Vehicle Equipment Manufacturer facing random failures in a critical bolt installation machine. Unscheduled downtime on a key assembly line machine due to inconsistent bolt installation failures, leading to production delays and increased costs.

• Sensor Data: Collected real-time data from the bolt machine's sensors (torque, pressure, vibration, temperature, cycle time).

• Maintenance Logs: Gathered historical records of past failures, repair times, and parts replaced.

• Production Data: Integrated production line data including batch numbers and operator shifts.

• Cleaning: Addressed missing values, outliers, and normalized data for consistent analysis.

• Utilized a secure Microsoft Azure environment for scalable data ingestion and storage.

• Implemented robust encryption of data at rest and in transit for raw data analytics safety.

• Applied identity and access management (IAM) with multi-factor authentication and RBAC to control access.

• Developed interactive PowerBI dashboards to visualize machine performance metrics, failure trends, and sensor readings over time.

• Created PowerPoint presentations with key findings for easy understanding across departments.

• Identified specific sensor anomalies (e.g., sudden torque drops, atypical vibration patterns) preceding failures.

• Correlated sensor data patterns with historical maintenance logs to diagnose specific component issues.

• Built machine learning models to predict potential machine failures based on identified pre-failure signatures in sensor data.

• Forecasted the likelihood of a bolt installation failure within a specific time frame, enabling proactive interventions.

• Recommended optimal maintenance schedules and specific component replacements before predicted failures occurred.

• Suggested ideal operating parameters to minimize wear and tear based on real-time conditions.

• Alerts were automatically triggered for maintenance teams when a high-risk failure was predicted.

• Maintenance tasks were shifted from reactive to proactive based on historical data, allowing scheduled downtimes with reduced impact on production.

• Reduced unscheduled machine downtime by 40%, significantly increasing production uptime.

• Decreased maintenance costs by 25% due to fewer emergency repairs and optimized part replacement.

• Improved overall equipment effectiveness (OEE) by 15%, leading to higher throughput and reduced waste.