Image-Based Gear Analytics Intern

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Job Description:
Business Context
Gears are critical machine components operating under heavy loads over extended periods. Their failure can result in significant operational disruptions and financial losses.
Lubricants business performs gear inspections using boroscopic imaging. While image acquisition is well established, the analysis of these images remains largely manual, leading to variability in assessments and longer reporting times.
This project aims to develop scientific image analysis algorithms to:
- Quantify gear wear patterns
- Improve consistency and objectivity of assessments
- Reduce turnaround time for inspection reporting
The outcome will significantly enhance the value proposition of Shell’s inspection services and support more data-driven maintenance recommendations.
Objective of Internship
To design and implement image-based analytical algorithms that can automate and standardize the interpretation of boroscopic gear inspection images.
Key Responsibilities
- Develop and implement image processing and analysis algorithms for gear wear detection
- Quantify wear characteristics from inspection images
- Validate algorithm outputs against existing inspection assessments
- Improve robustness, accuracy, and repeatability of analysis workflows
- Streamline processing to reduce inspection reporting time
- Document methodology, code, and results for deployment and future scaling
- Present findings to technical and business stakeholders
Key Deliverables
- Functional prototype for automated or semi-automated gear image analysis
- Quantitative metrics for wear assessment
- Documented workflow and user guidance
- Summary report demonstrating improvements in consistency and efficiency
Candidate Profile
- Student in Mechanical Engineering, Computer Science, Data Science, or related field
- Strong background in image processing and scientific computing
- Proficiency in Python (e.g., OpenCV, NumPy, SciPy) or similar tools
- Experience in developing analytical tools or algorithms
- Familiarity with machine learning or computer vision techniques is desirable
- Ability to work independently on applied technical problems
Selection Rationale
The candidate will be identified based on demonstrated technical merit in scientific image analysis and tool development. Experience aligning closely with the requirements of this project is desired to deliver practical, high-impact outcomes within a short span of time.
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