Let´s put things right into perspective at the beginning. When the aviation industry talks about BIG DATA they really mean a lot of data. Data which is usually collected by sensors on one aircraft covering more than 300,000 parameters. Out of these parameters engine data are one of the most important set of data points they capture. Let´s take an average commercial aircraft – like the Boeing 737. The aircraft has two engines where each of the engines creates 20 terabytes of information per engine hour. Multiply this by an average six-hour cross-country flight from New York to Los Angles and you get 240TB of data for every engine hour. Alone in the United States you have approx. 28,500+ commercial flights in the sky on any given day. Now multiply this with 365 days a year and you get a real big, big data challenge. And this is just for commercial flights and just the data from two engines. According to the US National Air Traffic Controllers Association a total of 87,000 flights are in the skies in the United States on any given day. This includes major airlines, cargo, planes to hire and military aircrafts.
The Big Data Challenge
Picture credit: HP
Let´s stay with this number of engine data collected only from the commercial aircraft engines in one year. 2,499,841,200 TB equals 1,527,000,000,000,000,000,000 quintillion books (avg. of 100 pages, 1800 characters on each page). Even if you would have a army of aircraft maintenance service engineers, this would be a lot of books to read through to make sense out of this data for the next 100 years to come. One other comparision to understand the scale of this amount of data would be: the ten digit TB figure mentioned above converts to approx. 2,7 Zettabytes of data. This 2,7 ZB equals nearly three times of the total estimated global IP data traffic per year in 2015 (source: Wolfram|Alpha: Computational Knowledge Engine).
So how can a commercial aircraft manufacturer benefit from all of this data they collect and store? What would be the impact if they don´t make use of it?
Big Cost Impact
Typically in this industry they would use this sensor data to maximize operational efficiency. When an airline operator aquires a $100-390 million aircraft for its fleet, the goal is to keep the aircraft up and running for at least 18 hours a day for the next 15-20 years. At the heart of that effort is the airlines maintenance, repair, and overhaul organization, charged with handling both routine and nonroutine issues to keep that fleet up and running.
Based on an analysis of International Air Transport Association (IATA) the leading cause of late flights (42%) are based on airline-controlled processes, such as maintenance. Every hour the aircraft is not in operation costs the airline operator an avg. $10,000.
With the Challenges Come New Opportunities
Predictive analysis can help turn huge amounts of maintenance-relevant data (whether machine data from sensors or logistic information from ERP systems) into actionable information, helping ensure that maintenance technicians execute the right work steps at the right time and with the right tools. Predictive analysis can help drive strategic improvements and provide better-quality output at lower operating cost and improved return on investment.
With the introduction of in-memory technology, the time and cost of analyzing massive quantities of data has been reduced dramatically, and makes it possible to perform predictive analysis against vast volumes of data in real time.
The Race Is On
These days every modern aircraft is equipped with a so called aircraft health monitoring system to monitor unanticipated events, and to reduce unscheduled maintenance and disruption in operations. Accoring to a recent research study from TechNavio the global market for these monitoring systems are expected to grow significantly in the next years. This is mainly based on new efficient aircrafts being produced to cope with the steep rising number of air passengers worldwide. The aerospace OEMs and speciality service players are building new alliances and partnerships to be first to market with novel and compelling Aircraft Health Monitoring offerings. The design concepts that originated in the early 1990s for the Integrated Diagnostics System are moving from theory to practice. The competition is real and now.
The winners of this race will gain a “first to market” advantage capturing dollars and minds.
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