Silicon Saxony Day
Use of Multi-Sensors in Predictive Maintenance – Drill Tool Predictions using AI
Sebastian Uziel
Abstract:
Use of Multi-Sensors in Predictive Maintenance – Drill Tool Predictions using AI
Pitch-Vortrag, Technology Island: Artificial Intelligence. In this talk, we will present a case study for drill tool wear prediction and discuss options to address the mentioned challenges. To do that, we will cover possible sensors, their positioning, requirements during the data acquisition phase, the feature selection, two options to model the problems at hand and the respective results. Special attention is paid on multi-sensor data acquisition and fusion of the sensor data. In addition, we cover aspects such as retrofitability.
AI-based monitoring of ventilation systems
Demo setup at our booth – artificial intelligence determines condition and maintenance requirements of fans:
- fast detection of defects in the motor bearing or fan blades using pre-trained AI (machine learning)
- very low energy consumption due to execution of the AI on microcontrollers
- retrofittable stand-alone solution with integrated acceleration sensor and direct status display via LED
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Lead application
Adaptive edge AI systems for industrial application
We are researching solutions for adaptive edge AI systems to make AI possible on low-consumption embedded systems in industry and to network them in real time.
Service for R&D
Development of embedded systems
We develop embedded systems for you as complete solutions consisting of sensors and actuators, signal processing and communications technology as well as open-loop and closed-loop controls.
Core topic
Embedded AI
The numerous existing AI algorithms and methods for high-performance computing are unsuitable for embedded systems. We are researching to optimise AI algorithms and methods so that they can be used on embedded systems.
Research field
Smart distributed measurement and test systems
Integrated sensor ICs are the heart of sensor and measurement systems like wireless sensors, stationary or handheld devices. We are researching solutions for ever more powerful sensors with more intrinsic intelligence and task allocation in the network.
Contact
Contact
Dipl.-Hdl. Dipl.-Des. Beate Hövelmans
Head of Corporate Communications
beate.hoevelmans(at)imms.de+49 (0) 3677 874 93 13
Beate Hövelmans is responsible for the text and image editorial work on this website, for the social media presence of IMMS on LinkedIn and YouTube, the annual reports, for press and media relations with regional and specialist media and other communication formats. She provides texts, photographs and video material for your reporting on IMMS, arranges contacts for interviews and is the contact person for events.