Implementing Effective Preventive and Predictive Maintenance Programmes Training

Acculearn Training Ltd is a company that provides training and development courses in Management and Leadership, as well as other areas such as Finance, Sales and Marketing, Human Resources, and Personal Development. They offer courses in various locations around the world, including London, Dubai, Kuala Lumpur, Istanbul, Paris, Bali, Houston, Rome, Madrid, Munich, Barcelona, Singapore, Berlin, Hong Kong, Zurich, Washington, New York, Lucerne, and Athens. They also offer virtual training options for those who prefer to take their courses online. Acculearn Training's mission is to help individuals and organizations achieve their full potential through high-quality training and development programs.

Upcoming Sessions

Introduction

Effective Planned & Predictive Maintenance are critical for a successful company and an integral part of maintenance management strategies such as RCM, RBM TPM, and even 6-Sigma. This comprehensive 5-day programme has been designed to benefit both qualified new professionals as well as experienced professionals who may be involved in the rollout of a comprehensive Maintenance system or auditing an existing system. It covers all the steps required in developing a successful Planning & Predictive Maintenance programme from system development until a well-managed Maintenance system is in place and operational.

Objectives

Leading industrial organizations are evolving away from reactive (“fix-it-when-it-breaks”) management into predictive, productive management (“anticipating, planning, and fix-it-before-it-breaks”). This evolution requires well-planned and executed actions on several fronts.

  • Understand how world-class organizations solve common planning problems
  • Improve productivity through use of better, more timely information
  • Implement a practical and effective predictive maintenance effort
  • Improve consistency and reliability of asset information
  • Achieve more productive turnarounds
  • Optimize preventive and predictive maintenance strategies

The Contents

Day 1 – The Need for Maintenance

  • Failure Mode Effect & Criticality Analysis (FMECA)
    • Causes of Failures
    • Likelihood & Severity of Failure – Risk Analysis
    • Reliability Centred Maintenance (RCM)
  • Optimisation of Maintenance Decisions
    • Failure Pattern Identification
    • Statistical Analysis of Failures
    • Weibull Analysis
  • Zero Base Budgeting
    • Define the production requirement
    • Define the maintenance requirement

Day 2 – Developing the CMMS

  • Database Construction
    • Installed Asset Base
    • Hierarchical Structure
    • Procedures and Plans
  • Resources
    • Dedicated Manpower
    • Contractors
    • Specialist Tools
  • Maintenance Strategies
    • Centralised/Decentralised
    • Life/Emergency/Corrective/Planned
    • Planned & Predictive

Day 3 – The Planning Function

  • Roles & Responsibilities
    • The Planners
    • Job Initiators
    • Maintenance Trades
  • Job Planning
    • Planning Corrective Work
    • Integrate Planning with Procedures
    • Resource Levelling
  • Scheduling
    • Long Term Scheduling with Production
    • Medium & Short Term Scheduling
    • Planning Department Interfaces

Day 4 – Predictive Maintenance

  • Potential Failure Analysis (PFA)
    • Integration of PFA with FMECA & RCM
    • Understanding the P-F Interval
    • Decide which Technologies to Apply
  • Vibration Analysis
    • Detectable Faults
    • Setup Parameters
    • Monitoring & Protection
    • On-Line or Off-Line
  • Supporting Technologies
    • Infrared Thermography
    • Passive Ultrasonics
    • Oil Analysis

Day 5 – Control of the Maintenance Process

  • CMMS Integration
    • Predictive Maintenance Interface
    • Optimising PM Kit Usage with PdM
    • Operational planning
  • Reporting
    • Monthly PM & PdM reports for Management
    • Financial Feedback Reports
    • Budget Control
  • Key Performance Indicators
    • Reliability & statistics – MTBF, Reliability etc.
    • Work request backlog analysis
    • Customer feedback analysis

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