Statistical descriptor of normality based on Hotelling's T/sup 2/ statistic and mixture of Gaussians

Novelty detection is an issue of primary importance as it can help to provide an improvement in the reliability of machine health monitoring. Novelty detection estimates the model of the normal operating regime or state and verifies whether new data is deviating from its normal operating regime. Feature extraction techniques using vibration data and novelty detection methods based on mixture of Gaussians (MoG), Chebyshev bound, Hotelling's statistic, and support vector machine (SVM) are discussed. A statistical descriptor of normality based on Hotelling's statistic and mixture of Gaussians is proposed. The performance of novelty detection algorithms based on the aforementioned techniques are analyzed for both real-life and artificial (real data with simulated load regime) vibration datasets. The proposed method demonstrates encouraging performance on real datasets with simulated load regime.