Robust detection of hidden material damages

Overview

Machine Learning (ML) techniques are widely used in Structural Health Monitoring (SHM) and Non-destructive Testing (NDT), but the learning process, the learned models, and the prediction consistency are poorly understood.

This work investigates and compares a wide range of ML models and algorithms for the detection of hidden damages in materials monitored using low-cost strain sensors.

The investigation is performed using a multi-domain simulator imposing a tight coupling of physical and sensor network simulation in the real-time scale. The device under test is approximated by using a mass-spring network and a multi-body physics solver.

Introduction

Sensorial Materials

A sensorial material poses tight coupling of structure, sensors, data processing, communication, and energy supply, integrated in a host material ⇒ Acquisition of the state of the structure material

Sensorial materials extend structural materials with the following functions:

1. Load Monitoring
2. Structural Health Monitoring
3. Non-destructive Testing

Machine Learning can be utilised to detect damages and to acquire the state of the material.

Test and Prediction

Damage diagnostic and prediction is the outcome of testing:

Non-destructive Testing (NDT)

Semi-automated or manual detection of damages and relevant changes of materials and structures

Structural Health Monitoring (SHM)

Automated recording of the state of a technical structure or device at run-time (including damages, but not limited to)

• NDT, SHM, and the prediction of damages is still a challenge even in conventional monolithic materials

• New materials and hybrid materials, e.g., fibre-metal laminates, are subject to hidden damages without externally visible change of the material

• Well established measuring techniques are ultra-sonic monitoring and computer tomography

  • Both techniques suffer from their high instrumental effort and difficulties in diagnostic robustness

Test and Prediction

External monitoring of internal damages of such materials and structures with simple and low-cost external sensors, e.g., strain-gauge sensors, under run-time conditions is of high interest.

• Different parameters and constraints have influence on the test result, accuracy, and its probability of trust:

  • Noise (external, internal)
  • Sensor failure
  • Sensor node failure in a sensor network
  • Change of material and structure properties at run-time
  • Gap between base-line and run-time conditions
  • Inaccurate models

Machine Learning

Models and Algorithms

Models and algorithms have to be distinguished. Models are functions, graphs, trees, and tables. Algorithms perform training, testing, and classification (i.e., prediction).

The following learning algorithms and models were used for damage prediction:

• Classical decision tree learner (C45)
• Advanced decision tree learner with interval arithmetic and nearest-neighbour approximation (ICE)
• Random forest tree learner (RF)
• Single layer perceptrons (one layered artificial neural network, SLP)
• Multi layer perceptrons (deep learning with hidden layers, MLP)
• Multi-label Support Vector Machines (SVM)

Architectures

Machine learning aims to find a model function M that maps an input vector x on an output vector y:

Machine learning is divided in three phases:

1. Learning of a model function M with training sensor data (labelled with target variable values commonly by an expert in case of supervised learning)
2. Testing of the computed model function with test data to estimate the model accuracy (using training data and additional labelled data sets)
3. Application of M to new sensor data

Architectures

There are two main classes of sensor data and learning strategies that can be used:

1. One spatially distributed data set D(t) sampled at a specific time t (or averaged in a time interval) → Global Learning with one instance

2. A set of time-resolved sensor data d(p) at a specific spatial position p → Local Learning with multiple instances and global fusion

Architectures

Learning and Noise

• Noise (including sensor failures) has a high impact on the model function M and its prediction accuracy

• Traditional learners like decision trees do not address noisy sensor data

• To cope with noisy sensor data, a new decision tree algorithm ICE is introduced, derived from classical ID3/C45 decision tree learners

• Instead using sensor variables directly, each sensor variable x_i is transformed to an interval variable with a noise margin ε, i.e., x_i → [x_i-ε_i,x_i+ε_i]

• This noise margin and interval arithmetic used by the decision tree learner improves the model quality and prediction accuracy significantly

Multi-domain Simulation

In this work, a multi-domain simulation tool is used to compare and evaluate different ML algorithms and models.

Domains

To enable the physical simulation of mechanical structures and the response of sensor networks on dynamic changes of the structure two relevant domains and models have to be coupled tightly:

Physical Model

Multi-body physics (MBP) using the CANNON physics engine to solve dynamic equations of mass-spring systems modelling a mechanical structure

Computational Model

Multi-agent systems and sensor networks using the JAM agent platform to implement centralised and decentralised sensor processing and damage prediction

Physical Model

Multi-body Physics

• Traditionally the mechanic behaviour of structures is computed by using Finite Element Methods (FEM)

• FEM poses high computation times

• To enable fast and real-time simulation of arbitrary shaped structures a simplified Multi-body physics (MBP) approach and Multi-body simulation (MBS) are used in this work

• A MBP model consists of a set of bodies (rigid or elastic) and a set of connections between the bodies

• Forces between bodies and friction is considered in MBS

Mass-Spring Systems

• Elastic materials are modelled by a set of rigid masses M connected by a set of springs Sp, creating a mass-spring graph network St=<M,Sp>

• Each mass node is connected with up to 12 neighbour nodes

Physical Model