Description

Course Description:

The Predictive Maintenance with Iota  is designed to equip professionals with the skills and knowledge required to implement predictive maintenance strategies using IoT (Internet of Things) technologies. This course focuses on leveraging real-time data from connected devices, sensors, and machinery to predict equipment failures, optimize maintenance schedules, and reduce operational downtime. Participants will gain hands-on experience in data collection, analysis, and predictive modeling using Iota platforms, enabling smarter decision-making and enhanced asset performance. By the end of the course, learners will be proficient in designing predictive maintenance solutions that drive efficiency, reliability, and cost savings across industries.

Key Features of Course Divine:

• Collaboration with E‑Cell IIT Tirupati
• 1:1 Online Mentorship Platform
• Credit-Based Certification
• Live Classes Led by Industry Experts
• Live, Real-World Projects
• 100% Placement Support
• Potential Interview Training
• Resume-Building Activities

Career Opportunities After Predictive Maintenance With IOTA Certified Course:

• Predictive Maintenance Engineer
• IoT Solutions Engineer
• Data Analyst – Industrial IoT
• Maintenance Optimization Specialist
• Condition Monitoring Engineer
• Industrial Automation Engineer
• Reliability Engineer
• Asset Performance Management Specialist
• IoT Application Developer
• Operations Analyst – Manufacturing
• Maintenance Data Scientist
• Smart Factory Consultant
• Equipment Health Monitoring Specialist
• Production Efficiency Analyst
• Industrial Data Engineer
• IoT System Integration Specialist
• Predictive Analytics Consultant
• Industrial IoT Project Manager
• Remote Monitoring Engineer
• Plant Maintenance Manager

Essential Skills you will Develop Predictive Maintenance With IOTA Certified Course:

• IoT device integration and connectivity
• Sensor data acquisition and management
• Predictive analytics for equipment health
• Condition monitoring techniques
• Failure pattern identification
• Data preprocessing and cleaning
• Time-series data analysis
• Machine learning for predictive maintenance
• Root cause analysis of equipment failures
• Asset performance optimization
• Cloud-based IoT platform usage
• Dashboard creation and visualization
• Real-time monitoring and alerting
• Remote diagnostics of machinery
• Predictive maintenance strategy planning
• Workflow automation in maintenance processes
• Data-driven decision making
• Troubleshooting IoT device issues
• Performance reporting and insights generation
• Integration of IoT with ERP/maintenance software

Tools Covered:

• Iota IoT Platform
• Arduino / Raspberry Pi (for IoT prototyping)
• Python Programming
• MATLAB / Simulink
• SQL Databases
• Microsoft Excel / Power BI
• Tableau
• Grafana
• Node-RED
• MQTT Protocol Tools
• SCADA Systems
• PLC Programming Tools
• Azure IoT Hub
• AWS IoT Core
• Edge Computing Devices
• Sensors (Vibration, Temperature, Pressure)
• Predictive Analytics Libraries (scikit-learn, TensorFlow)
• REST APIs for IoT Integration
• Cloud Data Storage Tools
• Condition Monitoring Software

Syllabus:

Module 1: Introduction to Predictive Maintenance & IoT Basics of maintenance strategies Importance of predictive maintenance in industries Overview of IoT and its role in asset management

Module 2: IoT Architecture & Devices IoT system components Sensors, actuators, and edge devices Connectivity protocols (MQTT, HTTP, CoAP)

Module 3: Data Acquisition & Sensor Integration Collecting data from machines Integration of sensors with IoT platforms Data formats and storage.

Module 4: Data Preprocessing & Cleaning Handling missing or noisy data Data normalization and transformation Feature extraction for predictive modeling.

Module 5: Condition Monitoring & Fault Detection Vibration, temperature, and pressure analysis Early fault detection techniques Threshold-based and statistical methods.

Module 6: Predictive Analytics & Machine Learning Time-series analysis Regression, classification, and anomaly detection Building predictive maintenance models.

Module 7: IoT Platform Implementation (Iota) Setting up Iota dashboards Real-time monitoring and alerting Cloud integration and data visualization.

Module 8: Maintenance Optimization & Strategy Planning Scheduling maintenance based on predictions Reducing downtime and operational costs Case studies and industry examples.

Module 9: Advanced Tools & Technologies SCADA integration Edge computing for real-time processing AI and IoT convergence.

Module 10: Capstone Project & Industry Applications Hands-on live project with predictive maintenance scenario Reporting insights and recommendations Best practices for industrial implementation.

Industry Projects:

• Vibration analysis for rotating machinery to predict failures
• Predictive maintenance of HVAC systems in smart buildings
• Temperature-based failure prediction for industrial motors
• Real-time monitoring of conveyor belt systems
• IoT-based fault detection in pumps and compressors
• Predictive maintenance for manufacturing robotic arms
• Monitoring and maintenance scheduling of wind turbines
• Asset health monitoring for power transformers
• Predicting failures in CNC machines using sensor data
• Remote monitoring of refrigeration units in cold storage
• Predictive analytics for vehicle fleet maintenance
• Monitoring and optimizing air compressor systems
• IoT-enabled maintenance of industrial boilers
• Predictive maintenance for water treatment pumps
• Real-time condition monitoring for assembly line machinery
• Sensor-based monitoring of bearings and shafts
• Energy-efficient predictive maintenance for industrial motors
• Cloud-based monitoring of factory equipment
• Fault prediction in elevators and escalators
• Industrial IoT solution for reducing unplanned

Who is this program for?

• Mechanical Engineers
• Electrical Engineers
• Industrial Automation Professionals
• Maintenance Engineers
• Reliability Engineers
• IoT Enthusiasts
• Data Analysts interested in industrial applications
• Production Managers
• Plant Managers
• Operations Managers
• Manufacturing Engineers
• Industrial IoT Consultants
• Robotics Engineers
• Technicians involved in machine maintenance
• Asset Performance Management Specialists
• Control System Engineers
• Students pursuing IoT or Industrial Automation courses
• Professionals aiming to upskill in predictive maintenance
• Professionals in energy and utilities sectors
• Anyone looking to implement smart maintenance

How To Apply:

Mobile: 9100348679

Email: coursedivine@gmail.com