Information Theory Based Probabilistic Approach to Blade Damage Detection of Turbomachine Using Sensor Data

An unplanned breakdown in power generation or chemical plant due to the component failure of turbomachines often results in a huge loss of property and productivity as well as a significant increase in maintenance costs. It has become of paramount importance to predict component damage in a turbomachine using instrumented data. Most existing models, however, are obtained from multiple assumptions, resulting in a high false detection ratio due to various data uncertainties. In this article, we present a novel model-free probabilistic methodology for damage detection to resolve the drawbacks of the classical methods. The proposed method adeptly integrates Bayesian inference, wavelets signal processing, probabilistic principal components analysis, and entropy information theory. Bayesian inference is developed for denoising raw data by integrating with multiscale discrete wavelet packets transform and reducing multivariate dimension by combining with principal components analysis. The entropy information theory has been proposed to extract the feature from principal components as a precursor of the event. A multimetric hierarchical alerting strategy is proposed to predict component damage to enhance the accuracy. The feasibility of the presented novel pattern recognition methodology is demonstrated with the detection of blade damage events in a real-world steam turbine using sensor data.