Opportunities for Explainable Artificial Intelligence in Aerospace Predictive Maintenance



Published Jul 19, 2020
Bibhudhendu Shukla
Ip-Shing Fan Ian Jennions


This paper aims to look at the value and the necessity of XAI (Explainable Artificial Intelligence) when using DNNs (Deep Neural Networks) in PM (Predictive Maintenance). The context will be the field of Aerospace IVHM (Integrated Vehicle Health Management) when using DNNs. An XAI (Explainable Artificial Intelligence) system is necessary so that the result of an AI (Artificial Intelligence) solution is clearly explained and understood by a human expert. This would allow the IVHM system to use XAI based PM to improve effectiveness of predictive model. An IVHM system would be able to utilize the information to assess the health of the subsystems, and their effect on the aircraft. Even if the underlying mathematical principles are understood, they lack an understandable insight, hence have difficulty in generating the underlying explanatory structures (i.e. black box). This calls for a process, or system, that enables decisions to be explainable, transparent, and understandable. It is argued that research in XAI would generally help to accelerate the implementation of AI/ML (Machine Learning) in the aerospace domain, and specifically help to facilitate compliance, transparency, and trust. This paper explains the following areas:

  • Challenges & benefits of AI based PM in aerospace
  • Why XAI is required for DNNs in aerospace PM?
  • Evolution of XAI models and industry adoption
  • Framework for XAI using XPA (Explainability Parameters)
  • Discussion about future research in adopting XAI & DNNs in improving IVHM.

How to Cite

Shukla, B., Fan, I.-S., & Jennions, I. (2020). Opportunities for Explainable Artificial Intelligence in Aerospace Predictive Maintenance. PHM Society European Conference, 5(1), 11. Retrieved from https://papers.phmsociety.org/index.php/phme/article/view/1231
Abstract 1188 | PDF Downloads 33246



XAI, Predictive Maintenance, IVHM, EXP, DNNs, AI

Technical Papers