Study Unit
Instrumentation And Control Technology: Tools And Techniques
Topics 10
Introduction to Instrumentation and Control Technology
This topic will provide an overview of instrumentation and control technology, its importa...
Measurement Instruments and Sensors
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Control Systems and Components
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Data Acquisition and Signal Conditioning
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Programmable Logic Controllers (PLCs)
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Supervisory Control and Data Acquisition (SCADA) Systems
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Human-Machine Interface (HMI)
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Industrial Networking and Communication Protocols
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Calibration and Maintenance of Instruments
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Emerging Trends in Instrumentation and Control Technology
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Unit Outline 60h
Learning Objectives
5 objectives- Understand the fundamental principles and importance of instrumentation and control technology in industrial processes.
- Identify and explain various measurement instruments, sensors, and control system components.
- Gain proficiency in data acquisition, signal conditioning, and programming of PLCs for industrial automation.
- Describe SCADA systems, HMIs, industrial networking, and protocols used in control environments.
- Analyze calibration, maintenance procedures, and emerging trends shaping the future of instrumentation and control.
Content Outline
PreviewUnit 3395: Instrumentation and Control Technology
1. Introduction to Instrumentation and Control Technology
- Definition and scope
- Importance in various industries (manufacturing, energy, pharmaceuticals, etc.)
- Role in monitoring and controlling industrial processes
- Historical development and evolution
2. Measurement Instruments and Sensors
- Types of measurement instruments
- Pressure, temperature, flow, level, and analytical sensors
- Principles of operation for common sensors
- Resistive, capacitive, inductive, piezoelectric, thermocouples, RTDs
- Applications and selection criteria
- Sensor calibration and error sources
3. Control Systems and Components
- Overview of control system types
- Open loop vs closed loop control
- Controllers
- PID control principles and tuning
- Actuators and final control elements
- Valves, motors, solenoids, relays
- Feedback and feedforward control
- System stability and performance considerations
4. Data Acquisition and Signal Conditioning
- Data acquisition systems (DAQ)
- Components and architecture
- Signal conditioning techniques
- Amplification, filtering, isolation, conversion (analog to digital)
- Noise reduction and signal integrity
- Sample and hold, multiplexing
5. Programmable Logic Controllers (PLCs)
- Introduction to PLCs
- Hardware components and architecture
- Programming languages
- Ladder logic, function block, structured text
- Input/output processing
- PLC operation and troubleshooting
- Applications in industrial automation
6. Supervisory Control and Data Acquisition (SCADA) Systems
- SCADA system architecture
- Master station, RTUs, PLCs, communication networks
- Communication protocols
- Modbus, DNP3, IEC 61850
- Data acquisition, monitoring, and control functions
- Alarm management and historical data logging
7. Human-Machine Interface (HMI)
- Purpose and importance of HMI
- Types of HMIs
- Touchscreens, graphical displays, software interfaces
- Design principles for effective HMI
- Integration with control systems
- Visualization of process data and control commands
8. Industrial Networking and Communication Protocols
- Overview of industrial networks
- Fieldbus, Ethernet/IP, Profibus, CAN
- Communication protocols and standards
- Network topologies and architectures
- Data exchange and remote monitoring
- Cybersecurity considerations in instrumentation networks
9. Calibration and Maintenance of Instruments
- Importance of calibration
- Calibration procedures and standards
- Maintenance strategies
- Preventive, predictive, corrective
- Troubleshooting instrumentation and control systems
- Documentation and record keeping
10. Emerging Trends in Instrumentation and Control Technology
- Internet of Things (IoT) integration
- Predictive maintenance and condition monitoring
- Industry 4.0 concepts
- Advanced sensors and smart instrumentation
- Wireless sensor networks
- Artificial intelligence and machine learning applications
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