Well-executed Preventive and Predictive Maintenance, seamlessly integrated into the workflow, is essential for the success of a company and a key component of maintenance management strategies like RCM, RBM, TPM, and even Six Sigma. This detailed 5-day Energy training program is designed to assist both newcomers and experienced professionals involved in implementing or evaluating a comprehensive Maintenance and Asset Management process. It covers all necessary steps to develop a successful Preventive and Predictive Maintenance program, from understanding failure patterns and selecting the correct tasks, to creating a well-organized program fully integrated with workflows and CMMS.
Forward-thinking organizations are shifting from a reactive ("fix-it-when-it-breaks") approach to a proactive, preventive, and predictive strategy ("anticipating, planning, and preventing failures"). This transformation requires coordinated efforts across various aspects.
This Implementing Effective Preventive & Predictive Maintenance Programmes training course will focus on:
- Preventive and predictive maintenance strategies and their role in Asset Management
- Risk-Based Maintenance approaches
- Best practices in maintenance and reliability engineering
- Effective planning and scheduling (workflow management)
- Implementation of CMMS (Computerized Maintenance Management System)
- Performance monitoring and management through Key Performance Indicators (KPIs)
- Continuous improvement practices
By the end of this Implementing Effective Preventive & Predictive Maintenance Programmes training course, you will be able to:
- Understand how leading organizations address common planning challenges
- Enhance productivity by utilizing more accurate and timely data
- Establish an efficient and practical predictive maintenance strategy
- Increase the consistency and reliability of asset-related information
- Optimize preventive and predictive maintenance approaches for better results
This Energy training course will be conducted along interactive workshop principles. There will be a variance of lectures and practical exercises. Experiences from different areas will be discussed. There will be many opportunities for discussion and sharing experiences.
This Energy training course is suitable to a wide range of professionals but will greatly benefit:
- Maintenance Managers & Supervisors
- Personnel designated as planners, or identified to become planners
- Predictive Maintenance Technicians & Supervisors
- Key leaders from each Maintenance craft
- Maintenance & Reliability engineers
- Materials Management Managers/Supervisors
- CMMS key users
Day One: The Need for Maintenance
- Maintenance & Asset Management as a business process
- Risk Based Maintenance (RBM)
- Causes of Failure
- Likelihood & Severity of Failure - Risk Analysis
- Failure Mode Effect & Criticality Analysis (FMECA)
- Choosing the (preventive) maintenance tasks
- 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 Two: Developing the CMMS
- Database & structure
- CMMS & workflow
- CMMS & Maintenance Strategies
- Asset register
- Configuration management
Day Three: The Planning Function
- The maintenance workflow and how it relates to the preventive maintenance strategy
- Roles & responsibilities in work preparation, planning and scheduling
- Principles of work preparation & planning
- Principles of scheduling
- Network planning
Day Four: Predictive Maintenance
- Potential Failure Analysis (PFA)
- Integration of PFA with FMECA & RBM
- Understanding the P-F Interval
- Decide which Technologies to Apply
- Predictive maintenance technologies
- Vibration analysis
- Visual inspection
- Infrared Thermography
- Temperature sensitive labels
- Megger tests
- Ultrasonics
- Oil analysis
Day Five: Control of the Maintenance Process
- Implementation stages of preventive & predictive maintenance strategies
- CMMS integration
- Reporting – use of (Key) Performance Indicators
- Case study
