An Enhanced Technique for Reactor Coolant Pump Abnormality Monitoring Using Continuous Wavelet Transform Based Sparse Code Shrinkage De-noising Algorithm

Detection of the weak signature of degradation of the Reactor Coolant Pump (RCP) at early stage gives more time for maintenance reaction, safety decision-making and also provides economic benefits. An integrated and improved method to detect and identify abnormality using continuous wavelet transform based sparse code shrinkage de-noising algorithm is suggested in this work. For RCP roller bearings, periodic impulses indicate the occurrence of faults in the components. However, it is difficult to detect the impulses because they are rather weak and are often immersed in heavy noise. Existing wavelet threshold de-noising methods do not work well because they use orthogonal wavelets, which do not match the impulse very well and do not utilize prior information on the impulse. Therefore, in order to suppress any undesired information and highlight the features of interest, a new method for wavelet threshold de-noising is proposed in this paper. It employs an adapted Morlet wavelet as the basic wavelet for matching the impulse and also uses the Maximum Likelihood Estimation (MLE) for thresholding by utilizing prior information on the probability density function (pdf) of the impulse. By using MLE de-noising method, the inspected signal is analyzed in a more exact way even with a very low signal-to-noise ratio.