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Analytical Reliability Centered Maintenance Airline Example

Analytical Reliability Centered Maintenance Airline Example. RCM Conference – Chattanooga, TN October 2002. Jahan Alamzad 1250 Aviation Avenue Suite 200M San Jose, CA 95110 Tel: 408-295-7730 Fax: 408-280-5700 Email: jahan_a@msn.com. CA Advisors. Objectives.

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Analytical Reliability Centered Maintenance Airline Example

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  1. AnalyticalReliability Centered Maintenance Airline Example RCM Conference – Chattanooga, TN October 2002 Jahan Alamzad 1250 Aviation Avenue Suite 200M San Jose, CA 95110 Tel: 408-295-7730 Fax: 408-280-5700 Email: jahan_a@msn.com 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. Data Compilation 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 Enhanced Asset Management RCM DecisionSupport

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

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

  11. Results (one breaker model) • One-parameter • Failure rate:0.00149 • R-Squared: 95.9% • Two-parameter • Beta: 1.06067 • Characteristic life:703.30 • R-Squared: 98.0% • Application • Input: • next scheduled maintenance: 400 days • time since last maintenance: 120 days • Output: • probability of failure beforescheduled 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

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