IDEAS home Printed from https://ideas.repec.org/h/spr/ssrchp/978-3-031-21232-1_2.html
   My bibliography  Save this book chapter

Smart Maintenance and Human Factor Modeling for Aircraft Safety

In: Applications in Reliability and Statistical Computing

Author

Listed:
  • Eric T. T. Wong

    (The Hong Kong Polytechnic University)

  • W. Y. Man

    (Heliservices (HK) Ltd)

Abstract

The global pandemic has significantly accelerated the need for remote monitoring and diagnostics of airline operations and assets. As passenger and cargo flights are impacted from all directions, maintenance can be the steady, reliable part of the puzzle that helps get things back on track. This chapter explores the aircraft safety challenges that can be addressed with better maintenance technology and human factor modeling. Aircraft safety relies heavily on maintenance. During the COVID-19 recovery phase, airline operators need to focus on the application of a robust management of change process to implement better maintenance technology, identify new aircraft safety risks, determine effective mitigation measures, and implement strategies for deploying changes accordingly. For years aircraft maintenance routines have been carried out in the same manner without change, now with international travel restrictions, social distancing, reduced staff, and limited maintenance funding, the need for smarter ways of doing maintenance is obvious. In this regard smart technology has an important role to play. For instance, IoT data generates the capacity for predictive aircraft maintenance, AI introduces the capacity for smart, deep-learning machines to make predictive maintenance more accurate, actionable, and automatic. AI-enabled predictive maintenance leverages IoT data to predict and prevent aircraft failures. While smart technology enhances aircraft safety through better maintenance performance on the one hand, there are technical and human factor problems induced by COVID-19 on the other. The Safe Aircraft System (SAS) model, based on the Dirty Dozen and SHELL human factor models, is an initiative proposed to minimize such COVID-19 problems. This work shows through a case illustration that SAS modeling is a useful tool in identifying potential hazards/consequences associated with any major or minor changes in flight operations. Hence the synergistic effect of smart maintenance and the SAS model in enhancing aircraft system safety are demonstrated.

Suggested Citation

  • Eric T. T. Wong & W. Y. Man, 2023. "Smart Maintenance and Human Factor Modeling for Aircraft Safety," Springer Series in Reliability Engineering, in: Hoang Pham (ed.), Applications in Reliability and Statistical Computing, pages 25-59, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-21232-1_2
    DOI: 10.1007/978-3-031-21232-1_2
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:ssrchp:978-3-031-21232-1_2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.