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

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

Simulation and Modelling

Get the Data!

The dynamic behaviour of mechanical structures are simulated with a simple Multi-body Physics engine and model to compute virtual sensors for the experiments!

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

Multi-body Physics Model

(a) Mass-spring model of structure (b) Sensor Node Network (c) Strain Sensors (d) Virtual defects and disturbant loads

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

Concept

The sensor processing and damage prediction concept: Local sensor processing, learning, inference ⇒ Global fusion and prediction of position and time of damage event

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

Training Data Variance

• One major issue in training of machine learned models is specialisation of the model!

• Broad variance of training data samples are required for generalised models!

• Experimental collection of large sensor data bases with high variance is difficult to achieve!

• Simulation can overcome this limitation:

  • Using Monte Carlo methods applied to sensor signals and experimental parameters create a broad variance of data samples!

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

Different device and measuring setups together with MC randomising sensor and parameter data create a diverse data base used for ML

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

Learning Predictor Functions

Using Associated Time-series Prediction with Recurrent state-based Artificial Neural Networks for Damage Prediction

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

Time-Series Prediction

• Recurrent ANN are state-based and remembering the history (e.g., of a series of data points)

• A Long-short Term Memory cell (LSTM) architecture can be used for:

  • Time-series Prediction of a time-resolved sensor signal (e.g., strain gauge sensor) or data point series, i.e., there is a f(x_i): x_i → x_i+δ

  • Associated data-series Prediction with a input-output variable mapping, i.e., there is a f(x_i): x_i → y_i+δ

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

(a) Simple Recuccrent Neural Network; data activates network sequentially (b) LSTM cell architecture (c) Time- and data series prediction x_i → x_i+δ (d) Associated data series prediction x_i → y_i+δ

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

Damage Location Prediction

Get the location by global fusion of local predictions!

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

Results and Evaluation

Experiment.

Artificial Sensor Network with 7 × 4 nodes. Each node was equipped with two orthogonal strain-gauge sensors.

A simple environmental vibration was the stimulus.

After a specific simulation time step t=t_n a defect was introduced (hole).

The time-resolved signal record was processed.

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

Distributed Damage Prediction

Network Activation (Local Prediction)

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

Prediction Accuracy

Fusioned true-positive damage event predictions; position prediction error ε ± 2σ, percentage value is position error relative to plate size)

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

Conclusions

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

Lessons Learned

1. Although the training of state-based damage discriminator functions mapping time-series of raw sensor signals on damage detectors is a challenge, some remarkable results could be achieved.

2. Due to overlapping damage detection areas (redundancy) of single nodes, the fusion of the outputs of all sensor nodes lead to a significant improvement of the overall damage event detection probability.

3. Multi-body physics and mass-spring models were used to simulate a vibration of the DUT and to compute virtual strain sensors accurately enough to test the damage prediction approach!

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

Thank You!

Thank you for your attention. All questions are welcome!

• Further information can be found here:
  • http://edu-9.de
  • http://sblab.de