This paper develops an innovative integrated methodology for turbomachinery event detection and prediction by using multivariate noisy operation data. The method seamlessly integrates probabilistic method with multiple advanced analytics techniques, including wavelets and entropy information theory. Wavelets based multi-scale principal component analysis is employed to de-noise the raw data for each tag/variable. Probabilistic principal components analysis is further developed to extract useful information from multiple corrected variables, and entropy information feature is extracted as a precursor of the event, the measure of disorder in a thermodynamic system. The proposed method is so-called Wavelet PCA Entropy. The method considers uncertainty in multivariate data, and provides proof-of-concept of advanced analytics for prediction of challenging events in turbomachinery. The feasibility of the presented methodology is demonstrated with the prediction of combustor lean blow out event and data collected from a real-world gas turbine. This study provides a novel intelligent approach to turbomachinery damage diagnostics and prognostics.