Spot trouble before a machine stops
Use sensor readings to explore early signs of equipment failure.
Can sensor readings help flag equipment that needs inspection?
A fictional workshop has synthetic vibration and temperature readings from independent machines. Build a baseline and a small classifier to flag potential failures. Focus on what false alarms and missed failures mean for an inspection team.
Build it, step by step
- 01
Load the data. Count failures and non-failures. Check the distributions of vibration, temperature, and operating hours. The equipment_id column is an identifier, not a feature.
- 02
Create fixed, stratified training and test splits with random_state=42. Keep test rows separate while choosing features or thresholds.
- 03
Use stratified cross-validation on training data to compare a majority-class baseline with a shallow decision tree. Report recall, precision, and a confusion matrix.
- 04
Choose a decision threshold using training validation predictions only. Explain the inspection cost of false positives and missed faults.
- 05
Evaluate the chosen model on the final test set once. Write down what real sensor history and maintenance logs you would need for a useful field study.
Your project checklist
- Class counts and a documented split.
- No equipment identifiers used as predictive features.
- Precision, recall, and a confusion matrix for the final model.
- A clear explanation of limitations from the tiny synthetic sample and its simplified failure labels.
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This page is part of Python to AI, a free course of 20 modules and 60 lessons in the engineering.fit( ) Learning Lab. The Learning Lab is built and run by engineering.fit( ), a nonprofit founded by Abdullateef Opeyemi Bakare that helps African engineering students gain research experience. You do not need an account or pay a fee to learn.