Big data is transforming the aerospace and defense industries by enhancing predictive maintenance, case study, optimizing flight paths, and improving decision-making. This comprehensive review explores its benefits, challenges, and future directions, highlighting the potential for increased safety, reliability, and profitability in aircraft design and operations.
Big Data in the Aerospace and Defense Industries: A Comprehensive Review
Big data in the aerospace and defense industries enhances predictive maintenance, optimizes flight paths, and aids decision-making. Despite challenges like data streaming limitations and cybersecurity, leveraging big data can improve safety, reliability, and profitability, ensuring a sustainable future for aircraft design and operations in this dynamic sector.
Executive Summary
The aerospace and defense industry (ADI), a pioneer in big data adoption, will see its newest aircraft platforms in use for decades. Big data is already integral to these platforms, given the industry’s heavy reliance on sensor-derived information. This report analyzed the current role of big data in the ADI, focusing on its benefits, sustainability, and future direction, alongside an examination of current challenges, gaps, and opportunities.
Key Findings:
- Primary Use: Predictive maintenance on aircraft is the most widespread application of big data in the ADI.
- Challenges: The industry struggles with deriving meaningful insights from the vast data collected and is limited by current data streaming infrastructure.
- Gaps: A critical gap is the failure to stream essential data, such as flight data recorder information, back to ground control using available technology.
- Opportunities: Big data can be leveraged to expand predictive maintenance, enhance data analytics, and optimize both flight plans and air traffic control.
- Recommendations: The industry should invest in infrastructure for big data both on and off the aircraft, and fund research to develop superior models, analytics, and decision-making applications.
The adoption of big data will facilitate increased aircraft automation, leading to higher safety, reliability, and profit margins. As the next generation of aircraft will be designed with big data in mind, today’s investors in this technology will become the future leaders of the ADI.
Introduction
The 2014 disappearance of Malaysia Airlines Flight 370 (MH370) underscored a critical question: why don’t aircraft, which record terabytes of data, transmit this information to the ground immediately? While the infrastructure to support continuous, worldwide data transfer for 100,000 daily flights doesn’t currently exist, the more pertinent inquiry is whether it should. The answer necessitates an analysis of big data’s applications within the Aerospace and Defense Industry (ADI).
The ADI has historically led technological advancements, and big data is no exception. Defined by the five V’s—huge Volume, high Velocity, high Variety, low Veracity, and high Value—big data introduces significant challenges. This report examines the current state of big data in the ADI, providing an analysis of gaps, challenges, and opportunities, while also discussing the benefits and sustainability of its use. A brief history of the industry provides necessary context.
Industry History and Overview
Starting with the Wright brothers’ 1903 flight, the industry rapidly evolved. World War II cemented aircraft as a defining military asset. The subsequent introduction of the jet engine spurred commercial air travel, leading to more sophisticated aircraft. The challenges of managing the vast data from complex aircraft and spacecraft systems presented some of the earliest big data problems.
By the 1990s, the industry entered a mature phase marked by major consolidation due to the fall of the Soviet Union and economic crises. Today, the modern ADI designs and manufactures a range of products, including airplanes, spaceships, missiles, and satellites. Revenue growth is projected at 3% through 2023, driven by air traffic forecasts and plane orders. A few large players, such as Boeing and Airbus, strongly consolidate revenue, while governments and airlines are the primary customers.
The ADI is influenced by significant technological change, globalization, and regulation. High barriers to entry and moderate capital intensity characterize the industry. From a Porter’s Five Forces perspective, rivalry is strong, and buyer power is high. Given these pressures, ADI companies are compelled to seek competitive advantages, making the potential applications of big data enormous. The industry has actively pursued these opportunities.
Status Quo of the Aerospace Industry
The ADI is deeply engaged in big data utilization. A single Boeing 737 generates over 240 terabytes of engine data on a six-hour flight. Extrapolated to all US commercial air traffic, this data volume is equivalent to all global data traffic in 2015.
Applications of big data analysis include:
- Optimizing flight plans and modeling weather effects.
- Determining customer patterns.
- Providing predictive maintenance.
- Monitoring engine parameters for fuel efficiency (e.g., pressure, temperature).
- Tracking part stress and temperature exposure to predict failure.
- Improving operational efficiency through the Internet of Things (IoT).
Predictive Maintenance (IVHM): This is the most widespread application. It uses big data to create models that anticipate component failure, recommending replacement before a failure occurs. This increases safety and reduces unplanned downtime costs. Integrated Vehicle Health Monitoring (IVHM) systems are used on various components, including engines, avionics, and UAVs.
Despite being ahead of most industries, the ADI still faces several unaddressed challenges, opportunities, and serious gaps.
Analysis
The importance of correctly interpreting data is highlighted by the Airbus A380 case. Despite reviewing identical market data as Boeing, Airbus’s decision to build the high-capacity A380 resulted in an estimated $25 billion loss, while Boeing’s choice to develop the medium-sized 787 Dreamliner proved successful. Raw data is useless without correct processing, analysis, and interpretation, which is just one of the challenges the industry faces.
Challenges
Big data remains a developing technology, and the ADI contends with common issues of data, computational, and system complexity. The largest challenges are:
- Limitation on Models to Interpret Data: The vast majority of ADI data goes unused. Developing models to accurately use this data remains a difficult engineering challenge. Predictive maintenance is currently limited to well-understood components because research is still needed to fully characterize how stress, fatigue, wear, and temperature affect part life.
- Data Streaming Limitations: Over-ocean flights rely on expensive, bandwidth-limited satellite streaming, posing a significant challenge for real-time big data use. This limitation restricts applications like in-flight optimization of flight paths based on real-time weather and flight data.
- Cybersecurity Concerns: Investing in flight data streaming raises major cybersecurity considerations due to the sensitive nature of flight security.
Opportunities
A promising future lies in the wide range of opportunities for advancing big data in aerospace:
- Expand Predictive Maintenance: Models are in their infancy. Expanding IVHM from a component level (like engines, the largest cost item) to an overall aircraft level is necessary to prevent unexpected downtime. This requires installing more sensors, upgrading on-aircraft analytics, and improving in-air data streaming.
- Upgrade Aging Aircraft: Proactively upgrading the majority of the world’s aging fleet with big data capabilities would reduce downtime and improve data gathering for IVHM development.
- Optimize Ground and Air Traffic: Combining aircraft data with ground control data can reduce air traffic congestion, increasing airport turnaround times and profitability. Similarly, combining live flight and weather data can optimize in-flight plans for increased efficiency.
- Design the Next Generation: The amassed data presents a “treasure trove” for designing more efficient airplanes. The first manufacturer to effectively use this data will gain a significant competitive advantage. The data can also be used to determine which parameters are actually useful, optimizing future big data investments.
Gap Analysis
Despite cutting-edge technologies, the ADI still relies on antiquated systems, creating noticeable gaps:
- Flight Data Streaming: Today’s connected aircraft record over 300,000 parameters, yet data recovery from lost aircraft still depends on 1950s “black boxes”. The technology to stream black box data (a limited number of parameters at regular intervals) is already in place, demonstrated by engine data transfers. Implementing this simple upgrade would significantly aid accident investigations, help locate lost aircraft (like MH370), and boost public confidence.
- Maintenance Culture: Maintenance personnel often show reluctance to follow predictive maintenance software recommendations to replace a part that has not yet failed. This defeats the purpose of the big data investment and signals a need for more training and awareness.
Direction for Advancement
Investments in big data strategically align with the industry’s goal of increasing profit margins by reducing costs through predictive maintenance and offering manufacturers an added product to sell. They also align with demands for increased efficiency, environmental friendliness, and enhanced safety.
The ADI well-positioned for advancements due to its unmatched human capital resource of scientists, engineers, and software programmers. However, a lack of alignment on data ownership between airlines and manufacturers—coupled with airline concerns about sharing data with competitors—impedes progress. Simple data-sharing agreements or joint ventures could resolve this. Accelerating improvements in analytics and decision-making software.
While alternatives to big data exist (e.g., aggressive maintenance intervals, more robust parts). They either short-sighted, defeat cost-saving goals, or are prohibitively expensive. The comprehensive scope and efficiencies of big data offer a stronger promise.
The industry must now embrace an “all in” commitment to advance analytics and decision-making software. The goal of zero unplanned aircraft out of service is achievable when production, maintenance, flight plans, and air traffic control are all optimized.
Mandatory advancements include:
- Live streaming of some flight data recorder parameters.
- Prioritizing research and development of models to interpret aircraft data.
- Improving infrastructure, both on and off aircraft, for better data streaming and decision-making capabilities.
These advancements will drive further flight automation and cost reduction. Potentially leading to a future where human pilots considered archaic.
Discussion
Big data yields both tangible and intangible benefits, including lower costs, better designs, more efficient aircraft, improved customer satisfaction, and an increased perception of safety.
Benefits
Maintenance issues cost airlines approximately ten thousand dollars per hour of downtime per aircraft. Predictive maintenance provides a strong, long-term return on investment by reducing unplanned downtime, cutting maintenance costs, and allowing for a reduction in spare parts inventory.
Overall, big data benefits materialize through:
- Cost Savings
- Improved Safety and Higher Reliability
- Increased Customer Loyalty (by improving reliability, turnaround times, and reducing flying times).
The company that best utilizes its big data will gain a competitive advantage in designing the next generation of aircraft. Retrofitting older fleets allows them to share in these benefits and creates economies of scale.
Sustainability
The long life cycle of aircraft programs (potentially 60 years) ensures that the investments already made in big data for today’s newest platforms have at least 50 years of sustainability ahead of them. The fierce competition among OEMs, suppliers, airlines, and outside services to provide big data technology demonstrates industry leaders’ recognition of its value. As the technology matures, cost savings will increase, and historical precedent suggests that new safety-enhancing technology will eventually become mandated. These factors ensure big data’s long-term sustainability in the ADI.
Conclusion
Big data has successfully integrated into the aerospace and defense industry, primarily through predictive maintenance. However, the software for analytics and decision support has not kept pace with data collection, leaving much data unused. Furthermore, infrastructure challenges limit real-time data streaming. Opportunities exist to expand predictive maintenance, improve models, and optimize flight and air traffic control.
The industry must close the gap demonstrated by the loss of MH370. Streaming flight recorder data is an achievable improvement using existing technology. While big data would not have prevented the crash, it would provide crucial insights into the cause and location. To design the aircraft of tomorrow, the ADI must invest in big data infrastructure and research to develop superior models and decision-making capabilities. These investments will deliver more automated aircraft, enhanced safety, greater reliability, and increased profit margins, securing a sustainable future where today’s early adopters become tomorrow’s industry leaders.