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.
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:
Machine Learning can be utilised to detect damages and to acquire the state of the material.
Damage diagnostic and prediction is the outcome of testing:
Semi-automated or manual detection of damages and relevant changes of materials and structures
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
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:
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:
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:
There are two main classes of sensor data and learning strategies that can be used:
One spatially distributed data set D(t) sampled at a specific time t (or averaged in a time interval) → Global Learning with one instance
A set of time-resolved sensor data d(p) at a specific spatial position p → Local Learning with multiple instances and global fusion
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 xi is transformed to an interval variable with a noise margin ε, i.e., xi → [xi-εi,xi+εi]
This noise margin and interval arithmetic used by the decision tree learner improves the model quality and prediction accuracy significantly
In this work, a multi-domain simulation tool is used to compare and evaluate different ML algorithms and models.
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:
Multi-body physics (MBP) using the CANNON physics engine to solve dynamic equations of mass-spring systems modelling a mechanical structure
Multi-agent systems and sensor networks using the JAM agent platform to implement centralised and decentralised sensor processing and damage prediction
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
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