Multivariate change detection for time series data in aircraft engine fault diagnostics

Change detection is an essential task in equipment monitoring, fault diagnostics and system prognostics. It involves monitoring change to the device state to detect faulty behavior. Early change detection can indicate abnormal conditions that can help in early fault diagnostics. This will allow for timely maintenance actions to be taken before the fault progresses, causes secondary damage to the system, and leads to equipment downtime. Approaches for change detection commonly make use of univariate techniques to detect changes in the measurement of individual sensors. However, overall system state is also characterized by the interactions and inter-relationships between the various sensor measurements considered together; univariate techniques, by focusing individually on the sensors, can miss some of the state information present in the system of sensors examined as a whole. In this paper, we briefly introduce a couple of interesting univariate change detection techniques, and then we investigate the application of three different multivariate change detection techniques. We also present results from the application of these techniques to high bypass commercial engines.