Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines

Abstract

Through condition-based maintenance strategy, engineers can monitor the health states of equipment and take actions based on the sensor data. Limited by the low failure frequency and high monitoring costs, it is difficult to obtain sufficient historical data of all fault types for condition monitoring (CM). In the steam turbine operation, environmental factors, varying power consumption and manual adjustments can lead to a multimode process, which consists of multiple normal and abnormal conditions. This paper proposes a framework for online unsupervised CM and anomaly detection, not relying on expert knowledge or labeled historical data. Since there are often few monitoring data at the beginning of a new incoming operating mode, an adaptive self-transfer learning algorithm based on Gaussian processes is developed to model the monitoring data with uncertainty information, and to capture the cross-correlations between the different normal modes. A two-hierarchical identification criterion based on the predicted posterior intervals is introduced to first identify the change-points in the observations, and second to decide whether it is an anomaly or a transition between normal modes. The proposed framework is tested on a real steam turbine. The results illustrate its high effectiveness.