Master's Thesis Project: "Predictive Maintenance using Condition Monitoring."

AkzoNobel

  • Malmö
  • Permanent
  • Heltid
  • 20 timmar sedan
About AkzoNobel
Since 1792, we've been supplying innovative paints and coatings that help to color people's lives and protect what matters most. Our world class portfolio of brands - including Dulux, International, Sikkens and Interpon - is trusted by customers around the globe. We're active in more than 150 countries and use our expertise to sustain and enhance the fabric of everyday life. Because we believe every surface is an opportunity. It's what you'd expect from a pioneering and long-established paints company that's dedicated to providing sustainable solutions and preserving the best of what we have today - while creating an even better tomorrow. Let's paint the future together.Project OverviewThis project focuses on the implementation of predictive maintenance strategies for critical equipment using condition monitoring techniques. The student will collect and analyze real-time and historical data such as vibration, temperature, and pressure to identify early warning indicators of failure. The project aims to develop a predictive model that can forecast equipment health, reduce unplanned downtime, and optimize maintenance schedules.Project Goals
  • To implement condition monitoring methods (e.g., vibration, thermal, acoustic, or oil analysis) on selected equipment.
  • To identify key failure patterns and define critical thresholds for early detection.
  • To design a predictive maintenance framework that reduces breakdowns and increases equipment availability.
  • To compare predictive maintenance with current preventive/corrective strategies in terms of cost and reliability.
  • To provide recommendations for scaling predictive maintenance across the facility.
Why Choose This Project?Unplanned breakdowns often cause significant production losses and high maintenance costs in industrial facilities. Traditional preventive maintenance may lead to unnecessary interventions, while corrective maintenance is reactive and costly. Predictive maintenance bridges this gap by utilizing real-time data and analytics to predict failures before they occur. For the company, this ensures higher equipment reliability and energy savings. For the student, it provides exposure to advanced maintenance methodologies, sensor-based monitoring, and data analysis-skills that are in high demand in Industry 4.0.Skills & Competencies
The student undertaking this project will develop and strengthen the following skills:
  • Technical skills: Condition monitoring techniques (vibration, thermography, acoustic, oil analysis), predictive maintenance methods.
  • Analytical skills: Data analysis, trend recognition, fault diagnosis, and predictive modelling.
  • Industrial knowledge: Understanding of moving machinery, failure mechanisms, and maintenance strategies.
  • Software competencies: MS Office, SAP
  • Soft skills: Critical thinking, technical reporting, presentation, and communication with maintenance teams.
Duration & Requirements
Duration: Negotiable, earliest start January 2026.
Location: On-site at AkzoNobel Industrial Coatings in Malmö.
Thesis must meet academic requirements.How to Apply
Submit the following documents to ahmer.javed@akzonobel.com, which will be evaluated continuously:
  • CV, and a short one-paragraph cover letter.
  • Academic transcript.
  • For more details, contact Ahmer Javed, Site Engineering Manager at: ahmer.javed@akzonobel.com
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AkzoNobel