Structural Health Monitoring (SHM) System (1-2S/M)

Status: Available

• Type: Semester Thesis
• Professor: Prof. Dr. L. Benini
• Supervisors:
  • Philip Wiese (IIS): wiesep@iis.ee.ethz.ch
  • Victor Kartsch Morinigo (IIS): victor.kartsch@iis.ee.ethz.ch

Introduction

Structural Health Monitoring of Bridge.

Smart sensors, which are sensors equipped with advanced computation capabilities, present a unique opportunity for innovation in multiple critical domains such as environmental sensing to reduce global greenhouse gas emissions or infrastructure monitoring.

A near-sensor processing of the gathered data that directly extracts meaning from the signal can lead to huge improvements in terms of energy efficiency and increased privacy. The reduced energy consumption comes from the fact that more specialized hardware such as the PULP platform including the GAP9 can be used, and, additionally, that the information density of the transmitted data is higher, and thus less data must be transmitted via the often very power-intensive wireless link.

Our long-term goal is to develop a low-power sensing node with very low power consumption. These nodes should incorporate multiple sensors to enable multi-modal sensing. In other words, combine the data of the different sensors to extract additional data. The idea is to use the very efficient GAP9 multicore processor and deploy multi-modal neural networks to perform feature extraction, prediction, anomaly detection, and even on-device learning to adaptively fine-tune the model based on the measured data.

In this project, students will explore structural health monitoring by integrating data from an Inertial Measurement Unit (IMU) for vibration analysis, a microphone for acoustic emissions indicating structural issues like cracks, and environmental sensors measuring temperature and pressure. The collected data will be analyzed using machine learning models developed to predict maintenance needs, detect potential failures, and assess how environmental conditions influence structural integrity. The end goal is to deploy these models into a real-time monitoring system capable of issuing alerts to maintenance teams, thus facilitating prompt and preventive actions to safeguard infrastructure.

Project

• Base Framework: Integrate data from the IMU for vibration analysis, the microphone for acoustic emissions (detecting cracks or failures), and environmental sensors (temperature, pressure) to assess the condition of structures.
• Application Specific Algorithm Development: Development of machine learning models that analyze the sensor data to predict maintenance needs, detect anomalies indicating potential failures, and assess the impact of environmental conditions on structural integrity.
• Deployment on Edge Device: Deployment and real-time monitoring. Provision of alerts for maintenance teams.

Prerequisites

• Good understanding of deep learning concepts.
• PCB Design and embedded C programming.
• Familiarity with Python programming.
• Familiarity with the PyTorch deep learning framework.