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



Published Jul 5, 2016
Marcia Baptista Ivo P. de Medeiros Joao P. Malere Helmut Prendinger Cairo L. Nascimento Jr. and Elsa Henriques


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 any 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 datadriven methods such as neural networks and decision trees. Our experimental results show significant performance gain compared to the baseline approach.

How to Cite

Baptista, M., Medeiros, I. P. de, Malere, J. P., Prendinger, H., Nascimento Jr., C. L., & Henriques, and E. (2016). A Comparison of Data-driven Techniques for Engine Bleed Valve Prognostics using Aircraft-derived Fault Messages. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1606
Abstract 148 | PDF Downloads 144



PHM industrial applications, Data Driven Approaches, Big Data Analytics, Aerospace

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