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https://github.com/jerry-felipe/ai-predictivemaintenancesystem

The AI-PMS Microservice uses AI to predict aircraft system failures before they occur, optimizing maintenance and enhancing safety. This modular microservice integrates with fleet management systems, providing real-time predictions and automated alerts for preventive maintenance interventions.
https://github.com/jerry-felipe/ai-predictivemaintenancesystem

aeronautical aeronautics aerospace aerospace-engineering ci-cd docker java logging oracle scalability

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The AI-PMS Microservice uses AI to predict aircraft system failures before they occur, optimizing maintenance and enhancing safety. This modular microservice integrates with fleet management systems, providing real-time predictions and automated alerts for preventive maintenance interventions.

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# AI - Predictive Maintenance System

The AI-PMS Microservice uses AI to predict aircraft system failures before they occur, optimizing maintenance and enhancing safety. This modular microservice integrates with fleet management systems, providing real-time predictions and automated alerts for preventive maintenance interventions.



# Features:

- Real-Time Data Processing: Gathers and processes data from aircraft sensors such as temperature, vibration, and pressure.

- Predictive Analytics: Uses machine learning models to predict equipment failure and schedule maintenance before issues occur.

- Alert System: Automatically sends notifications about predicted failures to maintenance teams through email or other communication channels.

- RESTful API: Provides a secure API for other systems to query the aircraft's health status and receive predictions.

- Scalability: The microservice is designed to scale easily in cloud or on-premise environments.

- Containerized: Dockerized to streamline deployment and ensure consistent environments across different stages of development.



# Benefits:

- Increased Aircraft Availability: By preventing unplanned downtime, it helps ensure that aircraft are ready for service, reducing maintenance delays.

- Cost Savings: Reduces costly emergency repairs and maximizes the lifecycle of aircraft components.

- Improved Safety: Proactively identifies potential failures, minimizing the risk of in-flight issues and improving safety.

- Efficient Resource Management: Optimizes maintenance scheduling, ensuring that resources are allocated efficiently and minimizing unnecessary downtime.



# Technologies:

- Backend: Java with Spring Boot for building RESTful APIs.

- Machine Learning: Python-based models using TensorFlow or PyTorch to predict system failures based on historical and real-time data.

- Database: Oracle Database for storing maintenance data, sensor readings, and predictive models.

- Containerization: Docker for packaging the microservice, ensuring easy deployment across environments.

- Orchestration: Kubernetes for managing the deployment and scaling of the microservice in production.



# Technological Stack Details:



1. Java & Spring Boot:  

   The microservice is developed using Java and Spring Boot to create a robust and scalable backend. Spring Boot's features like dependency injection, security, and data access simplify the development of microservices and provide a solid foundation for API endpoints.

   

2. Machine Learning (Python):  

   For predictive analytics, machine learning models are developed using Python with libraries such as TensorFlow or PyTorch. These models are trained on historical data from aircraft systems and used to predict potential failures based on sensor inputs.



3. Oracle Database:  

   Oracle is used for managing structured data such as maintenance records, sensor readings, and model performance metrics. It provides robust and scalable database management, enabling the microservice to store historical data, track trends, and handle large volumes of maintenance-related information.



4. Docker & Kubernetes:  

   Docker is used to containerize the microservice, ensuring that the application is portable and runs consistently across different environments. Kubernetes handles orchestration and scaling, ensuring that the microservice can scale up or down based on demand.



# System Workflow:



1. Data Collection:  

   Real-time sensor data is collected from aircraft systems (temperature, pressure, vibration, etc.) and sent directly to the microservice.



2. Data Processing & Prediction:  

   The system processes this data using pre-trained machine learning models to predict potential failures. The models analyze patterns and anomalies in sensor data to forecast component degradation or failure.

   

3. Alerts & Notifications:  

   If a potential failure is predicted, the microservice generates an alert and sends it to the relevant stakeholders (maintenance teams, operations) through email, API, or other communication channels.

   

4. Integration with Other Systems:  

   The RESTful API allows fleet management systems, maintenance platforms, or other services to query the microservice and receive up-to-date maintenance predictions and status reports.



# Installation & Setup:



1. Clone the Repository:  

   Clone the project repository to your local machine or server.

   '''bash

   git clone https://github.com/jerry-felipe/ai-pms-microservice.git

   '''



2. Build the Microservice:  

   Navigate to the project directory and build the microservice using Maven or Gradle.

   '''bash

   cd ai-pms-microservice

   mvn clean install

   '''



3. Set Up the Environment:

   - Ensure Java 11 or higher is installed.

   - Set up Oracle Database and create a database for the application.

     - You can use Oracle XE for local development or connect to a cloud-based Oracle database.

   - Configure Docker to containerize the application.

   

4. Run the Application:  

   Once everything is set up, run the microservice with the following command:

   '''bash

   java -jar target/ai-pms-microservice.jar

   '''



5. Access the API:  

   The microservice will be available at:

   '''url

   http://localhost:8080/api/v1/predictions

   '''



# Contributing:

We welcome contributions to improve the AI-PMS Microservice. If you'd like to suggest features, report bugs, or contribute to the codebase, please fork the repository and submit a pull request.



# License:

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.



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For more information, contact [email protected].