Predictive Maintenance for Machines
Sensor data + ML model predicting equipment failure risk.
Case Study
Problem Statement
Unplanned machine breakdowns cause costly downtime in manufacturing; most plants still rely on scheduled (time-based) maintenance instead of condition-based maintenance.
Our Solution
Using vibration, temperature, and runtime-hour data (real or simulated dataset), a machine learning model predicts the probability of failure within a given window, so maintenance can be scheduled proactively.
Features
- ✓Sensor data preprocessing and feature engineering
- ✓ML model (Random Forest / XGBoost) trained on failure-labeled data
- ✓Live dashboard showing current risk score per machine
- ✓Alert threshold configuration
- ✓Model evaluation report (precision/recall, confusion matrix)
Technology Stack
How It's Built
- 1
Data pipeline cleans and engineers features from raw sensor logs
- 2
Training script fits and validates the classification model
- 3
Streamlit dashboard loads the trained model and scores live/sample inputs
- 4
Report module exports evaluation metrics for your documentation
FAQ
Is real sensor data required?
No — a well-documented public dataset is used, and we explain how to plug in your own sensor readings if you have access to hardware.