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Maintenance

Number crunching

The use of big data has matured to the point where analytical services are being offered that allow predictive maintenance. Mario Pierobon reports
 

Data analytics is helping to reveal insights which empower a wide range of possibilities for reducing uncertainty in air operations.

 

With real-time data and new analytics tools and applications, the practice of predictive failure analysis (PFA) is making it possible to move from looking primarily for emerging fleet issues to preventing or reducing schedule disruptions from unscheduled maintenance events per aircraft in real-time.

 

Established practice


The use of data analytic methods, such as statistical analysis, in helping to predict and avoid unscheduled maintenance events, or to improve the performance and design of aircraft, is not a new practice in the industry. What is relatively new – and developing critical mass – is the level of sophistication of the analytics tools available, which now enable work on a near to or actual real-time basis. John Maggiore, Managing Director of Maintenance and Leasing Solutions at Boeing Digital Aviation and Analytics, says: “Boeing has a long history of using analytics to design and build more reliable, efficient aircraft, and in providing services to help improve the operation and maintenance of commercial and non-commercial aviation. In the mid-1990s, Boeing, in collaboration with its 777 customers, began sharing data via the In-Service Data Program. Sharing operational data allowed for additional analysis that helped further boost the dispatch reliability of this successful family of airplanes. This programme has since expanded to cover most Boeing models. We recently cored up these capabilities into our Boeing AnalytX suite of products.” 

 

He continues, “One of our key analytics tools in commercial aviation, Airplane Health Management (AHM), applies descriptive and prescriptive analytics to real-time airplane data, providing maintenance data and decision support to airlines to increase operational efficiency. This enables proactive maintenance management and maintenance scheduling to avoid schedule disruptions. Proactive management is like having an over the horizon radar, it provides more time to assess, plan, and manage, rather than react to a situation. AHM uses analytics to evaluate two million conditions each day to determine when alerts should be generated across 4,300 airplanes. AHM is our key predictive fault analysis tool.”

 

Boeing’s AHM is a web-based decision-support tool designed to help airlines make more efficient real-time fix or fly decisions, and provides a range of predictive analytical tools to help airlines identify and act upon maintenance conditions before they turn into faults which can cause delays and cancellations. AHM is offered as a subscription service to operators. “AHM leverages existing systems on the airplanes and as such, in-production airplanes require no additional equipage use it. AHM is one of our Boeing AnalytX-powered products. Boeing AnalytX, which brings together various products powered by data analytics and the 800 data scientists who work on them, is accelerating innovation and our predictive analytics capabilities,” says Maggiore.

 

Boeing introduced AHM in 2003. At the time, the tool represented a first generation of health monitoring solutions focused on real-time identification and resolution of maintenance issues with aircraft in-flight. Over the years, the adaptation of AHM has changed customer operations to the point that using health monitoring to address real-time maintenance issues is standard practice today. “In fact, it is hard to find a 777 or 787 operator that does not use AHM. To develop reliable, actionable predictive alerts, Boeing identifies airplane system data that can be used to infer system or component degradation. Our engineers use this to develop a predictive alert, test and tune it until it is ready for integration into the AHM application for all to use. We have been doing this for over a decade, and now have thousands of diagnostic and prognostic alerts in our library, spanning all our production airplanes and including some legacy airplanes,” Maggiore says. “AHM is powered by Boeing AnalytX, which is the Boeing global programme that fuses data analytics and aerospace expertise together to create new insights, opportunities, and products that further advance the aviation industry.”

 

Increased sophistication


PFA has been around for a long time using what was traditionally known as the physics-driven modelling technique. “As aircraft systems and components generate and capture more data, connectivity becomes faster and more affordable. Moreover, as computer storage and memory becomes cheaper, we can complement the physics-driven models with data-driven models, to not only improve our failure prediction accuracies, but also to move into predicting sub-components failures,” says Fong Li Wee, Director of Information Technology, Connected Aircraft at Honeywell Aerospace. 

 

PFAs are combinations of weak signals from aircraft systems that, once fed into models, can indicate if and when a system will fail. “Current Airbus predictive solutions in Skywise provide high dependability on the prediction enough in advance, providing the airline with the ability to take timely and informed decisions. Also, when connected with other systems in the airline, they provide actionable items so that benefits can be scaled and made systematic after every flight. Legacy systems were looking at a reduced number of parameters and therefore had limited coverage across the aircraft systems. Also, they were unable to systematically provide guidance without generating significant amounts of No Fault Found,” says Jaime Baringo, Head of Digital Business Development at Airbus. 

 

The company recently launched Skywise, which collects data from across the systems in Airbus A320  Family aircraft and, using the Rockwell Collins Flight Operations and Maintenance Exchanger (FOMAX) program, transmits the data to Airbus.

 

Baringo continues, “The power of the analytics contained in FOMAX- Skywise allows airlines to identify predictive models which, combined with the OEM’s expertise, can quickly make new models available and hence grow prediction coverage exponentially.”

 

With new generation aircraft such as the A380, the 787 and the A350, more data is available to predict failures on components before they fail. “Now, with the quantity of data available, the automatic transfer of data, the decreasing cost to store data and to perform the corresponding analytics, AFI KLM E&M has been able to develop its own solution, PROGNOS, which predicts failures which are not seen by legacy solutions,” says James Kornberg, Director Innovations at Air France Industries KLM Engineering & Maintenance (AFI KLM E&M). “Being both an airline and MRO performing strong IT operational research with data scientists and engineers who know the aircraft and engine systems, AFI KLM E&M has been able to develop PROGNOS, which also relies on all maintenance data available such as line maintenance complaints, hangar findings, fault found confirmation, shop reports, etc.”

 

The legacy approach to predictive failure analysis has relied heavily on known failure conditions, whereas advances in data management and analytical methods are revolutionising how predictive analytics are applied. “Modern data recording and processing systems onboard the airplanes, combined with ground-based analytics platforms using technology such as machine learning and natural language processing, have resulted in sophisticated tools. These tools are used by airline maintenance teams and Boeing/supplier support teams during day of operation, and are used to resolve in-service issues and inform future aircraft design. Advances in onboard and offboard data processing and analysis can drastically reduce the flowtime for alert development. But a reliable infrastructure and process for predictive alert maturation is vital as airlines are using these alerts to make real-time decisions on whether to spend resources to change out a component – which can sometimes cost several hundred thousand dollars, prior to failure,” says Maggiore. >>
 


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