1. Analytical Reliability Centered Maintenance
RCM Conference – Chattanooga, TN
October 2002
Analytical Reliability Centered Maintenance
Airline Example
Jahan Alamzad
1250 Aviation Avenue
Suite 200M
San Jose, CA 95110
Tel:
Fax:
CA Advisors

2. Objectives
Present applying Reliability-centered Maintenance (RCM) to develop analytical decision-support tools
Discuss analytical applications of RCM in the airline industry
Provide a structure of developments

3. Overview RCM focuses on understanding reliability characteristics
Reliability characteristics can be abstracted analytically, and be represented by indicators
Decisions can then be based on such indicators, providing significant benefits
Operations
Management
Data
Compilation
Enhanced
Asset Management
RCM
Decision Support

4. Decision-support tools
Reliability indicators
understand the inherent reliability of parts
estimate failure rate
evaluate aging features
Forecasting
predict maintenance events
anticipate maintenance activities
Planning
determine maintenance workload and material requirements during a specific period of time
strategic assessment of maintenance needs
identify support resources
Optimization
right-size inventory requirements
repairable parts
expendable parts
design optimal maintenance program

5. RCM – airline industry Genesis of RCM New philosophy
pre-deregulation
observing enhanced reliability of modern flight equipment
better ways to capitalized on increased performance
New philosophy
condition monitoring vs. overhaul
life-limited parts
threshold-limited parts
Regulatory control
highly-regulated operations; commercially deregulated
must prove before modifying maintenance practices
regulatory requirements and best practices

6. Federal Aviation Regulations (FARs)
Must comply with FAR’s as a certificated air carrier
Policing mechanism
auditing
code of honor
Mostly concerned with MRO (maintenance-repair-overhaul) tracking and record-keeping
manufacturer information
manufactured date
time since manufactured (flying hours, cycles, days)
time since overhaul/installation (flying hours, cycles, days)
history tracking
positive tracking
compliance with maintenance program
Maintenance activity record keeping
Requires substantial information system infrastructure to comply properly
RCM: separation of data-processing and decision-support

7. Significant benefits
Ease of developing analytical decision-support tools
Available data
standard
accessible
Quality decisions
identify performance criteria
monitor performance
evaluate practices
next-generation flight equipment maintenance
asset management
Industry-wide analysis
Living RCM
Performance
Unit Cost

8. Example – engine maintenance
Engine Reliability System
Engine Removal Forecast
Spare Engine Planning System
Engine Life Optimizer

9. Structured approach Platform and Data Automation Logic and Theory Data
Goals
Pilot
Beta
Production
User Support

10. Pilot study – October 2000 Analysis process: ln scale ln-ln scale f(p)
Probability of failure before t = p
one-parameter approach
two-parameter approach
ln scale
ln-ln scale
Slope: beta parameter
f(p)
Slope: failure rate
f(p)
Characteristic Life
ln scale t t
Analysis process:
identify Scheduled vs Unscheduled events
determine inter-event time
build probability distribution function (pdf)
transform data points
do regression analysis
determine parameters

11. Results (one breaker model)
One-parameter
Failure rate:
R-Squared: 95.9%
Two-parameter
Beta:
Characteristic life:
R-Squared: 98.0%
Application
Input:
next scheduled maintenance: 400 days
time since last maintenance: 120 days
Output:
probability of failure before scheduled maintenance
expected time until next failure (days)
one-parameter
two-parameter
0.4488
0.4360
301
306

12. Implementation
Living RCM allows developing decision-support tools that reduce the unit cost of production and delivery
Unit Cost
Shortage
Overage
Resource Level
Needed resources can be scientifically justified and decisions can be analytically supported
Requires industry-wide data standardization
Significant and immediate benefits
better planning
best practices