Implementing Effective Preventive and Predictive Maintenance Program

Introduction:

Effective Planned & Predictive Maintenance is 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 program 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 program from system development until a well-managed Maintenance system is in place and operational.

Targeted Groups:

The training is intended for:-

• Maintenance Personnel.
• Maintenance technicians.
• Maintenance engineers.
• Reliability engineers.
• Maintenance supervisors/managers.
• Operations Personnel.
• Operators who work closely with the equipment.
• Production supervisors/managers.
• Engineering Personnel.
• Reliability engineers.
• Management Personnel.
• Plant managers.
• Maintenance managers.
• Operations managers.
• Support Staff.
• Those involved in data analysis and interpretation.
• Administrative staff responsible for documentation and record-keeping.
• Cross-Functional Teams.

Training Objectives:

By the end of this course the participants will be able to:

• Understanding Maintenance Concepts.
• Equipment Knowledge.
• Maintenance Planning and Scheduling.
• Condition Monitoring Techniques.
• Predictive Maintenance Technologies.
• Maintenance Procedures and Best Practices.
• Safety and Compliance.
• Cross-Functional Collaboration.
• Performance Monitoring and Continuous Improvement.

Targeted Competencies:

• Equipment Knowledge.
• Maintenance Procedures.
• Condition Monitoring Techniques.
• Data Interpretation.
• Analytical Competencies.
• Problem-Solving.
• Data Analysis.
• Risk Assessment.
• Communication and Collaboration.
• Interdepartmental Communication.
• Documentation.
• Safety and Compliance.
• Continuous Improvement.
• Leadership and Management.
• Resource Management.

Course Content:

Unit 1: The Need for Maintenance:

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

Unit 2: Developing the CMMS:

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

Unit 3: The Planning Function:

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

Unit 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.

Unit 5: Control of the Maintenance Process:

• CMMS Integration:
  • Predictive Maintenance Interface.
  • Optimizing 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.