<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Aviation on Morgan Bye</title><link>https://morganbye.com/tags/aviation/</link><description>Recent content in Aviation on Morgan Bye</description><generator>Hugo</generator><language>en-ca</language><copyright>CC BY-SA 4.0</copyright><lastBuildDate>Mon, 24 Mar 2025 15:30:00 -0400</lastBuildDate><atom:link href="https://morganbye.com/tags/aviation/index.xml" rel="self" type="application/rss+xml"/><item><title>Predictive maintenance for business aircraft</title><link>https://morganbye.com/projects/2024_pmx/</link><pubDate>Mon, 24 Mar 2025 15:30:00 -0400</pubDate><guid>https://morganbye.com/projects/2024_pmx/</guid><description>&lt;h3 id="-the-business-challenge"&gt;📈 &lt;strong&gt;The Business Challenge&lt;/strong&gt;&lt;/h3&gt;
&lt;p&gt;A global business jet manufacturer, needed to modernize how it processed and analyzed flight sensor data to support predictive maintenance, enhance customer service, and enable continuous engineering improvements.&lt;/p&gt;
&lt;p&gt;Each new connected aircraft was equipped with over 6,000 streaming sensors and uploading after every flight.&lt;/p&gt;
&lt;p&gt;Their data lake had grown to over 100 terabytes of flight data, but extracting value had become increasingly challenging. Key stakeholders — from flight engineers to customer support to field technicians — struggled with slow, costly queries and limited access to actionable insights. They sought a scalable architecture that could power real-time maintenance decisions and long-term ML-driven predictions.&lt;/p&gt;</description></item><item><title>Flight operations at Boeing</title><link>https://morganbye.com/projects/2020_boeing/</link><pubDate>Fri, 17 May 2024 14:30:00 +0500</pubDate><guid>https://morganbye.com/projects/2020_boeing/</guid><description>&lt;p&gt;&lt;a href="https://services.boeing.com/flight-operations/flight-data-analytics/insight-accelerator"&gt;Insight Accelerator&lt;/a&gt; is a first-of-its-kind predictive maintenance solution that provides powerful advanced analytics and customized alerting – all in an easy-to-use tool. By analyzing full flight data from thousands of onboard sensors, users can derive prognostic insights and create alerting algorithms unique to each airline’s operation.&lt;/p&gt;
&lt;p&gt;Harness the power of built-in augmented analytics to identify patterns of premature component failure to pre-emptively perform maintenance and avoid the high impacts of unwanted disruptions – all without needing data science or advanced programming skills.&lt;/p&gt;</description></item></channel></rss>