Towards smart monitoring of systems: an integrated non-parametric Bayesian KDE and LSTM approach for anomaly detection of rotating machinery under uncertainties

Abstract

The Bayesian inference metric is derived from the normality assumption of data, and is widely used in engineering applications. Owing to data scarcity and uncertainty, the assumption is usually violated, leading to inaccurate results in anomaly detection of large rotating machinery. This paper proposes a novel non-parametric Bayesian evaluation metric based on kernel density estimation of data to avoid the normality assumption. Based on kernel density estimation, a non-parametric Bayesian evaluation metric is mathematically derived from both null and alternative hypothesis tests to improve its accuracy and generality without any distribution assumption. It provides a quantitative indicator for assessing the status of a rotating machines under data uncertainties. A comparison with the traditional method is conducted to demonstrate the effectiveness and feasibility of the proposed methodology in terms of normal and non-normal distributions of data. A general Bayesian evaluation procedure is proposed for the anomaly detection of rotating machinery. Sensor data collected from a real-world steam turbine were used to illustrate the effectiveness of the proposed method.