Condition Based Maintenance Masterclass from Basics to AI

Practical CBM Maintenance: predictive, condition monitoring, diagnostics, industry 4.0, AI, and real power plant cases

What you’ll learn
Understand the evolution of maintenance strategies, from reactive and preventive maintenance to condition-based, predictive, and prescriptive maintenance.
The fundamentals and objectives of Condition-Based Maintenance (CBM).
Gain practical knowledge of key CBM techniques: vibration analysis, thermography, ultrasonic testing, oil analysis, and electrical signature analysis.
Analyze the current global maintenance landscape and identify gaps between traditional and modern practices.
Recognize key challenges in CBM adoption, including operational, organizational, and financial barriers.
Learn structured frameworks and strategies to overcome challenges and build a realistic CBM roadmap.
Differentiate between CBM, predictive maintenance, prescriptive maintenance, and hybrid maintenance models.
Interpret real-world case studies showing improvements in reliability, availability, and maintenance cost reduction.
Understand how emerging technologies such as AI, machine learning, IoT, and digital twins support modern CBM systems.
Develop a step-by-step CBM implementation roadmap applicable to their own plant or organization and Real-world CMMS software demo and workflow

Requirements
Basic understanding of industrial or power plant equipment (such as turbines, generators, transformers, pumps, or rotating machinery).
Familiarity with basic maintenance concepts like corrective and preventive maintenance.
Basic engineering knowledge (mechanical, electrical, or instrumentation fundamentals).
No advanced mathematics, coding, or data science background is required.
A willingness to learn modern maintenance practices and improve asset reliability.

Description
Condition-Based Maintenance (CBM) for Energy Projects by EOM is a practical, industry-oriented course that teaches you how to design and implement a condition-based maintenance strategy to improve reliability, reduce unplanned downtime, and optimize maintenance costs and spare parts management.

What this course will help you achieve

By the end of this training, you will be able to

• Understand the role of CBM in modern maintenance and reliability programs

• Apply the full CBM workflow: Monitor → Detect → Diagnose → Prognose → Act

• Choose the right CBM techniques based on asset criticality and failure modes

• Define alarm thresholds, reduce false alarms, and improve decision-making

• Build a realistic CBM implementation roadmap for your plant or engineering environment

What you will learn (CBM techniques and tools)

You will gain practical exposure to the most widely used condition monitoring technologies

• Evolution of maintenance strategies from reactive to predictive CBM-driven maintenance

• CBM fundamentals and workflow: Monitor → Detect → Diagnose → Prognose → Act

• Core CBM techniques: vibration, thermography, oil analysis, ultrasound, and ESA/MCSA

• Role of CMMS in CBM execution, work order management, and data integration

• Global maintenance gaps between reactive, preventive, and condition-based models

• Key challenges in CBM adoption and proven mitigation strategies

• Frameworks to build a practical CBM implementation roadmap

• Differences between CBM, predictive, prescriptive, and hybrid maintenance models

• Real-world case studies with measurable reliability and cost improvements

• Future of CBM: AI, digital twins, IoT, and autonomous maintenance

• How to develop a CBM action plan and measure ROI

Standards and best-practice frameworks covered

This course links CBM execution to international references, including

• ISO 17359 (Condition monitoring — general guidelines)

• ISO 13374 (Data processing and communication)

• ISO 55000 (Asset management principles)

Following (course structure)

• Maintenance strategy evolution and CBM fundamentals

• CBM workflow and decision-making process

• CBM technologies (vibration, thermography, oil, ultrasound, ESA)

• Implementation roadmap and adoption challenges

• Real industry case studies with measurable results

• Future of CBM: AI, Digital Twins, Industry 5.0

Who this course is for
Power plant engineers (mechanical, electrical, instrumentation, and control).
Maintenance engineers, supervisors, and maintenance managers.
Reliability engineers and asset management professionals.
Operations and O&M professionals responsible for equipment performance.
Energy sector professionals transitioning toward digital and condition-based maintenance strategies.
Engineering students and early-career professionals interested in maintenance, reliability, and asset management.
Professionals involved in reducing downtime, improving equipment availability, and optimizing lifecycle costs.