Course Details

Objective

By the end of the course, participants will be able to understand

• Understand the fundamentals of motor and transformer operations, common failure modes, and their impact on oil and gas operations.
• Learn the latest techniques for condition monitoring, including vibration analysis, thermal imaging, electrical testing, and oil diagnostics.
• Identify early warning signs of equipment failure and apply diagnostic methods to detect faults before they lead to expensive downtime.
• Develop predictive maintenance strategies to improve operational reliability and safety, leveraging condition monitoring data for proactive maintenance scheduling.

Content

Day 1: Introduction to Condition Monitoring and Key Concepts

• Introduction to Condition Monitoring
  • Importance of condition monitoring in the oil and gas industry
  • Benefits of predictive maintenance: Reduced downtime, cost savings, safety
  • Overview of condition-based monitoring technologies and tools
  • Types of equipment commonly monitored: Motors, transformers, pumps, compressors, etc.
• Motor Fundamentals and Common Failures
  • Motor types: Induction, synchronous, DC motors
  • Common motor failures: Bearing faults, insulation failure, electrical imbalances
  • Early warning signs of motor failure: Vibration, temperature rise, unusual noise, electrical anomalies
  • Failure modes: Electrical, mechanical, thermal
• Transformer Fundamentals and Common Failures
  • Transformer types used in the oil and gas industry
  • Common transformer issues: Overheating, insulation degradation, short circuits, oil contamination
  • Early warning signs: Changes in sound, temperature rise, leakage of insulating oil
  • Key parameters for monitoring: Voltage, current, temperature, oil level

Day 2: Vibration Monitoring for Motors

• Vibration Theory and Measurement
  • Principles of vibration and its relationship with machine health
  • Types of vibration (radial, axial, tangential) and their significance
  • Vibration sensors: Accelerometers, velocity sensors, displacement sensors
  • Setting up vibration monitoring systems for motors
• Vibration Data Interpretation
  • Vibration signature analysis: Peaks, harmonics, and frequencies
  • Identifying motor fault signatures (e.g., imbalance, misalignment, bearing issues)
  • Severity classification and diagnostic thresholds

Day 3: Electrical Monitoring for Motors and Transformers

• Electrical Signature Analysis
  • Introduction to electrical signature analysis (ESA)
  • Motor current signature analysis (MCSA) for fault detection
  • Identifying fault types: Stator, rotor, and mechanical faults using electrical signals
• Thermography and Infrared Inspection
  • Using infrared thermography for motor and transformer inspections
  • Identifying hot spots and insulation failures
  • Interpretation of thermal images to detect issues before they lead to failure

Day 4: Oil and Insulation Monitoring for Transformers

• Oil Testing and Monitoring
  • Importance of transformer oil in heat dissipation and insulation
  • Methods for monitoring oil quality: Dielectric strength, moisture content, acidity, and gas analysis
  • analysis of oil samples to detect issues like overheating or contamination
• Insulation Health Monitoring
  • Insulation resistance testing (IR) and Polarization Index (PI)
  • Partial discharge detection and its significance
  • Power factor testing for insulation condition

Day 5: Advanced Techniques, Troubleshooting, and Maintenance Planning

• Advanced Monitoring Techniques
  • Acoustic emission testing for early-stage motor faults
  • Online monitoring systems and integration with SCADA
  • Machine learning and AI in predictive maintenance
• Troubleshooting and Failure Diagnosis
  • Case studies on typical motor and transformer failures in oil and gas
  • Step-by-step fault identification process using various condition monitoring tools
  • Root cause analysis (RCA) techniques and corrective actions
• Maintenance Strategy and Planning
  • Developing a comprehensive condition monitoring strategy
  • Creating predictive maintenance schedules based on condition data
  • Integration of monitoring data with the enterprise asset management system (EAMS)
• Course Wrap-up

NOTE:

Pre-& Post Tests will be conducted.

Case Studies, Group Exercises, Group Discussions, Last Day reviews, and assessments will be carried out.

                                                              ………………………………...............

Methodology

A highly interactive combination of lectures and discussion sessions will be managed to maximize the amount and quality of information and knowledge transfer. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. You will also be encouraged to raise your own questions and to share in the development of the right answers using your own analysis and experiences.  Tests of multiple-choice type will be made available on daily basis to examine the effectiveness of delivering the course.

Very useful Course Materials will be given.

• 30% Lectures
• 30% Workshops and work presentation
• 20% Group Work& Practical Exercises
• 20% Videos& General Discussions