The IoT for Mechanical Systems Certified Course is designed to equip engineers and professionals with the knowledge and skills required to integrate Internet of Things (IoT) technologies into mechanical systems. Participants will learn how IoT enables real-time monitoring, predictive maintenance, and intelligent automation in mechanical devices and industrial machinery. The course covers sensor integration, data acquisition, cloud connectivity, and analytics specific to mechanical applications, providing hands-on experience with cutting-edge tools and platforms. By the end of the program, learners will be able to design, implement, and optimize IoT-enabled mechanical systems, enhancing operational efficiency, reducing downtime, and driving innovation in industries such as manufacturing, automotive, and robotics.
Module 1: Introduction to IoT and Mechanical Systems Overview of IoT and its impact on mechanical engineering Applications in manufacturing, automotive, and robotics Understanding smart mechanical systems.
Module 2: Sensors and Actuators for Mechanical Systems Types of sensors: temperature, pressure, vibration, proximity, motion Actuators: motors, solenoids, and pneumatic/hydraulic systems Sensor selection and calibration.
Module 3: Microcontrollers and Embedded Programming Introduction to Arduino and Raspberry Pi Programming IoT devices using C/C++ and Python Interfacing sensors and actuators.
Module 4: Communication Protocols and Connectivity Wi-Fi, Bluetooth, Zigbee, and MQTT protocols Device-to-device and device-to-cloud communication Data transmission and security considerations.
Module 5: Data Acquisition and Signal Processing Collecting real-time data from mechanical systems Filtering and processing sensor data Understanding signal conditioning.
Module 6: Cloud Platforms and IoT Integration Connecting mechanical systems to cloud platforms (ThingSpeak, AWS IoT, Blynk) Real-time monitoring, dashboards, and alerts Data storage and management.
Module 7: Data Analytics and Predictive Maintenance Analyzing IoT data using Python, MATLAB, or Power BI Predictive maintenance strategies Fault detection and performance optimization.
Module 8: Industrial Automation with IoTm Integrating IoT with PLCs and SCADA systems Smart factories and automated mechanical processes mCase studies from manufacturing and robotics.
Module 9: Simulation and Digital Twin Concepts Using CAD/Simulink for mechanical system simulation Digital twin implementation for predictive analytics Monitoring virtual systems for real-world insights.
Module 10: Capstone Project and Hands-on Applications End-to-end IoT project implementation Designing, prototyping, and deploying an IoT-enabled mechanical system Presentation and performance evaluation
Mobile: 9100348679
Email: coursedivine@gmail.com
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