Stefan Bosse: Learning Damage Event Discriminator Functions with Distributed Multi-instance Machine Learning

Introduction

Motivation

This work addresses a novel distributed machine learning multi-instance approach to overcome limitations and flaws in decentralised Structural Health Monitoring using decentralised single-instance sensor processing

Stefan Bosse: Learning Damage Event Discriminator Functions with Distributed Multi-instance Machine Learning

Objectives

1. Robust prediction of hidden damage events in mechanical structures using raw sensor time series and time-series prediction;

2. Typical environmental vibrations of the structure are used for the measuting stimulus (no actuators are required);

3. Scalability: The sensor processing and learning is performed locally on sensor node level with a global fusion of prediction results to estimate the damage location and the time of the damage creation.

Stefan Bosse: Learning Damage Event Discriminator Functions with Distributed Multi-instance Machine Learning

Structural Monitoring

There are at least four different levels of information that can be delivered by a Structural Health Monitoring (SHM) systems:

1. Detection of damages and material changes;

2. Localization of damage;

3. Assessment of damages and impact on operational safety;

4. Prediction of mechanical and operation behaviour.

Stefan Bosse: Learning Damage Event Discriminator Functions with Distributed Multi-instance Machine Learning

Sensor Networks

Integration Levels

1. Traditional Sensor Networks used for SHM are applied separately to the structure
2. Ongoing progress in miniatursisation enables Material-intergrated Sensor Networks → Sensorial Materials!

Material-integrated Sensor Networks

Stefan Bosse: Learning Damage Event Discriminator Functions with Distributed Multi-instance Machine Learning

Machine Learning

• Two architectures:
  • Centralised Single-instance Learner
  • Decentralised Multi-instance Learner with Fusion