Cambridge
Academy
In London

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.

Course Objectives

By the end of this course, 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.

Target Audience

This course is designed for:

·        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.

Course Outlines

Day 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.

Day 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.

Day 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.

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.

·        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.