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Duration
Identical coursesSmart Sensing Technologies (B-KUL-B3079G)
Cannot be taken as part of an examination contract
Naets Frank (coordinator) | Gryllias Konstantinos | Naets Frank Aims
After successful completion of this course, a student will be able to:
- Understand the working principles and limitations of advanced sensing technologies in mechatronics
- Develop sensor processing and fusion methods using state-of-the-art techniques
- Make use of and deploy smart sensing systems to maximize the value for a particular application
Order of Enrolment
Mixed prerequisite:
You may only take this course if you comply with the prerequisites. Prerequisites can be strict or flexible, or can imply simultaneity. A degree level can be also be a prerequisite.
Explanation:
STRICT: You may only take this course if you have passed or applied tolerance for the courses for which this condition is set.
FLEXIBLE: You may only take this course if you have previously taken the courses for which this condition is set.
SIMULTANEOUS: You may only take this course if you also take the courses for which this condition is set (or have taken them previously).
DEGREE: You may only take this course if you have obtained this degree level.
SIMULTANEOUS(B3078G)
The codes of the course units mentioned above correspond to the following course descriptions:
B3078G : Digital Twin
Is included in these courses of study
- Master of Smart Operations and Maintenance in Industry (Bruges et al) (Elective Track 1 - Machine) 60 ects.
Activities
4 ects. Smart Sensing Technologies (B-KUL-B551CR)
Content
0. Introduction
- Definition of smart sensing
1. Innovative sensor-technology (full field sensors, sensor networks, ...)
Do we look into the design of the physical sensor at such: yes, but only regarding what can we expect as output and what are features needed to operate the sensor in a Smart environment
- Sensor networks (wired/wireless, 5G, …)
- Full field techniques (DIC, camera)
- Linking with existing system sensing platforms (e.g. Kistler in injection moulding systems) focus on specific sensor for manufacturing
- Sensor technology (e.g. MEMS)
- Microcontroller architectures/embedded controllers – notions on available technologies
- Power supply functions (energy harvesting, EMI) - innovative powering of sensors
2. Augmenting/improving/enriching sensor data/information // smart exploitation of sensor data
- Self-adapting sensor/smart sensors
- (Model selection for) deployment in state-estimation/sensor fusion
- Extension from state- to input-/state- and parameter-estimation
- Sensor fusion (multi-source sensors)
- Virtual sensing & case studies (electrical machine as a sensor, drive as a sensor, vehicle state estimation, …)
- Handling of large data streams
- Machine learning in view of smart sensing
3. Optimal use of smart sensors: how to select, deploy, … both smart, innovative sensors and traditional sensors
- Automatic data pre-processing (filtering, feature extraction - without interpretation (e.g. images), removing conflicting data, redundancy, …)
- (Automatic) calibration
- Sensor selection and placement
- (Manual) (virtual) sensor tuning
- Edge versus cloud
- Validation of the smart sensing system
Course material
The basic course material consists of the presentations used during the lectures. This material is complemented with compulsory reading material and optional reading materials for those students who want to deepen their insights in specific topics. Where possible, materials will be made available electronically (Toledo).