Master Thesis - Sensing in Smart Maintenance
Manufacturing & Production · Production & Assembly · Manufacturing Engineering · Process Improvement · Maintenance & Reliability
In short
This Master Thesis project at Alfa Laval in Lund focuses on implementing smart sensing and data analysis for optimized machinery maintenance. The student will investigate sensor selection, positioning, data handling, and visualization techniques to enable predictive maintenance and reduce downtime.
Responsibilities
- Investigate relevant data types and the state-of-the-art in sensor technology
- Recommend sensor specifications for various applications
- Advise on data storage, sampling frequency, and analysis methods
- Suggest ways to visualize data for effective anomaly detection
- Work closely with production engineering and maintenance personnel on the factory floor
- Interact with sensor suppliers to understand possibilities and limitations
Requirements
- Student pursuing a Master's degree
- Thrives in open-ended environments
- Interest in sensing and data for maintenance optimization
Desired Qualifications
- Develop a manual describing applications for sensing, sensor positions, parameters, and variables
- Provide recommendations for sensor specifications for different applications
- Create guidelines for sampling frequency and data storage
- Analyze correlations between measurement methods
- Deliver recommendations for data analysis and visualization to enhance anomaly detection
Benefits
- Opportunity to make a positive impact
- Work in a dynamic team at Alfa Laval Technologies, Global Core Components
- Gain industrial tutoring from a maintenance engineer and an automation engineer
- Directly support Alfa Laval’s efforts to minimize waste and optimize maintenance practices
- Potential for recommendations to be implemented and have a lasting impact
#thesis#master thesis#sensing#smart maintenance#production#automation#maintenance#sensor technology#data handling#data visualization#anomaly detection