Towards certification: A complete statistical validation pipeline for supervised learning in industry

TL;DR

This paper presents a comprehensive validation pipeline combining statistical, optimization, and deep learning methods to certify supervised learning models in industrial aerospace applications.

Contribution

It introduces a complete, multi-step validation pipeline tailored for AI certification in industry, integrating interdisciplinary concepts and efficient algorithms.

Findings

Successfully applied to an aerostructural failure prediction problem

Demonstrates the pipeline's effectiveness in industrial certification scenarios

Provides a structured approach for AI validation in aerospace industry

Abstract

Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aeronautical industries have recently developed a roadmap for concepts of design assurance and integration of neural network-related technologies in the aeronautical sector. This paper aims to contribute to this paradigm of AI-based certification in the context of supervised learning, by outlining a complete validation pipeline that integrates deep learning, optimization and statistical methods. This pipeline is composed by a directed graphical model of ten steps. Each of these steps is addressed by a merging key concepts from different contributing disciplines (from machine learning or optimization to statistics) and adapting them to an industrial scenario, as well as by developing computationally efficient algorithmic solutions. We illustrate the application of this pipeline in a realistic supervised problem arising in aerostructural design: predicting the likelikood of different stress-related failure modes during different airflight maneuvers based on a (large) set of features characterising the aircraft internal loads and geometric parameters.