Developed a comprehensive industrial machine monitoring system using ASP.NET MVC and IoT technology, featuring real-time visualisation and predictive maintenance capabilities. The system successfully integrates IoT signals with dynamic reporting tools, enabling efficient machine failure detection and maintenance management.

Overview

The project addresses the critical need for remote industrial machinery monitoring through a sophisticated web-based solution. Utilising ASP.NET MVC architecture and IoT sensor integration, the system provides real-time insights into machine performance and potential failures. The implementation of dynamic visualisation tools enables maintenance teams to identify patterns and trends promptly, significantly improving operational efficiency and reducing downtime.

Technologies

• Framework: ASP.NET MVC
• Frontend: HTML5, CSS3, JavaScript
• Visualisation: ChartJS
• Backend: C#
• Database: SQL Server
• IoT Integration: Arduino
• Real-time Processing: SignalR
• Authentication: ASP.NET Identity
• Reporting: SSRS
• Version Control: Azure DevOps

Implementation Details

1. IoT Integration Framework

• Developed IoT signal collection system
• Implemented real-time data processing pipeline
• Created sensor data validation mechanisms
• Established data streaming protocols
• Developed fault detection algorithms

2. Web Application Development

• Created responsive MVC architecture
• Implemented real-time dashboard updates
• Developed user authentication system
• Created role-based access control
• Implemented secure data transmission

3. Visualisation System

• Implemented dynamic ChartJS visualisations
• Created interactive reporting modules
• Developed trend analysis tools
• Created custom gauge components
• Implemented real-time updates

4. Performance Monitoring

• Created machine health monitoring system
• Implemented predictive maintenance alerts
• Developed performance metrics tracking
• Created anomaly detection system
• Implemented logging and monitoring

Analysis Features

• Real-time performance monitoring
• Failure pattern recognition
• Equipment efficiency analysis
• Historical trend analysis
• Alert management system
• Downtime analysis
• Predictive maintenance scheduling

Key Results

• Implemented comprehensive and scalable thermal power plant machine monitoring solution
• Developed real-time visualisation system
• Created predictive maintenance capabilities
• Established efficient alert mechanisms
• Improved machine uptime tracking
• Reduced maintenance response time and enhanced operational efficiency

Skills Gained

• IoT system integration
• Web application development
• Real-time data processing
• Visualisation implementation
• Industrial automation
• Database management

Impact

The system significantly improves industrial machinery management by providing real-time monitoring and predictive maintenance capabilities. The implementation demonstrates the effective use of modern web technologies and IoT integration in industrial applications, offering a template for future industrial monitoring systems. The project’s success in reducing downtime and improving maintenance efficiency showcases the value of digital transformation in industrial operations.