Predictive Maintenance Techniques

About the Course

Predictive maintenance is a philosophy or attitude that, simply stated, uses the actual operating condition of plant equipment and systems to optimize total plant operation. A comprehensive predictive maintenance management program uses the most cost- effective tools (e.g., vibration monitoring, thermography, tribology) to obtain the actual operating condition of critical plant systems and based on this actual data schedules all maintenance activities on an as-needed basis including predictive maintenance in a comprehensive maintenance management program optimises the availability of process machinery and greatly reduces the cost of maintenance. It also improves the product quality, productivity, and profitability of manufacturing and production plants.

This 5-day comprehensive Predictive Maintenance Techniques training course will equip professionals with the knowledge and practical skills to implement effective predictive maintenance strategies. Delegates will explore the principles, technologies, and methodologies of predictive maintenance, enabling them to enhance equipment reliability, reduce downtime, and optimise maintenance costs. It integrates theoretical concepts with hands-on exercises and real-world case studies to provide a holistic understanding of predictive maintenance practices.

Core Objectives

This training course will assist the delegates in gaining a deep understanding of predictive maintenance methodologies, technologies, and implementation strategies. They will be equipped to design and execute predictive maintenance programs, contributing to increased equipment reliability, reduced downtime, and overall operational efficiency in various industrial settings.

The delegates will achieve the following objectives:

• Learn the key concepts and methodologies used in predictive maintenance
• Gain proficiency in selecting appropriate predictive maintenance techniques based on equipment and operational requirements
• Develop skills in data acquisition and analysis for predictive maintenance
• Learn to interpret various types of data (e.g., vibration, temperature, oil analysis) to detect equipment abnormalities
• Understand the implementation process of a predictive maintenance program within an organisation
• Gain knowledge of predictive maintenance software tools and systems
• Develop skills in setting up and configuring predictive maintenance equipment and sensors
• Learn to perform routine inspections and condition monitoring activities to identify potential equipment failures
• Understand the role of predictive maintenance in optimising equipment reliability, reducing downtime, and minimising maintenance costs
• Gain insights into predictive maintenance best practices and case studies from different industries
• Develop skills in developing and executing predictive maintenance schedules and plans
• Learn to analyse historical maintenance data to improve predictive maintenance strategies
• Understand the integration of predictive maintenance with other maintenance strategies (e.g., preventive maintenance, reliability-centered maintenance)
• Gain knowledge of emerging trends and technologies in predictive maintenance
• Develop problem-solving skills for diagnosing equipment faults and failures using predictive maintenance techniques

Training Approach

The training approach will be interactive and hands-on, combining theoretical sessions, practical exercises, case studies, and group discussions. Delegates will engage in real-world simulations to reinforce theoretical concepts, ensuring a practical and immersive learning experience.

The Attendees

Likewise, it will be valuable to the professionals but not limited to the following:

• Maintenance Engineers
• Reliability Engineers
• Plant Managers
• Predictive Maintenance Technicians
• Asset Managers
• Maintenance Supervisors

Daily Discussion

DAY ONE: INTRODUCTION TO PREDICTIVE MAINTENANCE

• Overview and Benefits
• Difference from Preventive Maintenance
• Understanding Failure Patterns
• Data Collection
• Types of Data to Collect
• Sensors and Data Acquisition
• Automated Data Collection
• Cloud/Software Options

DAY TWO: VIBRATION ANALYSIS

• Machine Vibration Fundamentals
• Data Interpretation
• Common Machine Faults
• Determining Repair Priorities
• Time Waveform Analysis
• Frequency Analysis
• Spectral Analysis
• Enveloping
• Oil Analysis
• Sampling Techniques
• Testing Methods
• Interpretation Guidelines 

DAY THREE: OIL ANALYSIS

• How oil analysis works?
• Key Parameters Analysed and Interpretation of Results
• Viscosity – Measures the Oil's Thickness, Affecting Its Lubricating Ability
• Total Acid Number (TAN) and Total Base Number (TBN)
• Particle Count and Wear Metals
• Water Content
• Additive Concentrations
• Benefits of Oil Analysis

DAY FOUR: NON-DESTRUCTIVE TESTING

• Visual Inspecting
• Infrared Thermography
• Ultrasonic Testing
• Motor Circuit Testing
• Interaction with Material
• Signal Analysis
• Indication of Flaws
• Where Ultrasound Testing is Applicable
• Benefits in Early Fault Detection and Diagnostics
• Limitations of Ultrasound Testing

DAY FIVE: OVERALL PROGRAM IMPLEMENTATION

• Building In-house vs Outsourced
• Creating Routes and Schedules
• Reporting and Analysis
• Cost Justification
• Software Options
• Program Optimisation