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Robust detection of hidden material damages using low-cost external sensors and Machine Learning - A Multi-domain Simulation Study with SEJAM and Mass-Spring Models
Stefan Bosse * 1 , Dirk Lehmhus 2
1  University of Bremen, Dept. of Mathematics and Computer Science, Robert Hooke Str. 5, 28359 Bremen, Germany
2  Fraunhofer IFAM, Wiener Str 1, 28359 Bremen, Germany


Non-destructive Diagnostics and prediction of damages in conventional monolithic materials is still a challenge. New materials and hybrid materials, e.g., fiber-metal laminates, pose hidden damages that show no externally visible change of the material. Well established measuring techniques are ultra-sonic monitoring and computer tomography using x-rays. Both techniques suffer from their high instrumental effort and difficulties in diagnostically robustness. 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. But there is a significant gap between knowledge and understanding of damage models and the interpretation of sensor data. Machine Learning (ML) is a promising method to derive sensor-damage relation models based on training data.

In this work, a multi-domain simulation study is presented comparing and evaluating different ML algorithms and models, i.e., Decision Trees (C45,ID3,ICE), Artificial Neural Networks (single and multi-layer), and many more. A simple plate is used as a Device under Test (DUT), which is modelled using a simple physical mass-spring network model (MSN), finally simulated (computed) by a multi-body physics engine. The physical computation of the DUT under varying load situations in real-time is directly performed by the simulator combined with an agent-based simulation of signal processing in a Distributed Sensor Network (DSN). Artificial strain-gauge sensors placed on the top of the DUT surface are computed directly from the MSN and are used to predict hidden damages (holes, inhomogenities, and impurities) by applying ML to unreliable sensor data. Monte Carlo simulation is used to introduce noise and sensor failures. There are some surprising results concerning robustness and usability of different ML algorithms showing that Deep Learning is not always suitable!

The simulator (SEJAM) combining physical and computational simulation can be used as a generic tool for investigation of ML, sensing, sensor design, and distributed data processing. The entire simulation can be controlled by the user via a chat dialogue controlled by an avatar agent. This feature enables the usage of the simulator for educational purposes, too.

There are two major scientific questions addressed in this work: (1) The suitability and accuracy of mass-spring models, especially for modelling of laminated and hybrid materials; (2) The suitability of ML for damage detection using noisy and unreliable low-cost sensors

Keywords: Multi-domain Simulation, Structural Health Monitoring, Non-destructive damage diagnostics, Machine Learning, Distributed Sensor Networks
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