A Comparison of Data-driven Techniques for Engine Bleed Valve Prognostics using Aircraft-derived Fault Messages

Márcia Lourenço Baptista
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Embraer; MIT Portugal
AttachmentSizeTimestamp
phmec_16_024.pdf2.03 MBJune 10, 2016 - 8:34am

Prognostics plays an increasingly important role in preventive maintenance and aircraft safety. An approach that has recently become popular in this field is the data-driven technique. This approach consists in the use of past data and advanced statistics to derive estimates for the reliability of an equipment without relying on any physics or engineering principle. Data-driven models have been based on two types of historical data: past failure times and health monitoring data. A kind of health monitoring data rarely used in data-driven models are aircraft-derived maintenance messages. These data consist of fault messages derived from the aircraft onboard systems to notify unexpected events or abnormal behavior as well as to send warning signals of equipment degradation. Fault messages have not received much attention in aircraft prognostics mostly due to its asynchronous and qualitative nature that often causes difficulties of interpretation. The main goal of this paper is to show that data-driven models based on fault messages can provide better prognostics than traditional prognostics based on past failure times. We illustrate this comparison in an industrial case study, involving a critical component of the engine bleed system. The novelty of our work is the combination of new predictors related to fault messages, and the comparison of data-driven methods such as neural networks and decision trees. Our experimental results show significant performance gain compared to the baseline approach.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
024
Page Count: 
13
Submission Keywords: 
Big Data Analytics
Data Driven Approaches
Aerospace
PHM industrial applications
Submission Topic Areas: 
Component-level PHM
Data-driven methods for fault detection, diagnosis, and prognosis
Structural health monitoring
Verification and validation
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