Risk Based Maintenance

In today’s fast-paced industrial landscape, maintenance teams must go beyond reactive strategies and embrace proactive, data-driven approaches. Risk-Based Maintenance (RBM) is essential for optimizing asset reliability, reducing unexpected failures, and ensuring cost-effective operations. By analyzing failure modes, assessing risk levels, and prioritizing maintenance tasks based on potential impact, RBM allows organizations to allocate resources efficiently and focus on the most critical areas. Maintenance professionals who miss out on RBM training risk falling behind in industry best practices, leading to inefficient workflows, increased downtime, and missed opportunities for performance optimization.

RBM is not just about preventing breakdowns—it directly contributes to safety, compliance, and financial sustainability. Industries with high-risk assets, such as manufacturing, energy, and transportation, rely on maintenance personnel to make informed decisions that prevent catastrophic failures. A structured course on RBM equips professionals with the tools and methodologies needed to assess risks, implement predictive techniques, and develop strategies that align with operational goals. Without this expertise, maintenance teams may struggle to justify maintenance investments or lack the ability to align their efforts with broader business objectives.

Moreover, as technology evolves, maintenance professionals must adapt to new tools like AI-driven analytics, condition monitoring, and digital twin simulations—all of which integrate seamlessly with RBM principles. Learning how to leverage these advancements ensures that maintenance teams remain competitive, proactive, and capable of driving continuous improvement. By attending an RBM course, professionals gain not just theoretical knowledge but practical insights they can apply immediately, leading to a more resilient, efficient, and strategically aligned maintenance operation. Missing this opportunity means missing the chance to future-proof maintenance practices and maximize asset performance.

Top Learning Objectives

  • Learn how to evaluate and manage risks
  • Develop skills of applying advanced risk assessment techniques
  • Discover strategies for planning and implementing risk management/reduction actions/plans
  • Develop advanced risk based maintenance
  • Reduce risks via advanced root cause analysis
  • Maximise profits with effective maintenance management and equipment life cycle control
  • Eliminate risk and downtime caused by maintenance defect and failure with highly effective scheduled maintenance

Who Should Attend?

VPs, Directors, Division Heads, Managers, Superintendents, Specialists, Leaders, Supervisors, Foremen, Planners, Technicians, & Engineers from the following departments:

  • Maintenance
  • Engineering
  • Reliability
  • Preventive Maintenance
  • Predictive Maintenance
  • Shutdowns & Turnarounds
  • Condition Monitoring
  • Rotating Equipment
  • Static Equipment
  • Mechanical Engineering
  • Asset Management
  • Asset Integrity
  • Operations
  • Plant
  • Production
  • Process
  • Inspection

Trainer's Background

  • He has trained and consulted for different industries worldwide. [The trainer] has 35 years of real-life industrial experience in maintenance planning and scheduling holding key positions in major companies such as Rolls-Royce, SKF, Caterpillar, Dresser-Rand, Shell and Severn Trent Water.
  • He has developed and delivered training programmes worldwide for power & utilities, oil & gas, and manufacturing, including courses on maintenance planning and scheduling, machinery failure analysis and prevention, reliability centred maintenance, condition monitoring of machinery, risk assessment for production and operations, and problem solving and decision making.
  • He is also the author of over 200 publications (including 17 patents, 4 books and 2 book chapters) in the areas of maintenance, condition monitoring of complex mechanical systems and root cause analysis.
  • Due to his many contributions, He has received many awards such as the Oxford OBN Award (2014), the Rolls-Royce Innovation Award (2011), and the William Sweet Smith Prize from the Institution of Mechanical Engineers (2010).

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