UC-Predictive Maintenance Dashboard

Description:

This use case describes an AI-powered Predictive Maintenance Dashboard that allows users to upload IoT sensor data and receive intelligent insights into potential equipment failures. The system leverages machine learning models to analyze data in real time, generate predictive reports, and provide actionable alerts. Secure access control and user management are ensured through Azure AD B2C.

Actors:

• Maintenance Engineer – Uploads sensor data, views predictions, and schedules maintenance.

• Operations Manager – Reviews predictive reports and alerts for strategic planning.

• System Admin – Manages user access, roles, and system configurations.

• AI Model (Cognitive Services) – Processes sensor data and returns failure predictions.

Preconditions:

• The user is authenticated via Azure AD B2C.

• Valid sensor data (CSV/JSON) is available for upload.

• The AI model is trained and deployed via Cognitive Services or a custom Azure Function.

• Cosmos DB and other services are properly configured.

Flow of Events:

1. Login & Access - User logs into the web app using Azure AD B2C authentication.

2. Upload Data - User uploads sensor data through the dashboard (via Azure Static Web App frontend).

3. Data Processing - The uploaded data is sent to an Azure Function for validation and pre-processing.

4. Prediction Engine - The Azure Function invokes a trained AI model (via Cognitive Services or a custom model) to analyze data and detect failure risks.

5. Result Storage & Retrieval - Prediction results and insights are stored in Cosmos DB.

6. Visualization - Results are rendered on the dashboard with charts, failure probability, and equipment health trends.

7. Report Generation - Users can generate downloadable maintenance reports (PDF/CSV) from the dashboard.

8. Notification (Optional) - Alerts for critical equipment risks can be triggered via web-hook or email.

Postconditions:

• Predictions and reports are stored securely in the system.

• Maintenance engineers have actionable insights to plan preventive actions.

• System logs the activity for future auditing.

Benefits:

• Reduced equipment downtime through proactive planning.

• Minimized unexpected failures and maintenance costs.

• Centralized access to all maintenance intelligence and history.

• Scalable architecture using Azure's cloud-native services.

• Secure and role-based user access.

Tools & Technologies Used:

• Azure Static Web Apps – Frontend hosting for the dashboard.

• Azure Functions (Python) – Serverless compute for data processing and ML inference.

• Azure Cosmos DB – NoSQL storage for prediction results and metadata.

• Azure Cognitive Services – AI services for prediction models (if applicable).

• Azure Active Directory B2C – User authentication and secure access management.

• Python – Backend processing, data handling, and AI integration.