Project 03
Machines Before Failure
A predictive-maintenance workflow using industrial telemetry, rolling-window features, and model comparison.
- COMPLETED INTERNSHIP PROJECT
- Category
- Industrial AI / Predictive Maintenance
- Role
- Electrical & Instrumentation Intern
- Maturity
- Completed internship project
- Year
- 2024-2025
30-second proof
What this proves
- Problem
- Industrial motor telemetry needed to be transformed into interpretable maintenance-risk signals.
- Operational or business context
- Bahrain Steel internship project using real industrial telemetry.
- My contribution
- I engineered rolling-window sensor features, compared Random Forest and XGBoost, evaluated the normalized time-to-failure target, and presented the findings.
- Constraints
- Multivariate sensor signals · Time-dependent patterns · Normalized prediction target · Stakeholder interpretation
- Project maturity
- Completed internship project
- Contextual technical stack
- Python · pandas · scikit-learn · XGBoost · Time-series feature engineering
Measurement noteMAE and RMSE refer to the normalized target and must not be described as physical time units.
Ownership
Designed
- I designed rolling-window features to represent recent sensor behavior.
Implemented
- I implemented the model comparison workflow for Random Forest and XGBoost.
Collaborative context
- I presented the findings in an internship stakeholder context.
Overview
A predictive-maintenance workflow using industrial telemetry, rolling-window features, and model comparison.
The problem
Raw industrial telemetry needs careful feature preparation before it can support predictive-maintenance analysis.
Context
The project used industrial motor telemetry in an internship context.
My role
I worked on this project as an Electrical & Instrumentation Intern.
What I built
I built a workflow using rolling-window features and model comparison for predictive maintenance.
How it works
Telemetry records are shaped into rolling-window features and compared through predictive models against a normalized time-to-failure target.
- Telemetry
- Rolling-window features
- Model comparison
- Risk estimate
- Maintenance insight
Decisions
- I used rolling-window features to represent recent sensor behavior.
- I compared two tree-based model approaches.
- I evaluated performance on the normalized target.
Evidence and results
Verified evidence includes telemetry scale, sensor scale, motor-system count, and normalized-target error values.
The MAE and RMSE values describe error on a normalized target and should not be interpreted as physical time units.
MAE and RMSE refer to the normalized target and must not be described as physical time units.
Challenges and limitations
Public details are limited to the verified summary and aggregate evidence listed here.
What I would improve next
I would add sanitized model-comparison diagrams and clearer operational interpretation.