Part 2 Sample generation
In order to conduct Reliability Analysis we require a sample. What is a sample?
Assume that this vertical arrow represents the time line on which maintenance work orders are issued and executed.
We’ll consider five work orders conducted over a period of calendar time.
Work order 1 was a functional failure of failure mode 15.
Work order 2 was a functional failure of failure mode 16, and so on.
On Work order 4 a maintenance technician suspended the life of failure mode 15.
And on work order 5 we caught the failure mode 15 while it was still in its potential failure state.
But this format in which the data is stored in the EAM, won’t allow us to perform Reliability Analysis directly.
We must extract a sample from the EAM’s work order database. Then, we may perform Reliability Analysis on the sample.
We’ll transform the work order data into an Events table.
Note that each work order generates two events. The failure mode life’s ending, designated by E and the failure mode’s life renewal or beginning event designated by the symbol B.
Failure Mode 15’s ending event by failure is represented by the symbol EF15. It’s renewal or beginning event is B15. Failure Mode 16’s ending by suspension is ES15 and so on.
Notice that in work order 5, for purposes of Reliability Analysis, a potential failure transforms simply to a failure. That’s because, although we may have avoided the direst consequences of failure, we haven’t prevented the failure itself.
The solid arcs join the beginning event on one work order to the ending event on a subsequent work order. For example an arc joins B15 to ES15. Each arc represents a life time of its respective failure mode.
We define a sample window by specifying two points in calendar time.
The arcs that appear within the window constitute our sample. Reliability analysis is counting the arcs in the sample. For a particular failure mode the number of arcs divided by the sum of all its lifetimes is approximately equal to the failure mode’s MTBF, or mean time between failure.
What about those partial dashed arcs, whose beginnings or endings lie outside the sample window? They are known as suspended or censored data.
The arcs that begin inside the window but end outside the window are called “right suspensions”. The arcs that begin outside the window but end inside are called “left suspensions”. The software enabled procedure that I will demonstrate will account for this suspended data appropriately.
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LRCM
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RCM
Reliability Analysis
- Achieving Reliability from Data
- Challenges to Achieving Reliability from Data
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- 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
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- 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
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