If history is to serve as an example, today’s newest aircraft platforms will extend 50 years into the future. This is good news for the sustainability of big data in the aerospace and defense industry since big data has already found a home on today’s newest aircraft. As an industry that is heavily dependent on huge amounts of information from a wide array of sensors, the aerospace and defense industry were early adopters of big data analytics and decision support applications. This report examined the role that big data plays in the aerospace and defense industry today, as well as the challenges, gaps and opportunities that are present. The benefits, sustainability and directions for advancement in the industry were discussed as well.

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Big data was found to be most widely used in the industry for predictive maintenance on aircraft. The industry faces challenges with meaningfully using the data collected, as well as with the infrastructure to stream the data. Nevertheless, there are opportunities to utilize big data to expand predictive maintenance, improve data analytics, as well as optimize flight plans and improve air traffic control. One of the gaps in the industry is the lack of streaming critical data, such as data from the flight data recorder, back to ground control using existing technology. Recommendations for the industry included investing in the infrastructure for big data both on and off the aircraft, as well as investing in research to develop better models, analytics and decision making applications. The benefits of big data will allow for more automated aircraft while increasing safety, reliability as well as profit margins. The next generation of aircraft will be designed utilizing big data, therefore today’s investors in the technology will be the leaders of the future aerospace and defense industry.

Introduction

In 2014, the disappearance of Malaysia Airlines Flight 370 (MH370) with all 239 people on board captured the world’s attention as investigators scrambled to figure out what had happened to the aircraft (MacLeod, Winter, & Gray, 2014). As theories swirled around the cause of the disappearance, pundits asked why aircraft that record terabytes of data do not transmit the data back to the ground, making it immediately evident what the cause of an accident was. With 100 thousand flights a day worldwide, the infrastructure to support this kind of data transfer simply does not exist, but, perhaps the better question is: should this infrastructure exist? (IATA, 2018). The answer to this question requires an analysis of big data and its applications in the aerospace and defense industry (ADI).

The ADI has long been on the leading edge of technology. When it comes to big data aerospace is once again a pioneer in the field. According to one definition, big data differentiates itself from traditional data by having the 5 Vs, which are “huge Volume, high Velocity, high Variety, low Veracity, and high Value” (Jin, Wah, Cheng, & Wang, 2015, p. 59). Consequently, with big data comes big challenges. This report examines the current state of big data in the ADI and provides an analysis of the gaps, challenges and opportunities of big data in aerospace. The benefits provided by big data, as well as their sustainability, are discussed as well. To provide the context of how the industry got to where it is today, first, a brief history of the industry is presented.

Industry History and Overview

The study of flight goes back millennia, however, it was not until the first successful flight by the Wright brothers in 1903 that the aeronautical industry was born. By 1911, aircraft were being used to deliver mail and by 1915 multi-engine aircraft were being used for commercial passenger transport. Technology in the field rapidly progressed and by World War II aircraft were the defining factor of a country’s military might. Coming out of the war, the jet engine allowed for an explosion in commercial air travel, driving the development of larger and more sophisticated aircraft. In parallel, the space race was taking form creating the field of astronautics, which combined with aeronautics to form the aerospace industry. The thousands of sensors and complex systems needed to control aircraft and spacecraft presented some of the earliest challenges of how to handle mass amounts of data.

By the 1990s, the fall of the Soviet Union and the corresponding drop in military spending, along with an economic crisis on the commercial side of the industry, led to a major consolidation among aerospace companies. The consolidations and dropping margins indicated that the industry had entered the mature phase of its lifecycle. Today, most aerospace companies are involved in both the commercial and defense sides of the industry. The modern ADI designs and manufactures airplanes, spaceships, helicopters, missiles, satellites and defense products. Suppliers provide components ranging from sensors, computers and integrated systems, to engines, structures and interiors. Also, maintenance, repair and overhaul play a big role in the industry (Longo, 2017).

Revenue growth for the industry is expected to occur at a rate of 3% through 2023 (Longo, 2017). The ADI is a highly cyclical industry and is currently in a growth phase. The industry generally follows the global economy and has key drivers such as air traffic forecasts, plane orders and plane deliveries (Corridore & Chuah, 2018). While there are hundreds of players in the industry, revenue is strongly consolidated among a few large players, namely, Boeing, Airbus, Lockheed Martin, United Technologies, BAE Systems and Raytheon (Corridore & Chuah, 2018). On the other end, the primary customers of the industry are governments and airlines.

The factors in the general environment that influence the ADI include economic, global, political, demographic and technological components (Corridore & Chuah, 2018). Due to the nature of the ADI, it is unsurprising that it is characterized by high levels of technology change, globalization and regulations (Longo, 2017). Additionally, consolidation has created high barriers to entry and a medium level of capital intensity (Longo, 2017). From Porter’s Five Forces perspective, rivalry is strong due to shrinking margins and fierce competition. The power of the buyer is strong since customers have options, while the power of the seller is moderate as consolidation continues. Finally, the threat of new entrants and the threat of substitute are low due to the high barriers to entry and the lack of realistic alternatives to flying, respectively.

Powerful buyers, high technology demands and expensive regulation place a lot of pressure on ADI companies to find competitive advantages to maintain their margins. The potential applications of big data in the ADI are enormous, and successful implementation can provide a company the edge it needs. The industry has not ignored this potential nor remained idle to the opportunities of big data. In the next section, the current state of big data in the aerospace industry is discussed.

Status Quo of the Aerospace Industry

The ADI is well entrenched in the utilization of big data. The current amount of data produced by the industry is mind-boggling. Take, for example, the typical Boeing 737, the most widely flown commercial aircraft. The engines alone on a 737 record over 240 terabytes of data over a 6-hour flight (Badea, Zamfiroiu, & Boncea, 2018). Figure 1 shows the vast scope of this amount of data when it is extrapolated to all US commercial air traffic. Put into context, the data amount shown in Figure 1 is equivalent to all global data traffic in 2015 (Badea et al., 2018).

Figure 1. Data generated from aircraft engines alone in the US each year

Source: (Badea et al., 2018)

The applications of big data analysis in the industry include areas such as optimizing flight plans, modeling weather effects on flight, determining customer patterns and providing predictive maintenance (Badea et al., 2018). Big data is used in the field to optimize performance and in the lab as feedback in design optimization (Sethi, 2015). Examples include monitoring engine pressure and temperature to increase fuel efficiency, as well as monitoring stress levels and temperature exposure of parts to predict when they will fail (Sethi, 2015). Furthermore, through the use of Internet of Things (IoT) manufacturers are able to track the performance of their operations and improve efficiencies (How Big Data Is Transforming The Aerospace Industry, n.d.).

Of all the applications of big data noted, predictive maintenance currently has the most widespread use. Predictive maintenance utilizes big data to create models that determine the conditions that precede the failure of an aircraft part (Ezhilarasu, Skaf, & Jennions, 2019). The models then recommend to the operator to replace the part prior to a failure occurring, thereby increasing safety and reducing unplanned downtime costs. These systems, called Integrated Vehicle Health Monitoring (IVHM), are currently used on avionics, aircraft engines, unmanned aerial vehicles (UAV), fuel systems, satellites and spacecraft (Ezhilarasu et al., 2019). SAP’s Predictive Maintenance program is an example of one application commonly used by airlines for IVHM (Badea et al., 2018).

While the aerospace industry is ahead of most industries in the use of big data, there are a number of challenges and opportunities the industry has yet to address. In some areas, there are some serious gaps that have drawn widespread criticism of the industry. The analysis section examines these challenges, opportunities and gaps in detail.

Analysis

The determination to build the Airbus A380, the largest passenger jet in the world, emanated from a joint market study completed by Airbus and Boeing (EASA, 2017; Reuters, 1995). Airbus determined that the market was looking for a high-capacity jet and developed the A380 while Boeing determined the market was looking for medium jets and developed the 787 Dreamliner. In 2019, Airbus announced it would shut down the production of its A380 jets after producing just 30 aircraft at an estimated loss of $25 billion (Airbus, 2019; West, 2014). Meanwhile, Boeing has produced 840 of the 787 aircraft and continues to ramp up production (Ostrower, 2014). Despite looking at identical data sets, the companies drew different conclusions from the data. Unfortunately for Airbus, their misinterpretation cost them $25 billion and 21 years of development.

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Raw data on its own is meaningless. The data must be processed, analyzed and correctly interpreted. Yet correctly interpreting the data is just one component of the challenges that big data presents to the aerospace industry. The analysis that follows digs into the details of the challenges, opportunities and gaps of big data in aerospace, and provides resulting recommendations for advancement.

Challenges

As with industries everywhere, big data is still a developing technology in aerospace (Urbinati, Bogers, Chiesa, & Frattini, 2019). Huge amounts of data are collected in the ADI raising common challenges with data complexity, computational complexity and system complexity (Jin et al., 2015). The largest challenges of big data in the aerospace industry are limitation on models to interpret data, data streaming limitation and realtime use of data.

There is no doubt that the ADI generates astronomical amounts of data, yet the majority of the data goes unused (Badea et al., 2018). Creating models and tools to make accurate use of the data has proved to be a difficult engineering challenge (Taylor, & Waldron, 2019). There is still plenty of research to be done to create models to determine how stress, fatigue, wear and temperature affect the useful life of a part. Until then, predictive maintenance is limited to well-understood areas and components on an aircraft (Ezhilarasu et al., 2019).

When a plane flies over an ocean, satellite streaming is its primary way of transferring data. Satellite streaming is both expensive and limited in bandwidth (Taylor, & Waldron, 2019). This presents a significant challenge for realtime big data use, limiting applications of the data to the capabilities on the aircraft. One resulting limitation is that it prevents the optimization of flight paths based on weather conditions and flight data (Badea et al., 2018). Furthermore, even when the cost investment is made into streaming flight data, concerns over the cybersecurity of the data are a major consideration due to the sensitive security nature of flight (Taylor, & Waldron, 2019). Yet, with challenges come opportunities, which are discussed next.

Opportunities

The wide range of opportunities for the advancement and use of big data in aerospace paints a promising picture for the industry. Predictive maintenance models and algorithms are still in the infancy stage of application; their potential for widespread use on the aircraft is enormous. To date, a large focus for predictive maintenance has been on the aircraft engines since they are the single largest cost items on the aircraft (Ezhilarasu et al., 2019). According to Ezhilarasu et al., the expansion of predictive maintenance from a component level to the overall aircraft level is necessary to accomplish the goal of preventing unexpected downtime (Ezhilarasu et al., 2019). To get IVHM to the aircraft level requires an installation of sensors throughout the aircraft, upgraded analytical capabilities on the aircraft and improvements of in-air data streaming.

An additional benefit of increasing the sensing capabilities throughout the aircraft is that, over time, the data collected will allow for better characterization of failure modes thereby increasing the effectiveness of the IVHM models. This brings up another opportunity – the majority of the world’s aircraft fleet is still made up of aging aircraft with limited built-in capability to handle big data (Taylor, & Waldron, 2019). Proactively upgrading aircraft that may have another 15 years of life ahead of them will help with downtime reductions while improving data gathering for IVHM capability improvements.

Another opportunity is to utilize the data output from the aircraft, combined with ground control data, to reduce air traffic congestion and thereby increase airport turnaround times for aircraft (Badea et al., 2018). Better turnaround times result in higher profitability (Schlesinger, 2011). A similar combination of live flight data with weather data can be used to optimize flight plans inflight, thereby increasing aircraft efficiency (Badea et al., 2018). Both of these opportunities, once again, utilize existing data systems but require new programs to implement the optimizations.

With the amassed amount of data already collected by the ADI, there is a huge opportunity to put that data to use. The first opportunity this presents is determining what parameters of the data collected is useful. If certain parameters are deemed unneeded, manufacturers can optimize the investment in big data capabilities on the aircraft (Sethi, 2015). The second opportunity presented by this treasure trove of data is using analytics to design the next generation of efficient airplanes (Sethi, 2015). The first-to-market that utilizes this opportunity will likely obtain a new competitive advantage.

Gap Analysis

Today’s digitally connected aircraft record data on over 300,000 parameters (Badea et al., 2018). However, data recovery from a lost aircraft is done through the “black boxes” – the flight data recorder and cockpit voice recorder – technologies that were developed in the 1950s (Yu, 2015). Herein lies a gap that plagues the aerospace industry: while some technologies used are on the cutting edge, there is still widespread use of antiquated technology. In a safety above all else industry, some level of relying on tried and true over new can be expected, but in areas like the black boxes, the industry is just laggard.

The technology to stream black box information is already in place, as evidenced by live data transfers from aircraft engines. Neither continuous streaming nor every parameter is required; a limited number of parameters transferred at regular intervals into big data infrastructure would greatly improve accident investigations and help in locating lost aircraft (Yu, 2019). A simple upgrade would increase public confidence in flying and provide closure to families of accident victims, like those of MH370.

Another gap exists in the maintenance culture of airlines. Even where predictive maintenance powered by big data exists, maintenance personnel are reluctant to follow the recommendations of the software and replace a part that has not failed yet (Taylor, & Waldron, 2019). Obviously, this defeats the purpose of the investment in big data capabilities and demonstrates the need for more training and awareness on big data. These gaps, along with the challenges and opportunities, provide the directions for advancement in the industry, which are discussed next.

Direction for Advancement

Aircraft manufacturers have been combating shrinking margins for some time now (Longo, 2017). They have done this by pressuring suppliers for price concession and suppliers have responded with mergers to provide them with more negotiating power (Corridore & Chuah, 2018). Likewise, the airlines have historically struggled with low profit margins. The investments in big data, therefore, provides strategic alignment with the goals of increasing profit margins. It does this by providing airline customers with ways to reduce their costs through predictive maintenance while providing the manufacturer with an add-on product to sell. Furthermore, improvements from big data align with the demands of the general environment to increase the efficiency and environmental friendliness of aircraft. With ever increasing safety regulations, big data also provides the opportunity for aerospace companies to stay ahead in terms of safety.

As pioneers in the field of big data, the ADI is well positioned to make use of its resources to push advancements in big data collection, analytics and decision making software. For one, the ADI has an unmatched resource when it comes to scientists, engineers and software programmers (Aerospace Industries Association, 2016). This human capital resource puts the industry in a strong position to tackle big data challenges.

The industry has some work to do, however, when it comes to alignment of companies within the industry. Big data in the industry is most useful when it is shared between the airlines and manufacturers (Taylor, & Waldron, 2019). It allows the manufacturers to predict part demands and allows them to improve their designs. However, there is a debate between the airlines and manufacturers over who owns the data, as well as concerns by airlines of sharing data with manufacturers that also serve their competitors (Taylor, & Waldron, 2019). This has resulted in a hesitancy to share data that has the potential to impede progress with big data. Solutions to create alignment in the industry can be as simple as data sharing agreements or even joint ventures between the airlines and manufacturers. This will allow for more rapid improvement to the analytics and decision making software for in the industry.

Big data and its application do face a number of criticisms in the ADI. Some airlines have criticized the focus on aircraft analytics over ground operation analytics, which can provide cost savings as well (Taylor, & Waldron, 2019). Still others level the fair criticism over the mass amounts of data collected, much of which does not provide actionable insights (Taylor, & Waldron, 2019). Of course, there are alternatives to big data.

One alternative, albeit not a very strong one, is to continue using traditional analytics while becoming more aggressive on maintenance intervals. While cheaper in terms of investment, this alternative adopts a short outlook. It will require stocking parts based on historical needs, so will, therefore, defeat the purpose of reducing inventory costs and unplanned downtime. Another alternative is to engineer more robust parts and error-proof the manufacturing process. However, weight and cost increases in this approach can be prohibitive, making this a poor alternative as well. For each use of big data, an alternative can be found. However, the efficiencies and widely encompassing scope of big data provide stronger promise than any of the alternatives proposed to date.

The results that big data have shown so far in the ADI, illustrated that it is a train that cannot be stopped. It is the next step in the technological development of aircraft. What is needed now is one of the large manufacturers to go “all in” and take the helm in advancing the analytics and decision making software so that it can be used at multiple tiers in the ADI (Taylor, & Waldron, 2019). The goal of zero unplanned aircraft out of service is achievable when production, ground maintenance, predictive maintenance, flight plans and air traffic control have been optimized through the use of big data.

Some directions for advancements should be “go dos” for the industry, such as live streaming some aspects of the flight data recorders (Yu, 2015). Dedicating resources to research and development of models to interpret the data coming off of aircraft should be a high priority for the industry. Improvement of the infrastructure, on and off aircraft, to improve data streaming and decision making capabilities, go hand in hand with these models. The advancements gained through big data will allow for further flight automation and reduced costs. As the field advances, the day may come when the idea of a human at the controls in the cockpit will seem arcane. The benefits do not stop at automation alone; the benefits are far reaching and are discussed in detail next.

Discussion

Lower costs, better designs and more efficient aircraft are among the tangible benefits derived from big data. Customer satisfaction, customer loyalty and an increased perception of safety are some of the intangible benefits derived from big data. The following sections go into further detail on the benefits and sustainability of investments into big data analytics and decision support applications.

Benefits

Maintenance issues cost airlines a lot of money, to the tune of ten thousand dollars per hour of downtime per aircraft (Badea et al., 2018). Clearly, there is a lot of money at stake in keeping aircraft flying. That is why the return on investment for big data analytics and decision support applications has such a strong case for the long term. Predictive maintenance programs powered by big data have already shown promising returns by reducing unplanned downtimes and reducing maintenance costs. The expansion of predictive maintenance will only increase these returns. Additionally, predicting when parts will be replaced also allows a reduction in spare parts inventory.

The benefits of big data in aerospace materialize through cost savings, improved safety and higher reliability. Due to its intangible nature, a price cannot be placed on the effect the loss of consumer confidence has on an airline that suffers an accident. Utilizing big data to improve aircraft reliability, aircraft turnarounds times and reduce flying times will increase customer satisfaction and loyalty, resulting in direct improvements to a company’s bottom line.

The next generation of aircraft will be designed using the data generated from today’s aircraft. The company that best puts to use its big data will achieve a competitive advantage in the industry. Furthermore, retrofitting older fleets will allow them to share in the same benefits as the newer aircraft while also generating economies of scale (Taylor, & Waldron, 2019). The many benefits of big data analytics and decision support applications provide sustainable returns over the long run, as detailed in the next section.

Sustainability

The life expectancy of an aircraft is typically 20 years. With production runs going for over 40 years, the life of a program can be expected to last 60 years. This means that investments into big data that have already been made for today’s newest aircraft have at least another 50 years ahead of them. With a time horizon that large, from a business perspective, this means that big data is here to stay in aerospace.

The future looks even more promising when the current level of investment by the ADI in big data is considered. The competition is fierce to provide big data technology, and the corresponding analytical and decision making services, on the newest aircraft platforms (Peaford, 2018). The aircraft OEMs, suppliers, airlines and even outside services are competing against each other in this arena (Peaford, 2018). Clearly, the industry leaders have recognized the value of big data and are investing huge amounts into their technology and service capabilities. As the technology matures, so will the cost savings. Furthermore, as has often been the case in the ADI, when a new technology demonstrated a higher level of safety, it eventually becomes mandated. All these factors combined bode well for the long-term sustainability of big data in the ADI.

Conclusion

Big data has found a home in the aerospace and defense industry where it has found widespread use in the form of predictive maintenance. Yet, the analytics and support decision support applications to utilize big data lag behind the data collection efforts in the industry, leaving large amounts of data unused. Furthermore, aircraft face challenges with the current capabilities of data streaming infrastructure, limiting the use of big data in real-time. However, opportunities exist to expand the predictive maintenance network on the aircraft, improve the predictive models, as well as optimize flight plans and improve air traffic control.

Gaps exist in the industry as was demonstrated after the loss of flight MH370. Streaming of flight recorder data can be done with existing technology and is an improvement the ADI should implement. Big data technology in its current form would not have prevented the tragic loss of flight MH370, but it could have shed light on why the aircraft crashed, as well as provide the location of the crash, thereby providing closure for the families who lost loved ones. Big data will design tomorrow’s aircraft. The ADI should invest in big data infrastructure on and off the aircraft, as well as invest in research to create better models and decision making capabilities. The results will allow for more automated aircraft while increasing safety, reliability as well as profit margins. Big data has a sustainable and promising future in the ADI. Therefore, the early adopters of big data today will be the industry leaders of tomorrow.

References

Aerospace Industries Association. (2016). The Defining Workforce Challenge in U.S. Aerospace & Defense. Retrieved from http://www.aia-aerospace.org/wp-content/uploads/2016/09/STEM_Report_lowres_V11.pdf.
Airbus. (2019). Airbus and Emirates Reach Agreement on A380 Fleet, Sign New Widebody Orders. Retrieved from https://www.airbus.com/newsroom/press-releases/en/2019/02/airbus-and-emirates-reach-agreement-on-a380-fleet–sign-new-widebody-orders.html
Badea, V., Zamfiroiu, A., & Boncea, R. (2018). Big Data in the Aerospace Industry. Informatica Economică, 22(1), 17-24. Retrieved from https://doi-org.ezproxy.lib.uwf.edu/10.12948/issn14531305/22.1.2018.02
Corridore, J., & Chuah, S. Y. (2018, April). Media. Standard & Poor’s Industry Surveys. Retrieved January 25, 2019 from Standard and Poor’s NetAdvantage.
EASA. (2017). Type-certificate Data sheet: Airbus A380 (Report No. EASA.A.110). Retrieved from https://web.archive.org/web/20180219151308/https://www.easa.europa.eu/sites/default/files/dfu/TCDS_EASA%20A%20110_A380_Iss_12.pdf
Ezhilarasu, C., Skaf, Z., Jennions, I. (2019). The application of reasoning to aerospace Integrated Vehicle Health Management (IVHM): Challenges and opportunities. Progress in Aerospace Sciences, 105(1), 60-73. Retrieved from https://www-sciencedirect-com.ezproxy.lib.uwf.edu/science/article/pii/S0376042118301337#sec4
How Big Data Is Transforming The Aerospace Industry. (n.d.). Retrieved from https://www.opentracker.net/article/how-big-data-is-transforming-the-aerospace-industry
IATA. (2018). Traveler Numbers Reach New Heights. Retrieved from https://www.iata.org/pressroom/pr/Pages/2018-09-06-01.aspx
Jin, X., Wah, B., Cheng, X., & Wang, Y. (2015). Significance and Challenges of Big Data Research. Big Data Research, 2(2), 59-64. Retrieved from https://www-sciencedirect-com.ezproxy.lib.uwf.edu/science/article/pii/S2214579615000076
Longo, D. (2017, August). Aircraft, Engine & Parts Manufacturing in the US: 33641a. IBISWorld Industry report. Retrieved February 1, 2019 from IBISWorld.
MacLeod, C., Winter, M., & Gray, A. (2014, March 7). Beijing-bound flight from Malaysia missing. Retrieved from https://www.usatoday.com/story/news/world/2014/03/07/malaysia-airlines-beijing-flight-missing/6187779/
Ostrower, J. (2014, Jan 7). Boeing’s Key Mission: Cut Dreamliner Cost. https://www.wsj.com/articles/boeing8217s-key-mission-cut-dreamliner-cost-1389143509?tesla=y
Peaford, A. (2018, March 14). Big data: Two little words, huge impact in aerospace. Retrieved from https://www.wearefinn.com/topics/posts/big-data-two-little-words-huge-impact-in-aerospace/
Reuters. (1995). Boeing, Partners Expected to Scrap Super-Jet Study. Retrieved from https://www.latimes.com/archives/la-xpm-1995-07-10-fi-22333-story.html
Schlesinger, J. (2011, July 15). 10 Minutes That Changed Southwest Airlines’ Future. Retrieved from https://www.cnbc.com/id/43768488.
Sethi, C. (2015, Dec 11). Aerospace Bets on Big Data. Retrieved from https://www.asme.org/topics-resources/content/aerospace-bets-on-big-data.
Taylor, E., & Waldron, G. (2019, March 26). ANALYSIS: Five key themes for Big Data in aerospace. Retrieved from https://www.flightglobal.com/news/articles/analysis-five-key-themes-for-big-data-in-aerospace-456869/
Urbinati, A., Bogers, M., Chiesa, V., & Frattini, F. (2019). Creating and capturing value from Big Data: A multiple-case study analysis of provider companies. Technovation, 84(1), 21-36. Retrieved from https://www-sciencedirect-com.ezproxy.lib.uwf.edu/science/article/pii/S0166497218300361#s0080
West, K. (2014, Dec 28). Airbus’s Flagship Plane May Be Too Big To Be Profitable. Retrieved from https://www.businessinsider.com/airbuss-flagship-plane-may-be-too-big-to-be-profitable-2014-12
Yu, Y. (2015). The Aftermath of the Missing Flight MH370: What Can Engineers Do? IEEE, 103(11), 1948-1951. Retrieved from https://ieeexplore.ieee.org/abstract/document/7302632