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LRCM
- CMMS Impediments to Reliability Analysis
- Components of continuous improvement
- How to start LRCM
- Justifying Living RCM Certified
- Leading and lagging performance numbers
- Living RCM Certified – Consulting Services
- LRCM – Reporting failure modes of rotable components
- LRCM and HSE
- LRCM Justification Template
- LRCM reliability analyst survey results
- LRCM reliability technician survey results
- MESH – RCM knowledge continuous improvement
- Motivation, leadership, training
- PAS-55
- RCM – Dashboards
- Reliability engineer’s work cycle
- Service vs. maintenance
- Streamlined RCM and LRCM
- Structured free text
- The role of media in living RCM
- The winning paper at the XIV International Congress of Maintenance
- Two philosophies in maintenance improvement
- Waiting for CMMS maturity
- What is a pilot project?
- What is the difference between RCM and LRCM?
- Achieving Reliability from Data outline with video
- Course brochure – Living RCM Certified
- Deepwater Horizon
- Elevator description of LRCM
- How does LRCM “improve” RCM?
- Living RCM Certified
- Living RCM Certified eLearning
- Living RCM Certified® and ISO 14224
- LRCM – off the maintenance improvement radar
- LRCM-EXAKT – a general solution
- MESH Basic reliability analysis on the work order
- Mesh Living RCM Certified brochure – Mesh Cloud Service
- Obtener confiabilidad a partir de los datos – esquema del curso
- RCM – Analyst course outline
- RCM – feedback suggestion mechanism
- RCM – Living RCM
- RCM – LRCM dashboards
- RCM – feedback – suggesting a new failure mode
- Training course in achieving reliability from data
- Two kinds of decision making in maintenance
- Two LRCM purposes
- Videos
- Why Living RCM works
RCM
Reliability Analysis
- Achieving Reliability from Data
- Challenges to Achieving Reliability from Data
- Data analysis precedes reliability analysis
- Data is the key to the way forward
- Defeating CBM
- Does historical age data have value?
- Failure declaration standards
- Free text on the work order
- How much data is required for RA?
- How to assess EAM and CBM predictive capability
- Interpreting failure data
- LRCM – Reporting failure modes of rotable components
- Maintenance software
- Mesh: 12 steps to achieving reliability from data
- RA requires LRCM
- Reliability analysis in 2 dimensions-Part 2
- Sample selection
- So you’re getting an EAM
- Take the EAM data health check
- The CMMS barrier to RCM
- The data barrier to analysis
- The reliability data Catch 22
- Thoughts from a mine maintenance engineer
- Variations in a sample
- Warranty for haul trucks
- Weibull exercises
- What’s the right data?
- A survey of signal processing and decision technologies for CBM
- Achieving reliability from data
- CBM Defined
- Conditional failure probability, reliability, and failure rate
- Conditional probability of failure
- Conditional probability of failure vs. hazard rate
- Criticality analysis in RCM
- Diagnostics versus prognostics
- Difference between LRCM and EXAKT
- EXAKT’s Three Modules
- Expected failure time for an item whose maintenance policy is time-based
- Failure analysis for reliability analysis
- Failure probability prior to attaining MTTF
- FAQ
- FMEA according to Wikipedia
- Is “random failure” really random?
- Leading and lagging performance numbers
- LRCM and the Failure Finding Interval
- MTTF is the area under the reliability curve
- Myths about RCM in heavy mining equipment
- Non-rejuvenating events
- Optimal PM and spares strategies – exercises
- Performance metrics – Low and High level KPIs
- Problem statement
- Purpose of RA
- RA – Micro (day-to-day decision) analysis
- Random failure and the MTTF
- Random failure is exponential reliability decay
- RCM – Living RCM: Achieving reliability from data
- RCM vs RA
- Real meaning of the six RCM curves
- Reliability analysis is counting
- Reliability trend yes Weibull analysis no
- Remaining Useful Life Estimation Using Hybrid Monte-Carlo Simulation and Proportional Hazard Model
- Safety Instrumented Systems
- TBM or CBM?
- Terminology in LRCM
- Thinking RCM
- Time to failure
- What is PM?
- What is the scale parameter?
CBM
- A survey of signal processing and decision technologies for CBM
- Automating CBM
- Building a CBM decision model
- CBM Exercises
- CBM Optimization
- Combined analysis for early predictive maintenance
- Deploying the CBM model
- EXAKT cost sensitivity analysis
- EXAKT needs LRCM
- EXAKT vs Weibull
- Measuring and Improving CBM Effectiveness
- Optimizing a Condition Based Maintenance Program with Gearbox Tooth Failure
- RCM – Reliability analysis in more than two dimensions is CBM
- Smart CBM demo
- What is Maintenance Decision Automation?
- Confidence in predictive maintenance
- Diagnostics versus prognostics
- Inspections – CBM and others
- Inspections or CBM?
- Internal and external CBM variables
- NAVAIR and the PF interval
- Objectivity in condition based maintenance decisions
- Optimized interpretation of CBM data
- P-F Interval a red herring?
- PF interval from the failure rate
- PM, PdM, Proactive Maintenance
- Predictive analytics
- Temporary fix work orders
- The elusive P-F interval
BACKGROUND
As one of their principal functions, work order and condition monitoring systems provide data to the reliability engineers in a maintenance organization. Why collect data? To support decisions regarding what maintenance to perform, where, and when. An “optimal” decision rule or policy will apply the right maintenance at the right time to the right components in a way to achieve highest long term profitability. Maintenance managers and engineers have long believed that within their data lie the secrets to achieving optimal decisions. That realization has driven the growth of high technology services and tools to collect, display, manipulate, store, and analyze limitless quantities of data.
THE PROBLEM
Given that the amount of data available is infinite, the maintenance engineer faces new questions:
- Which data will support optimal decision making? and
- How should one transform the relevant data into optimal maintenance policies? and
- How should one verify the performance of those policies in order to improve them continuously?
THE Living RCM Certified® SOLUTION
The “Living” Reliability Centered Maintenance methodology addresses three challenges to Achieving Reliability from Data:
- Management of the relationship between the work order system and the maintenance knowledge base,
- Sample generation,
- and Reliability analysis.
Living RCM Certified applies fundamental Reliabability Centered Maintenance (RCM) thinking to each challenge by combining technology solutions that encourage vital human participation and control over the entire process. Key information needed from the CMMS/EAM is “age” or “life” or “event” data. Simply, Living RCM Certified® enables the referencing and growth of knowledge. Each significant work order becomes both an instance of a Failure Mode and a point in a sample essential for conducting a Reliability Analysis and developing practical, verifiable day-to-day work selection decision models that optimize maintenance activities.