy and faulty bearings, have been analysed. The bearing fault frequencies' excitation level has been analysed with 130 different mother wavelets, yielding a definitive measure on their performance. Also, the applicability of Shannon entropy as a ranking method of mother wavelets has been investigated. The results show the discrete wavelet transforms ability to identify faults regardless of mother wavelet used, with the excitation level varying no more than 4%. By analysing the Shannon entropy, broad predictions to the excitation level could be drawn within the mother wavelet families but no direct correlation to the main results. Also, the high computational effort of high order Symlet wavelets, without increased performance, makes them unsuitable.

Bearing monitoring in the wind turbine drivetrain: A comparative study of the FFT and wavelet transforms

“…The Daubechies family of mother wavelets has often been used in literature, with both an increase in performance when monitoring bearings and gears as well as increasing computational effort as the order increases . The Daubechies of order 30 and was chosen for this study has been shown to perform at a high level in a comparison of 130 different mother wavelets, while not requiring the increased computational effort of the higher order Daubechies which was shown not to significantly increase performance . The first implementation of the DWT consists of a three‐level breakdown on the low‐frequency components.…”

Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding

“…Similarly, a signal-to-noise ratio based wavelet selection (SNRBWS) method is proposed in [11], and a sub-band energy to entropy based wavelet selection (SBETEBWS) method is proposed in [12]. Energy to entropy ratio, sample entropy, and sparsity index are also utilized as criteria in optimal mother wavelet selection [28][29][30].…”

Wavelet-Based Filtration Procedure for Denoising the Predicted CO2 Waveforms in Smart Home within the Internet of Things

“…In this context, a selection of a suitable wavelet function is essential for the proper trend detection. Furthermore, it is important to mention that the wavelet transformation allows for the CO 2 signal decomposition into individual decomposition levels, keeping certain trends and detail information, while the rest is irreversibly suppressed [50,51]. On the basis of this procedure, we can build the wavelet-based filter bank, allowing for the CO 2 signal decomposition in multiple levels.…”