Internship Anomaly Detection for Smart Maintenance

About us

Damen aims to become the worlds most sustainable and digitally connected shipyard. The Research Development & Innovation(RD&I)department develops and implements the technology andknow-howto achieve these ambitions. We activelyassistthe business in creating an innovative product portfolio andprovideforward-thinking guidance to improve the quality and performance of Damens products and services.You will be joining the Data Science team within Damen RD&Ilocatedin Gorinchem. Our department focuses on applyingcutting-edgedata and AI solutions to Damens shipbuilding and maritime operations. The team includes domain experts in physics-informed machine learning simulation acceleration predictive maintenance computer vision and operational analytics.This internship is part of SmartMaintenancea strategic project aimed at using AI to detect abnormal equipment behavior on board vessels before it leads to failure or unplanned downtime.

Therole

As an intern you will work on our Smart Maintenance project where we have developedananomaly detection algorithm which aims to help engineers spot early signs of equipment problems such as engines pumps propulsion and cooling systems before they escalate into failures. Vessels generatehuge amountsof sensor data during operation and our goal is to turn that data into reliable trustworthy signals that support maintenance decisions.You will contribute to an existing pipeline that learns what healthy equipment behavior looks like and flags deviations from it. Your primary focus will be on a dedicated research topic to be selected together with the team that strengthens a specific part of this pipeline from data selection todetectionreliability health trending explainability or deployment. There is room to shape the exact topic based on your interests and background either before or shortly after you start.This can be a thesis/graduate internship and could start from September onwards.

Possible researchtopicsthat we offer on which the final scope is to be defined together:

• Model transferability across vessels:exploring how an anomaly detection model trained onone vesselcan be adapted to other vessels machinery types or operating environments including retraining recalibration and drift detection strategies.

• Reliable anomaly detection:improving detection models to minimize false alarms adapt to different operating conditions handle transient events and quantify prediction confidence.

• Health and degradation trending:moving beyond fault detection toidentifygradual performance degradation developing health indicators that give early warning of wear or efficiency loss.

• Explainable AI and fault diagnosis:making anomaly models explainableidentifyingwhich sensors or components drive an alert and supporting root-cause analysis for engineers.

• Defining healthy operation:identifying selecting andvalidatingrepresentative data from vesselsoperatingunder normal conditions accounting for varying operating modes and environmental influences.

Key accountabilities

As an intern you will:

• Support the development and improvement of ML-based anomaly detection models for vessel equipment.

• Preprocess and analyze sensor/time-series data from onboard systems.

• Run experiments in Python evaluating model performance against real and/or simulated data.

• Work closely with our Data Scientists maintenance engineers and vessel operations stakeholders.

• Document results and present findings to the team regularly.

Skills & Experience
We are looking for a student who:

• Is currently pursuing a Bachelor orMaster in Data Science Applied Mathematics Computer Science Mechanical/Electrical Engineering or a related technical field.

• Ideally combines data science with a mechanical/electrical engineering background with the ability to model equipment behavior and understand which sensors are informative for which failure modes.

• Has experience withStatisticsPython and ideally with machine learning(LSTMs)or time-series analysis.

• Has an interest in predictive maintenance sensordataor industrial/marine systems.

• Iscomfortable working with real-world sometimes messy operational data.

• Communicates fluently in English.

What we offer

• Mentoring atacademiclevel throughout the internship.

• Internship/graduation fee and travel allowance for the duration of the assignment.

• Opportunity to contribute to a high-impact predictive maintenance project used in real vessel operations.

• Research publication islikely possiblewitha possible extensionof the internship period.

• Exposure to a multidisciplinary team combining data science and maritime engineeringexpertise.

Other

Are you ready to sail into your new adventure at Damen Dont hesitate send us your motivation letter and resume here.

Due to housing issues we cannot accept international students that do not have accommodation in the Netherlands yet.