Extending Engine Gas Path Analysis Using Full Flight Data

Abstract

Abstract The ability to trend engine module performance and provide engine system fault detection and isolation are arguably, core capabilities for any engine Condition Based Maintenance (CBM) system. The origins of on-condition monitoring can be traced back nearly four decades, and a methodology known as Gas Path Analysis (GPA) has played a pivotal role in its evolution. Legacy Gas Path Analysis is a general methodology that assesses and quantifies changes in the underlying performance of the major modules of the engine (compressors and turbines), which in turn, directly affect overall performance measures of interest such as fuel consumption, power availability, compressor surge margins, and the like. Additionally, this approach is easily adapted to enable fault detection and identification of many engine system accessory faults such as variable stator vanes, handling and customer bleeds, sensor biases, and drift. Classical GPA has been confined to off-board analysis of averaged snapshot data when the engine is in steady state operation. This discrete data point approach, while reasonably accurate and repeatable, introduces a time latency to confidently detect and identify a faulty condition. Depending on the type and severity of the underlying fault, time to identify can be the differentiating factor in avoiding an unanticipated engine removal, flight delay or cancellation, in-flight engine shutdown, or catastrophic event. In this paper, we explore the use of streaming full flight data, which includes both transient and steady state operation. This type of data stream, when properly processed by gas path analysis and information fusion algorithms, allows faster anomaly detection, credible fault persistency checks, and timely fault identification within the current flight.