{"id":2433,"date":"2026-06-24T13:40:58","date_gmt":"2026-06-24T13:40:58","guid":{"rendered":"https:\/\/zephyrsys.us\/index.php\/2026\/06\/24\/pam-apac-the-data-infrastructure-constraints-holding-back-predictive-maintenance-at-scale\/"},"modified":"2026-06-24T13:40:58","modified_gmt":"2026-06-24T13:40:58","slug":"pam-apac-the-data-infrastructure-constraints-holding-back-predictive-maintenance-at-scale","status":"publish","type":"post","link":"https:\/\/zephyrsys.us\/index.php\/2026\/06\/24\/pam-apac-the-data-infrastructure-constraints-holding-back-predictive-maintenance-at-scale\/","title":{"rendered":"PAM APAC: The data infrastructure constraints holding back predictive maintenance at scale"},"content":{"rendered":"

Our second in a new series of PAM webinars explored why predictive maintenance in aviation remains difficult to adopt and scale despite advances in AI and machine learning.<\/p>\n

Sponsored by FTG, the webinar highlighted that while analytics capabilities have matured significantly, their effectiveness remains constrained by the underlying data infrastructure.<\/p>\n

William Cecil, FTG sales director, said legacy aircraft data systems, based on ARINC 429 and 717 standards, provide airlines with a high degree of independence from OEMs.<\/p>\n

\"\"To watch the webinar in full click on the above image or click here<\/a><\/em><\/strong><\/figure>\n

However, this flexibility comes at the cost of scalability, as each implementation requires custom configuration, parameter mapping, and decoding logic.<\/p>\n

This results in tightly coupled systems where onboard data acquisition and offboard interpretation must remain aligned.<\/p>\n

\u201cThese constraints lead to challenges in achieving continuous, high-quality time-series data\u2014critical for predictive maintenance models,\u201d said Cecil.<\/p>\n

\u201cWhile maintenance records exist, the lack of consistent and scalable data acquisition limits the effectiveness of these models.\u201d<\/p>\n

OEM platforms have improved data capacity and integration but typically rely on vertically integrated architectures. This enhances consistency but can reduce flexibility and airline control.<\/p>\n

The webinar examined emerging architectural approaches that decouple data acquisition from traditional constraints.<\/p>\n

Connectivity was discussed in terms of lifecycle alignment rather than performance alone. While 4G remains functional, it is already a mature technology.<\/p>\n

Cecil said: \u201cAligning long-lived aircraft investments with early-stage network generations, such as 5G, is critical to avoiding shortened investment lifecycles.\u201d<\/p>\n

The webinar reframed predictive maintenance as a data infrastructure challenge.<\/p>\n

\u201cThe key to adoption and scalability lies in enabling flexible, consistent, and decoupled data architectures rather than relying solely on advancements in analytics,\u201d concluded Cecil.<\/p>\n

FTG is one of the sponsors of PAM APAC which will take place on September 21-22 in Singapore. To register for free if you work for an airline or to buy tickets click here<\/a>.<\/strong><\/em><\/p>\n

The post PAM APAC: The data infrastructure constraints holding back predictive maintenance at scale<\/a> appeared first on Aviation Business News<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"

Our second in a new series of PAM webinars explored why predictive maintenance in aviation remains difficult to adopt and scale despite advances in AI and machine learning. Sponsored by FTG, the webinar highlighted that while analytics capabilities have matured significantly, their effectiveness remains constrained by the underlying data infrastructure. William Cecil, FTG sales director, […]<\/p>\n","protected":false},"author":1,"featured_media":2434,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2433","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/posts\/2433","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/comments?post=2433"}],"version-history":[{"count":0,"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/posts\/2433\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/media\/2434"}],"wp:attachment":[{"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/media?parent=2433"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/categories?post=2433"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zephyrsys.us\/index.php\/wp-json\/wp\/v2\/tags?post=2433"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}