Design, develop, and implement an IoT-based software solution to monitor the health and performance of manufacturing equipment in real-time, enabling predictive maintenance and minimizing downtime.

Develop and implement a predictive maintenance algorithm using machine learning (ML) or statistical methods to analyze sensor data and predict when maintenance is needed to prevent failures.

System Requirement Gathering and Analysis

• Outcomes: Establish a clear understanding of the requirements for the software system, focusing on key functionality such as real-time equipment monitoring, data collection from IoT devices, and predictive maintenance alerts.
• Deliverables:
• System requirements specification document.
• List of equipment and sensors to be monitored (e.g., vibration sensors, temperature sensors, pressure sensors).
• User stories for software features (e.g., dashboard views, alert notifications, equipment performance reports).

Software Architecture and Design

• Outcomes: Design the overall software architecture, ensuring it integrates seamlessly with IoT sensors and devices and provides easy-to-use interfaces for operators and maintenance teams.
• Deliverables:
• Software architecture diagram showing the integration with IoT devices, data collection points, and the backend database.
• Wireframes or mockups for the user interface (UI) of the dashboard for equipment monitoring and reporting.
• Data flow diagrams to illustrate how data will be processed and displayed in real time.

IoT Integration and Data Collection

• Outcomes: Integrate IoT sensors into the software system and establish a reliable mechanism for collecting and transmitting data to the cloud or local database for processing.
• Deliverables:
• Code for data collection and integration with IoT devices.
• Test results showing the accuracy of the data transmission and reception (e.g., sensor data like temperature, vibration).
• A report on the data synchronization process between the IoT devices and the software platform.

Predictive Maintenance Algorithm Development

• Outcomes: Develop and implement a predictive maintenance algorithm using machine learning (ML) or statistical methods to analyze sensor data and predict when maintenance is needed to prevent failures.
• Deliverables:
• Predictive maintenance algorithm code (e.g., anomaly detection or failure prediction).
• Documentation on the model’s performance (e.g., accuracy, precision).
• Visual representation of maintenance predictions (e.g., graphs showing predicted vs. actual failure incidents).

Software Development and User Interface Implementation

• Outcomes: Develop the front-end and back-end components of the software system, ensuring a user-friendly interface for operators and maintenance personnel to monitor equipment health and respond to alerts.
• Deliverables:
• Fully functional software with a dashboard for monitoring equipment and performance.
• Web or mobile interface allowing users to interact with real-time data, view alerts, and generate maintenance reports.
• User manual and installation guide for the software system.

System Testing, Debugging, and Optimization

• Outcomes: Test the software to ensure reliability, accuracy, and efficiency, making necessary adjustments for optimization.
• Deliverables:
• Test protocols covering real-time monitoring, sensor data accuracy, system stability, and user interface interactions.
• A report detailing any bugs found and how they were resolved.
• Recommendations for future improvements based on system performance.

Final Report and Presentation

• Outcomes: Compile a final report detailing the design, development, and testing processes, and present findings to the industry partner.
• Deliverables:
• PowerPoint presentation summarizing the software’s functionality, testing results, and predictive maintenance capabilities.
• Detailed final report covering the software development lifecycle, challenges encountered, and deployment recommendations.
• A video demo showcasing how the system works in a simulated manufacturing environment.