A Study on SmartMaint – AI-Enabled Predictive Maintenance for Manufacturing

Equipment downtime and inefficiencies are among the most significant losses manufacturers face. This project is designed to overcome these challenges by developing a smart equipment monitoring system. It will use IoT sensors and AI-driven predictive maintenance models to allow for real-time tracking of equipment performance, early detection of potential failures, and proactive planning of maintenance.

 Week 1-2: Requirement Analysis and System Design

Activities: Gather requirements, define project objectives and design system architecture.

Deliverables: System architecture and project plan.

 Week 3-4: Sensor Deployment and Data Collection Setup

Activities: Install IoT sensors and set up the data collection pipeline.

Deliverables: Functional IoT sensor network.

 Week 5-6: AI Model Development

Activities: Develop and train predictive maintenance models using historical data.

Deliverables: Tested AI models for failure prediction.

 Week 7-8: System Integration and Dashboard Design

Activities: Integrate AI models and sensors with the monitoring dashboard.

Deliverables: Interactive monitoring dashboard prototype.

 Week 9-10: Testing and Validation

Activities: Test system functionality in real manufacturing scenarios and validate performance.

Deliverables: System testing reports and performance validation.

 Week 11-12: Deployment and Training

Activities: Deploy the system on the manufacturing floor and train users.

Deliverables: Live system and trained staff.