CAVU Café: Royboy’s Prose & Cons

*Note: The views expressed in CAVU Café: Royboy’s Prose & Cons blog are those solely of the writer and are not necessarily shared by the Aviation Suppliers Association or the Association’s staff, members, or Board of Directors.

   About Roy Resto


When I hear popular culture terms, I tend to borrow the lingo so I don’t seem remote and out of touch. A term I’ve heard bandied-about is ‘TMI’, or Too Much Information. ‘TMI’ might be uttered by you following a conversation with an acquaintance who shared information that was socially awkward, over-the-top, or just plain overwhelming. But this is the age of information, which reportedly is the new gold standard. If so, when is information too much?

Having information or data does not necessarily make you smart; any more than having a big collection of books in your home or office library makes you well-read. Data becomes gold when we put it to work for us. And so it is with the big data being generated by today’s aircraft.

So Royboy, TMI? What TMI? Consider the following:

“An A320 generates information from about 20,000 data sources, Airbus says, compared to 200,000 for an A380 and more than 400,000 for an A350.” 1

How about:

“Take an average jet engine from a commercial airliner, which can be equipped with over 5,000 individual sensors, as an example. According to SAP business software, these engines can generate around 20 terabytes of data per hour.”2

And there you have it; that is big data! Let’s develop this; there are many dots to connect. Here are the topics to be discussed:

  •     Sources Of The Data

  •     Immediate Uses

  •     Collateral Uses

  •     Who Will Mine The Data?

  •     Possible Effects Upon The Aftermarket

  •     Future Enhancements

  •     Broad Acceptance Has Been Slow



In order to gain a proper perspective on the evolution and typical sources of aircraft data, I encourage you to read a previous blog: “AUTONOMIC LOGISTICS”, here’s the link:




Various OEMs are going to call these uses by any number of names, but generically we can refer to the following:


·       Aircraft/Engine/Helicopter Health Monitoring: Simply, the data being generated is telling us something has failed.


·        Predictive Maintenance: Based on trending data, something, if left unaddressed, may fail in the near future.


·      Analytics: This emerging field is software based and designed to sift through the reams of data and perform the required analysis. The desired result is that, in the parlance of the CIA and NSA, you get ‘actionable intelligence’.




Here are the existing and developing uses of all this data:


  • Timely resolution of write-ups/pireps/unscheduled, non-routine maintenance. While in flight, data is transmitted to the ground apprising Maintenance Control that a failure has occurred. Labor, tools, and parts are dispatched or scheduled, and ready sooner.


  • Delays and cancellations: a measureable percentage of these are caused by maintenance issues. With Predictive Maintenance (driven by the big data), the theory is that some maintenance events will be addressed prior to the hard failure; this has great potential to mitigate delays and cancellations due to maintenance. By the way, the biggest cause of delays and cancellations is weather related, unless of course there are labor problems occurring, but don’t get me started on that one!


  • Reliability: Monitoring reliability of the various aircraft systems indexed by ATA Chapter and then the respective LRU is critical to running an efficient, productive, on-time operation. In addition, airframe and engine OEMs choose suppliers based on guaranteed on-wing reliability performance. The data to substantiate such performance comes increasingly from these systems.


  • Warranty: Debates about units under warranty going to the shop and resultant NFFs, No Fault Found (meaning the airline is going to be charged for the service which, if there had been a failure, would have been covered by the warranty agreement) are rife in the industry. But what if collected aircraft data clearly pointed to a certain LRU failure (which is under warranty), which then went to the shop but the shop states its NFF? Will the shop still honor the no charge warranty? Hmmm…fluttering of the eyebrows please…


  • Better analysis of Rogue or Chronic components and/or aircraft. For an in-depth introduction to this topic, see my blog on the issue at:


  • Management of MSG-3 programs. Many airlines model their maintenance programs on the MSG-3 standard, which is driven by data. Moves are afoot to make the big data generated by aircraft systems an authoritative source of such data.




Right now the big airframers offer fee-based programs for operators. The engine OEMs are very strong in similar programs, and perhaps history will write that they indeed pioneered the evolving techniques. Following the airframe and engine OEMs are the OEMs of the systems and LRUs, particularly since the aforementioned issues of reliability and warranty are in play; they are highly motivated to mine the data for those reasons, and to perform what I call ‘Targeted Analytics’.

How about MROs? Many airlines outsource a significant portion of their maintenance programs to MROs. This ranges from line maintenance, to component maintenance, to full airframe and engine overhauls. Will the airline give big data access to the MRO? Will the airline accept the MRO fees for acting on ‘actionable intelligence’ derived from management and analysis of the data?




Currently, other than the aforementioned OEM use of the data, there does not appear to be direct aftermarket use of the data; indeed its use is still evolving. In the near future however, there will be a trickle-down effect upon the accuracy of the following:


  • Pooled parts: The number and types of parts pooled will increasingly be driven by big data.


  • Analysis of spares: This too will be gradually derived from the data.


  • Power By the Hour: The performance data used (for example MTBR or MTBF) as the basis to calculate PBH will be based upon and adjusted on this new source of data.


  • Exchange programs: The use or non-use of exchange programs will progressively be based upon and adjusted by the analysis of spares tied to the data.


  • ALIS: In the blog “AUTONOMIC LOGISTICS” mentioned earlier, there is a critical introduction into the Lockheed F-35’s Autonomic Logistics Information System (ALIS). In Royboy’s opinion:



ALIS is going to be the harbinger and standard-setting system by which such big data integrates into logistics and aftermarket support systems. What works and doesn’t work is going to have a major trickle-down effect on nascent civilian programs.




  • Analytics: Algorithms reflected in software offerings promise to systematically sift through the big data and provide meaningful analysis. These systems continue to evolve.


  • Artificial Intelligence, AI: Analytics will help us arrive at a given conclusion derived from a given set of data. Then what? The conclusion should be actionable; but who’s going to follow through? Enter AI. We are on the cusp of seeing Analytics coupled to AI. A simple possible example: Big data is driving analytics to conclude that an in-service engine’s Exhaust Gas Temperature (EGT) is starting to climb toward upper limits and may fail in the near future. AI steps in, analyzes the aircraft routing, availability of tools and skilled labor at the available maintenance stations, and issues a work order to perform borescope inspections at a suitable overnight station with a spare engine in stock; all without human intervention. We’re not there yet.


  • Sharing data: ATA/A4A Spec 2000 chapter 11 already provides a standardized means for airlines to share and exchange reliability and performance data. According to their website, the purpose and outcome of this reporting is the following:


o   Assist operators and manufacturers to attain and maintain higher reliability through trend monitoring

o   Evaluate if certain problem areas are unique to an operator or observable throughout the industry

o   Monitor which modification has a better payback by comparing removal and failure rates of operators who have incorporated various service bulletins/modifications

o   Determine utilization rates through aircraft flight hours, flight lengths and number of landing cycles

o   Assist operators and manufacturers in determining the effectiveness of aircraft maintenance programs

There is no doubt that the new big data will progressively become the source of such information sharing systems.




Finally, it seems that broad acceptance and implementation of the benefits of aircraft big data has been slower than anticipated1. This is curious since OEMs continue to market their data-driven support systems as producing maintenance savings of approximately 10-15%. How about:


“…with 10 times return on investment for aviation companies and a potential 70 to 75 percent reduction in airplane breakdowns, there is a clear case for automated predictive maintenance in the aviation industry.”2


Sounds convincing, but reluctance may be driven by the following:


  • OEM Support Systems driven by this big data are fee based. I suspect current cost-benefit math is not a slam-dunk selling point…yet. After all, there is the cost-benefit analysis performed by the seller, and one performed by the buyer. Is there a gap to be closed here?


  • It seems there is lingering suspicion that using the data will require a small army of new employee/analysts to monitor the data…yet another system to monitor and dedicate labor to; more overhead (perish the thought!).


  • Lingering suspicion of ‘false positives’. For example, a system used by flight crews (for example, a navigation system) is reported by big data as having failed in flight, but the aircrew did not write it up. Is it actionable? Did I just increase my mx costs by servicing “nuisance faults’?


We’ll see…

Over ‘n out

Roy ‘Royboy” Resto


1 Surely, but Slowly; Aviation Week & Space Technology; April 11-24, 2016; Page 36


Posted By Roy Resto | 12/1/2016 10:34:59 AM

Subscribe By Email