Predictive Maintenance Model
Project Overview
This model predicts whether an engine requires maintenance using historical engine sensor data.
Business Context
Unexpected engine failures can result in costly repairs, vehicle downtime, reduced fleet availability, and safety risks.
This predictive maintenance model helps identify failure risk early so maintenance can be planned proactively.
Dataset Features
The model uses the following sensor parameters:
- Engine rpm
- Lub oil pressure
- Fuel pressure
- Coolant pressure
- lub oil temp
- Coolant temp
Target variable:
Best Model
Best selected model: Random Forest
Performance
Accuracy: 0.6596
Precision: 0.6728
Recall: 0.8957
F1 Score: 0.7684
ROC AUC: 0.6992
Best Hyperparameters
{
"n_estimators": 100,
"min_samples_split": 2,
"min_samples_leaf": 1,
"max_depth": 5
}
Example Usage
import joblib
import pandas as pd
model = joblib.load("best_predictive_maintenance_model.pkl")
sample = pd.DataFrame(
[[700, 3.5, 5.0, 2.5, 80, 85]],
columns=[
"Engine rpm",
"Lub oil pressure",
"Fuel pressure",
"Coolant pressure",
"lub oil temp",
"Coolant temp"
]
)
prediction = model.predict(sample)
print(prediction)
Business Value
- Reduces unexpected breakdowns
- Improves maintenance planning
- Helps optimize service schedules
- Supports data-driven fleet and engine reliability decisions
