A scalable, production-ready system designed to monitor industrial machines and predict potential failures using LSTM-based time-series analysis and a Clean Architecture backend built with ASP.NET Core.
The system processes real-time data, detects anomalies, and enables proactive decision-making through intelligent predictions and structured backend design.
- 📡 Real-time machine data processing
- 🤖 AI-based failure prediction (LSTM)
- 🔔 Automated alerting for abnormal behavior
- 🔐 Secure APIs using JWT authentication
- ⚡ Modular and scalable backend design
This project follows Clean Architecture (Onion Architecture) to ensure clear separation between business logic and infrastructure concerns.
┌──────────────────────────────┐
│ Presentation │ Controllers, Hubs, Filters
├──────────────────────────────┤
│ Application │ Services, DTOs, Validators
├──────────────────────────────┤
│ Infrastructure │ Repositories, Data, EF Core
├──────────────────────────────┤
│ Domain │ Core Entities & Interfaces
└──────────────────────────────┘
Inner layers remain independent from external frameworks, ensuring flexibility and long-term maintainability.
Graduation_Project/
├── Core/ # Domain models & interfaces
├── Data/ # DbContext & configurations
├── Extensions/ # Dependency injection setup
├── Filters/ # Cross-cutting concerns
├── Hubs/ # Real-time communication (SignalR)
├── Migrations/ # Database evolution
├── Modules/ # Feature-based structure
│ └── [Module]/
│ ├── DTOs/
│ ├── Repository/
│ ├── Service/
│ ├── Validators/
│ └── Controller
└── GlobalUsings.cs
Features are implemented using a Vertical Slice approach, where each module encapsulates:
- API contracts (DTOs)
- Data access logic
- Business rules
- Validation
- Endpoints
This structure improves scalability, readability, and team collaboration.
- Repository & Service layers for clean separation
- DTOs to isolate domain from API contracts
- FluentValidation for structured input validation
- SignalR for real-time updates
- EF Core Migrations for controlled database changes
- LSTM model trained on time-series machine data
- Detects patterns and predicts failures early
- Integrated with backend for seamless inference
- ASP.NET Core (.NET 8)
- Entity Framework Core
- SQL Server
- SignalR
- FluentValidation
- Python (LSTM)
1.Machine sensors send real-time data 2. Backend processes and stores data 3. Data is passed to LSTM model for prediction 4. System detects anomalies or failures 5. Alerts are triggered via notifications/dashboard 6. Results are available for monitoring systems
- Clean, scalable architecture ready for extension
- AI-driven predictive capabilities
- Real-time communication support
- Secure and maintainable backend
- Containerization & cloud deployment
- Advanced analytics dashboard
- Direct IoT integration
This project demonstrates strong capabilities in software architecture, backend development, and AI integration within a real-world use case.