Continuous Health Monitoring of Bearing by Oscillatory Sparsity Indices Under Non Stationary Time Varying Speed Condition

Aiming at the limitations of conventional bearing health monitoring indices to perform under varying speed operating condition, in this research, sparsity index guided bearing health monitoring has been achieved after oscillation based decomposition of vibration signals. Firstly, low oscillatory component of a vibration signal is separated from the original signal with the help of tunable Q factor wavelet transform (TQWT) based on its ability to perform continuously under varying speed condition. Then, considering the low oscillatory transient nature of bearing fault signature, health monitoring of rolling element bearing is conducted by quantifying the extracted low oscillatory vibration signal component with four prominent sparsity indices namely kurtosis, Gini index, negative entropy, and reciprocal smoothness index. One experimental dataset and one commercial wind turbine-bearing dataset have been used to verify the effectiveness of the proposed indices in comparison to the original sparsity indices and recently proposed adaptive weighted signal preprocessing technique based sparsity indices.