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AI in Equipment Maintenance Service and Support

AI in Equipment Maintenance Service and Support. Michael Halasz National Research Council Canada Plenary Session March 23, 1999 Stanford University. What?. “renewed” interest in software tools and techniques to improve maintenance process monitoring diagnosis and/or prognosis

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AI in Equipment Maintenance Service and Support

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  1. AI in Equipment Maintenance Service and Support Michael Halasz National Research Council Canada Plenary Session March 23, 1999 Stanford University

  2. What? • “renewed” interest in software tools and techniques to improve maintenance process • monitoring • diagnosis and/or prognosis • repair management (parts, location, tools, skills) • handling repercussions (e.g. rescheduling) • capturing and reapplying expertise

  3. Why? • cost • can exceed capital outlay (125% - regional jet) • difficult to control (wide swings) • quality of service • intangibles (customer perception, side effects) • maintenance “outsourcing” • manufacturer: reduced profit margins • operator: not primary mission • maintenance service provider: specialty, economy of scale, risk mitigation

  4. Maintenance: an information rich environment • source: equipment, humans, systems • data: voluminous, messy, distributed, diverse • knowledge: humans, documents, operating practices, software • infrastructure: communications & hardware crosses organizational boundaries

  5. Leverage through Innovative Information Technologies • applied AI to reason about situations • case-based reasoning • fuzzy logic • neural networks • induction • model based reasoning • rule-based • etc. hybrid systems No Silver Bullet!

  6. Symposium Format • 1 tutorial • soft computing, multiple techniques • 20 papers/talks • purely technical & application oriented • 2 working sessions

  7. Breakdown • application areas • automotive, aircraft, HVAC, gas turbines, pumps,… • design for maintainability • techniques • networks (belief, bayesian, neural) • MBR, CBR, rules • fuzzy logic • free text interpretation • source • 45% industrial, 30% academic, 25% blend

  8. Summary Equipment Maintenance Service and Support poses many interesting IT challenges fertile area for AI

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