Case Study
Automotive Predictive Maintenance
Automotive4 min
Reducing downtime and maintenance costs with ML-powered diagnostics and real-time equipment health monitoring.
Project Overview
Manufacturing facilities faced costly production halts due to unpredictable equipment failures and reactive maintenance. We implemented ML models trained on IoT sensor data (vibration, temperature, load) to forecast failures, unified plant data in the cloud, and delivered real-time dashboards for proactive interventions.
Key Challenges
Unplanned Equipment Failures
Inefficient Preventive Maintenance
Data Silos Across Plants
Limited Fault Prediction
Lack of Real-Time Monitoring
Our Solution
- Time-Series Failure Prediction
- Centralized Data Integration
- Real-Time Dashboards
- MLOps Deployment
Key Technologies
LSTMScikit-learnAzure IoT HubDatabricksPower BIAzure Cloud
Impact
60%
Reduction in unplanned downtime
40%
Decrease in maintenance costs
90%
Increase in fault detection accuracy
Client Testimonial
“We can now predict issues days before they happen. The savings and production stability we’ve gained are transformative.”
— Director of Operations, Global Automotive Manufacturer