Speaker Presentation File
INCOSE Membership Meeting
6:00-6:30 PM: Meet, Greet, and Mingle
Local Introductions at Local Sites
Order/Obtain Food and Drinks
6:30-7:00 PM: Chapter Business
7:00-7:45 PM: Invited Speaker: Dr. Liang Tang, Associate Director, Control & Diagnostics Systems group, Pratt & Whitney
7:45-8:00 PM: Discussion and Wrap-Up
Title: Extending Engine Gas Path Analysis Using Full Flight Data
Dr. Liang Tang is an Associate Director in the Control & Diagnostics Systems group at Pratt & Whitney. He is a sub-discipline technical lead on Performance Analytics with over twenty years of experience in signal processing, fault diagnostics, prognostics and engine health management. He is an expert on gas path analysis, diagnostic reasoning and predictive data analytics. At Pratt & Whitney, Liang is leading several teams developing models & analytics and supporting the health monitoring of revenue service engines. Prior to joining P&W, he was a team lead at Impact Technologies, a Sikorsky company, and earlier, a post-doctoral research fellow at Georgia Tech. Liang is an author of over 60 papers and serves as Assoc. Editor for the ASME Journal of Engineering for Gas Turbines & Power.
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 4 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 (e.g., 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, allows faster anomaly detection, credible fault persistency checks and timely fault identification.