More and more operations are seeing the benefit of meshing their MRO software with their flight operations IT solutions. Together, the two offer more synergy than perhaps any other pairing to ensure safety, efficiency and profitability. We will take a look at how these two seemingly disparate products can work together to create amazing synergy.
Stottler Henke Associates was awarded a contract with the U.S. Navy to customize and demonstrate a critical chain and critical path scheduling capability for surface ship maintenance operations. Stottler Henke’s solution is based on its Aurora intelligent planning and scheduling system, combined with built-in Critical Chain Project Management (CCPM) capabilities.
During the project, a series of demonstrations and Government tests will verify the system’s ability to solve large, resource-constrained scheduling problems, generate numerous reports and displays to provide an integrated picture of the Navy’s surface ship maintenance demands, support tens of thousands of users, and provide system administration tools. If the evaluation is successful, the Navy is expected to contract Stottler Henke to develop and deploy a customized, operational version of the system at naval shipyards, regional maintenance centers, and ship facilities.
Navy ship maintenance is extremely labor and resource intensive, and ship maintenance schedules must satisfy complex constraints. For example, many maintenance operations require different combinations of limited resources such as maintenance shops, equipment, and maintenance teams with specific skills and certifications. Large parts are removed from the ship and transported to specialized maintenance shops where they are reconditioned or repaired. Other work is performed on board in small spaces, so spatial reasoning is needed to schedule those tasks.
The US Navy has successfully used the Critical Chain Project Management (CCPM) methodology to manage other operations efficiently, so they desire a ship maintenance scheduling software system that supports CCPM. The Critical Chain method consolidates per-task safety margins into a smaller number of aggregate safety margins. By allocating safety times strategically, organizations achieve higher throughput. Also, CCPM provides organizations with greater visibility into how their projects are performing, and it provides global metrics to guide and motivate all parties to do what is best for the entire organization. For this project, Stottler Henke is teaming with Main Sail who contributes expertise in Critical Chain Project Management and Theory of Constraints.
Like complex operations at many organizations, management of Navy ship maintenance requires resource-constrained scheduling of tens of thousands of activities, which is beyond the ability of ordinary CCPM software. Stottler Henke’s solution for the Navy is based on Aurora-CCPM which combines Stottler Henke’s Aurora intelligent planning and scheduling system (www.stottlerhenke.com/products/aurora) with the added power and flexibility of multi-project Critical Chain Project Management.
Aurora is the world’s leading planning and scheduling system that uses artificial intelligence to generate efficient solutions to complex, highly constrained scheduling problems. Most other systems use simple rules to schedule activities and assign resources to carry them out. Often, these schedules and resource assignments are far from optimal. Aurora outperforms conventional software because it uses artificial intelligence technologies to encode and apply extensive scheduling knowledge.
“We are delighted that the US Navy is evaluating Aurora software for managing their extensive ship maintenance operations,” says Richard Stottler, president, Stottler Henke. “Aurora’s artificial intelligence scheduling technology will improve the Navy’s readiness, save taxpayer dollars, and enable superior operations management, as already seen by the US Air Force, aircraft manufacturers, and many industries,” continues Stottler.
Aurora scheduling software was originally developed to help NASA tackle difficult, mission-critical scheduling problems with complex constraints by incorporating the judgment and experience of expert human schedulers. NASA used Aurora at Kennedy Space Center to schedule International Space Station payload and Shuttle processing activities. The Boeing Company uses Aurora to help them manage aspects of building the Boeing 787 Dreamliner™ commercial airliner. Other Aurora software users include Pfizer, Mitsubishi Heavy Industries, Korea Aerospace Industries, Massachusetts General Hospital, Bombardier Learjet, Alaska Airlines, the US Air Force, and the US Navy. Aurora was featured in NASA’s Hallmarks of Success video series which showcases successful spin-off technologies.
ACIA contracted AD SOFTWARE to implement and support the modules of its AIRPACK software suite. AD SOFTWARE ability to provide an aviation dedicated solution adapted to the specificities of ACIA’s business was key in the decision process. For several months AD SOFTWARE and ACIA’s teams met to define the best way forward, finalizing the partnership late 2019.
ACIA Technics and Leasing was registered in early 2017 to assist with technical management services and oversight. It oversees and manages the heavy maintenance checks for group owner aircraft as well as technical and CAMO oversight at Aircraft acquisitions, disposals and leasing including oversight of the IPR conversions program with applicable conversion centers.With this agreement AD SOFTWARE increases its presence on the ATR market and in the French Aerospace Valley. The close relationship between ATR and AD SOFTWARE was one of the key decision criteria for ACIA as well as the ability to develop professional and technical interactions between key individuals. For the last few years, AD SOFTWARE has been expanding its software to cover production activities of Part 145 infrastructures: mobile applications for mechanics, cost control dashboards and real-time progression insights for heavy check and line maintenance. Furthermore, AD SOFTWARE has teamed-up with ACIA to build tailored solutions for aircraft conversions with the end-users in mind. The new modules have a fast time-to-market planning and will be designed by aviation professionals. Frederic Ulrich, CEO of ADS OFTWARE, commented: “We’ve known ACIA for a long time, their careful decision is a recognition of our ability to provide off-the-shelf solutions and an aviation dedicated IT developing capability.”
DP Technology announces a comprehensive product update, called ESPRIT 2020, for their computer-aided manufacturing (CAM) software. Among the most significant developments are updates to the software’s computer-aided design (CAD) interfaces and new or improved solutions for specific machine tools.
ESPRIT 2020 features plentiful updates for Swiss-type machining. This technique is defined by its small, often intricate parts. Medical devices, such as bone screws, are typically manufactured on Swiss-type machines. ESPRIT’s 2020 update introduces or enhances support for 200 different Swiss-type machine models, including:
Citizen D25, which features three channels, 3x Y-axis, 3x Z-axis, B-axis front and back
Star SV 38R, which features three channels and a B-axis
Tsugami SS38, a chucker-convertible sliding headstock lathe with B-axis
Seamless integration of laser cutting operations for Tsugami and Citizen
● Willemin-Macodel MT series machines
The 2020 update also includes updated support for the latest CAD software, including:
PTC Creo 6
Improved CAD support allows users to better design and visualize parts before manufacturing begins. CAD modeling is an integral part of designing and optimizing any new part, and different software types specialize in different modeling techniques.
Additionally, ESPRIT 2020 expands mill-turn support to the following machines:
Index G200 and G220, featuring two and three turrets and a disk turret mounted on a B-axis
Miyano BNE 51 MSY, featuring three X-axes and three Z-axes
Traub TNX, featuring simultaneous independent machining with up to four tool carriers
CMZ TTL, featuring two turrets and two spindles
Enhanced profile threading and probing capabilities round out the product release.
“Product releases are always exciting for us, because often we’re providing a solution where one did not previously exist,” says Tania Campanelli, Director of Research and Development at DP Technology. “2020 is no different. We look forward to more users discovering why ESPRIT is the right choice.”
An innovation that helps speed the design of fishing rods, satellites and cellphone electronics soon will help the U.S. Navy save millions in costs and downtime, while extending the service life of helicopters.
AnalySwift LLC, a Purdue University-affiliated commercial software provider, has received a $240,000 Small Business Innovation Research program grant from the Navy. The SBIR award will help the company further develop its SwiftComp software, technology that provides efficient, high-fidelity modeling of composites.
“We are excited to partner with the U.S. Navy to help address this challenge,” said Allan Wood, president and CEO of AnalySwift. “The Navy is going to be able to use the resulting software technology to properly align a helicopter’s predicted life to actual service life, reduce downtime in redesigns and, ultimately, save money.”
Wood said the software would help meet a need by the Navy and others involved with rotorcraft. The specific project is aimed at advancing the software to better predict the durability of flexbeams made from composite materials, which are materials made from two or more different materials that when combined are stronger, lighter or have other advantages over those individual materials by themselves.
A helicopter flexbeam is the critical component that connects the blade with the hub. Flexbeams made from composites are particularly difficult to design and analyze due to their complexity, including their tapered and curved nature and complex microstructures.
While NAVAIR policy for durability determined by analysis typically requires the analysis to show four times the service life required, the reality is that testing shows actual life well below required service life and what was analytically predicted. This discrepancy between predicted life and tested life has cost both time and money in redesign, with efforts spanning years and costing millions of dollars.
Attempts to address these shortcomings have included changes in the ply layup as well as the locations of ply drops with respect to the neutral axis to improve life. A lack of physical understanding of the physics involved in flexbeam fatigue failure prevents basing the redesign on a more accurate analysis method or understanding than originally used to cleared the failed part.
Instead, the same analysis used to show that the failed part had sufficient life is reused on the newly designed part — historically with little success. That analysis is inadequate because these are complicated composite structures with hundreds of plies, often hybrid materials, and twisted and tapered geometry. Additionally, the loading environment, while understood, is equally complex with axial, bending and torsion loads. This loading leads to multiaxial stress that, combined with the geometry of flexbeams, makes determining stresses/strains at the ply level of first importance, but is often ignored.
“Our specific project aims to enable an efficient high-fidelity tool set with significantly improved durability predictive capabilities for composite flexbeams using user-defined elements,” Wood said. “Success of this proposal will produce a practical solution for efficient yet accurate durability analysis of composite flexbeams.”
In addition to better strength and durability analysis for curved and tapered composite structures such as composite flexbeams, the project aims to enable:
* Significantly reduced time and cost used for design and redesign of complex composite structures.
* More insightful guidance for experiments in understanding the effects of ply drop-offs and other defects of composite flexbeams.
* More explicit modeling of internal features and defects, easy handling of hybrid materials and direct incorporation of new material models.
Although the direct commercial application is durability analysis of composite flexbeams used by the Navy, the proposed work will have many other potential commercial applications:
* Composite helicopter rotor blades, which are usually tapered with ply drop-offs along the span-wise direction.
* Composite wind turbine blades with cross-sections varying significantly.
* Complex composite structures featuring non-uniform cross sections used in aerospace, automotive, and sports.
* Thick composite structures where ply-level stress and durability prediction is critical.
The technology was developed by Wenbin Yu, a professor of aeronautics and astronautics in Purdue’s College of Engineering.
The software also has been licensed to companies and universities worldwide, including those using it for work on satellites and mobile phone components, including printed circuit boards.
“One of the advantages of the SwiftComp software is its ability to carry out efficient high-fidelity multiscale modeling for structures featuring complex microstructures,” Yu said. “SwiftComp takes details of the fundamental building block of materials and structures as input, then outputs the structural properties needed for macroscopic analysis. It can be used for composite beams, plates and shells, and 3D structures, for both micromechanical and structural modeling. This project will help expand the application of SwiftComp even further for composites used in rotorcraft and other applications with curved or tapering structures, as well as applications where a clear understanding of durability is critical.”
Honeywell is bringing clarity and control to business aviation flight departments with the introduction of Honeywell Forge. The data-driven analytics platform provides a full suite of mission-management capabilities in the areas of connectivity, flight operations, navigation databases and maintenance, empowering flight departments to improve operational inefficiencies.
Honeywell says Forge provides business aviation customers with an easy-to-use, integrated dashboard that sends real-time alerts on connectivity issues and flight plan changes. With full visibility into their services, customers can use the platform to tap into data that helps flight departments troubleshoot and fix issues as soon as they arise. Based on these insights, Honeywell Forge can improve the passenger connectivity experience, help manage profitability and give flight departments a better understanding of their fleet.
“We understand that flight departments need a holistic solution that combines the entire fleet operation into a single view,” said John Peterson, vice president and general manager, Software and Services at Honeywell Connected Enterprise, Aerospace. “Honeywell Forge is a powerful suite of technologies that enables operators to prevent problems and have ongoing visibility into their fleet status in real time. This information helps them focus on their work with the assurance that any issues will immediately be brought their attention.”
As the next evolution of what was formally known as Honeywell’s GoDirect portfolio of solutions, Honeywell Forge will focus on state-of-the-art enhanced offerings, partner integrations and a better user experience. Software enhancements will help customers oversee their entire operation, improve how they manage their fleet and reduce operational costs while improving the passenger experience. It offers the following advantages:
Custom alerts in an integrated dashboard, so directors of
maintenance and flight operations always know the status of their fleet
Real-time insights and actions to address connectivity issues,
changes in flight plans, navigation database availability and
maintenance events — improving operational inefficiencies and ultimately
24/7 personalized service and support from Honeywell’s exceptional
support team is available, anytime and anywhere throughout the year
Ramco Systems announced the successful implementation of its Aviation Suite V5.8 at Aero Contractors Company of Nigeria Limited, a state-controlled Nigerian airline company, for its MRO operations, thereby automating manual work execution processes and enabling organization-wide visibility.
With modules for Planning, Work Execution, Stores, Procurement, MRO Sales and billing process, Ramco’s integrated Aviation M&E MRO Solution automates and optimizes Aero Contractors’ MRO Services. The company says their solution will help the organization optimize employee utilization and improve efficiency and accuracy in billing processes.
“Aero is known for its scheduled operations and reliable services. To keep up with the market demand, we needed a technological partner who would be with us in every step of our digital journey,” Captain Ado Sanusi, CEO, Aero Contractors Company of Nigeria Limited, said. “Ramco’s next-gen, integrated Aviation suite has helped streamline our maintenance processes, thereby giving us a bird’s eye-view of all our operations and enabling digital transformation.”
Titan Airways has chosen Conduce Group’s TechLog8 mobile software solution as an element to their ongoing progress to the greater digitalization of its processes and procedures. eTechLog8 will replace the current paper-based aircraft technical logbooks including its deferred defect logbook and cabin logs. The Conduce eTechLog8 application will also be fully integrated into Titan’s CAMO solution – Commsoft OASES – to enable real time aircraft status data to be available 24/7.
“This is an exciting step forward for Titan as it moves toward an all Airbus fleet and looks for greater digitization of the Airline,” said Dave Bunker, engineering director at Titan Airways. “As an ACMI provider it is critical that the business has real time aircraft status data in order to be able to provide excellent operational service to our client base around the world. Following a review of the market for ETL solutions Conduce Group was selected due to several factors, including a proven track record with many airworthiness authorities, total integration capability with back-office systems, and a unique and intuitive interface that will both minimize training requirements but also allow for a rapid implementation and acceptance of the solution.”
Conduce MD Paul Boyd commented: “We are extremely pleased to welcome Titan Airways to the growing community of airlines that have chosen eTechLog8. The adoption of eTechLog8 allows an ACMI providers’ fleet to be totally “base” independent, operating anywhere in the world ensuring full visibility and integrity of the aircraft technical status. Implementation will commence this month with full rollout planned for mid-2020.”
WinAir has taken measures to ensure they mitigate the health and safety risks to their team during the coronavirus pandemic. It will remain in effect until no earlier than April 6th. The company says the new policy will not affect their client’s use of WinAir or the level of service and support that provided to these companies. WinAir is assuring their users “everything will follow standard operating procedures and will be business-as-usual.”
“We want to assure our clients that during this difficult time, we are with you every step of the way,” says WinAir managing director, Kyle Vergeer. “Your access to, and use of WinAir, will remain the same, and should you require assistance, our team is ready to help via our regular support mechanisms. We are thinking of you and look forward to overcoming this crisis together as a united industry.”
If clients have a support item that requires assistance, they are asked to follow the standard procedure for submitting a ticket via the company’s Incident system. If they need telephone support, their Technical Support team is available at our regular number at 1-519-691-0919.
“We know that the aviation industry has been hit particularly hard by this pandemic. We are hopeful that together, as an interconnected global community, we can overcome this challenge. Thank you for your patience and understanding. Take care, be safe, and stay strong.”
Predictive maintenance has progressed from industry buzzword into a goal for many operators. Today, several airlines and MROs are demonstrating how to use data to increase fleet reliability. But how are they able to fully benefit from the vast wealth of information available, and mine it effectively without incurring unmanageable costs?
Tata comes in many forms and from various sources in an airline – the vast amount available today created the term ‘big data’. Unless robust digital solutions are installed that can aggregate, distribute and analyse information, data is useless. Complex algorithms are required for this analysis, specifically machine-learning algorithms to handle aircraft and engine sensor information.
According to an Oliver Wyman MRO Survey, the global fleet of commercial aircraft could generate 98 million terabytes of data per year by 2026, due to big data. Aircraft data comes from sources including the flight data recorder (FDR), engine health monitoring (EHM) and airframe health monitoring (AHM); each receiving and transmitting thousands of parameters from in-built sensors, often down to component level. The amount of data has implications for transmission costs and for an airline’s connectivity and storage capabilities. That is, for the data to perform proactively, it needs to feed data regularly into maintenance (M&E) and operational systems to create a current picture. Having the infrastructure for this can feel cost-prohibitive for carriers.
Engine and airframe original equipment manufacturers (OEMs) were initially at the forefront of these digital solutions; as aircraft become more sophisticated, the intellectual property (IP) that governed them meant that OEMs were ideally positioned to generate software that could manage data effectively. However, airlines with multiple fleet types still sought solutions that could ingest different data standards and forms. To maximise the ability of big data in the industry, it can’t be kept in-house. “Today, OEMs, airlines and maintenance, repair & overhaul (MRO) operators are showing interest not just in gathering data, but sharing it for a number of different uses—predictive maintenance or health monitoring systems being key applications,” says James Elliott, Principal Business Architect, Aerospace & Defence at IFS.
Predictive maintenance is explored here. By utilising solutions that can interpret aircraft data, maintenance control centres can build a day-to-day picture of individual aircraft (and fleet-wide) performance. Overlaying this with historical information means one can forecast – using advanced analytics – when a part will fault. Moreover, this performance data will contribute to the historical data – meaning that predictive models generated become ever more accurate. By predicting fault behaviour, operators can schedule maintenance ahead of the fault being flagged in operation.
As Aerospace Technology Week approaches, ATR is reviewing the industry stance on predictive maintenance analytics – that is, how are airlines best utilising maintenance and operational data to maximise time-on-wing (TOW). “Ideally, predictive solutions shall reduce the overall cost of operation, reduce interruptions and increase the reliability of the fleet,” agrees Frank Martens, Head of Customer Development Digital Products at Lufthansa Technik (LHT). “There is no generic number available, but some predictive solutions reduce the number of unscheduled removals by 80%, and just one predictive solution can save an airline more than a million Euros per year, but this strongly depends on specific operational patterns.”
Before predictive maintenance can reach maximum potential in the industry there remain challenges pertaining to data ownership, connectivity and regulatory support.
Data Origins and Access
In addition to FDR, AHM and EHM data, predictive maintenance can utilise information from other sources to present a robust picture of aircraft and engine performance.
Honeywell’s digital platform – Honeywell Forge – supports its Connected Maintenance application. Connected Maintenance analyses aircraft data in order to generate trends, maintenance alerts and proximity warnings for failures and faults. Honeywell Forge then allows customers to assimilate and distribute data effectively, which are key for predictive maintenance. “There are a variety of data sources used for predictive maintenance, namely quick access recorder (QAR) Data (or a subset thereof), ACMS Fault Messages, ACMS Performance Reports, and Maintenance Tech Logs,” describes Josh Melin, product line director for Honeywell Forge Connected Maintenance at Honeywell. “The richest data set is direct sensor data from the 717 bus or 429 buses which can be pulled from the QAR, or tapped directly from the bus using wireless enablers. These can be installed on the aircraft.”
While wireless enablers can simplify data flow for airlines, Melin adds that data can be extracted in other ways for the operator, with no need for aircraft modification or retrofits. “We do find, however, that if data is not collected regularly, the value of predictive maintenance solutions is lower, because predictive maintenance relies on regular data feeds to predict failures,” continues Melin. “Furthermore, it is important that the airlines owns the data it generates, and can decide which elements to share and withhold. So Honeywell actually does not need a full set of QAR data to create a predictive solution, in fact, we only need a subset of data labels from the 717 bus which we can provide as a list to the airline.” Melin adds that Honeywell can offer wireless enablers to the airline which can tap the 717 bus and pull only the exact parameters needed to provide the service the airline requests.
If an airline has issues pertaining to cost, data ownership or distribution, Melin explains that Honeywell does not need to collect all aircraft data in order to provide a predictive solution. “Honeywell Forge has airlines providing everything from ACMS Performance Reports, QAR data, to Maintenance Tech Logs in order to formulate their solution. While all the data sets listed are ideal, it’s possible to get started with just a subset of data, such as ACMS data and then as ROI is established the data set can be expanded,” he adds. The solution started as a tool to analyze data coming from thousands of Honeywell APUs. In 30 years, just one model of Honeywell APU has amassed over 100 million hours of service data; an ideal starting point for predictive analytics involving the complex systems which make up the APU.
Saravanan Rajarajan (Saran), Associate Director for Aviation Practice at Ramco Systems explains that maintenance-related data on the Components / Aircraft recorded in their MRO platforms provide another data stream for predictive maintenance. “Non-routines, removals / NFF / minimum equipment list (MEL) occurrences and Operator Maintenance programs all enhance predictive data analytics,” he says. “Analysing both the operational data from the sensors and the MRO data is key for high accuracy.”
Due to the data now available from connected aircraft, Sander de Bree of Exsyn Aviation Solutions adds that operators can now go further than traditional maintenance and health data, to boost predictive and analytical capabilities. “Non-aircraft related data such as weather information and airport data are important data-sources to be used in predictive maintenance algorithms,” he says. “These can be used to detect the impact of operational conditions (such as dry or humid operations) on component health. Additionally maintenance data from MRO’s needs to be used to report back any failure data to an operator’s prediction models.”
Data platforms and advanced analytical capabilities aside, there is one digital tool that a growing number of operators use today: the electronic techlog (ETL). It was the implementation of this device for recording faults that gave rise to the potential for predictive maintenance to flourish. It is also the primary interface between operational and maintenance data; an area where data can become disconnected.
“Data for predictive maintenance is critical, as there are so many areas in which it can be exploited—if it can be collected,” explains Elliott. “Think about a paper technical logbook on the plane, which is only accessible by a single person at a time. Handwritten entries cannot be used in analytics, and cannot be mined for information.
“An electronic, connected logbook can be used by multiple people at the same time,” continues Elliott. “A mechanic can see what faults are on the aircraft, and arrange for proper parts and tools for arrival at the aircraft. And, of course, that digital data can be aggregated and mined. The Internet of Things (IoT) will also help, with sensors being used to measure and collect data.
Digital twins are one industry development linked inherently to predictive maintenance, and applications of the technology are becoming more prevalent. For example GE has helped develop a digital twin for an aircraft’s landing gear. “In this last scenario, sensors placed on typical landing gear failure points, such as hydraulic pressure and brake temperature, provide real-time data to help predict early malfunctions or diagnose the remaining lifecycle of the landing gear,” adds Elliott.
Preventative vs. Predictive Maintenance
There are two core approaches to data-based maintenance, each geared towards different connected capabilities of aircraft or component. For instance, an A320 Classic aircraft will not transmit the same level of operational data as the A320neo; therefore maintenance strategies are different.
Preventative maintenance relies more on ‘trend monitoring’; trying to prevent a fault from being flagged by a line maintenance team by removing a component in the next scheduled maintenance event. The onus is less on the data being transmitted ‘that minute’, or the condition of a specific serial number, but rather taking an intelligent look at historical patterns across a fleet with that part installed, and determining based on age and hours or cycles when that part should be removed for inspection. But is preventative maintenance less dynamic or effective than predictive maintenance? “Preventative maintenance is an age-based maintenance philosophy, not taking into account actual condition of systems & components,” explains Sander de Bree, founder of EXSYN Aviation Solutions. “Predictive maintenance aims to use the actual calculated condition of components (based on operational usage) to serve as triggers for maintenance requirements.”
“Effectiveness of the predictive maintenance (over preventative) lies in its ability to leverage the historical data alongside live operational data,” explains Saran. “This is purely aided by the latest developments on processing the high volume of dynamic data feeds and analysing with sophisticated statistical tools. Because preventive maintenance relies only on historical data it is less effective.” Moreover, the age-based approach often leads to parts being removed ahead of time; meaning ‘wasted’ time remaining on the part if not re-installed.
There are instances where preventative maintenance is more appropriate for operators. “It is a good option in the absence of insights into the actual condition of a component/system,” describes Melin. “But as those insights become available, moving from preventative to predictive can ensure that maintenance actions be prescribed to exactly what maintenance action is required to remedy the current issues and at the right time.”
Data Hurdles in Maintenance
One of the main hurdles preventing operators from investing fully in predictive maintenance initiatives is the data itself – the completeness of it, and the ability to synch data from different sources, departments and formats.
Melin of Honeywell states two primary hurdles that preventing airlines realising predictive maintenance potential. “Some Airlines have a wait-and-see approach to data sharing. This is understandable but unless it’s shared, it is difficult for a software provider to demonstrate potential,” he explains. “Moreover, airlines’ traditional decision-making processes are tough for the software, applications and platforms that can harness and interpret data.”
“Airlines should be in full control of their operational data and be able to share it with their partners like MROs for example,” elaborates Martens. “We doubt that the approach of certain OEMs to restrict operational data access and control will prevail, since all airlines have a strategic interest to control their data.”
“Feedback from component shops on the actual health of components once removed from the aircraft based on prediction models is another hurdle,” adds de Bree. “This information is not readily available to airlines either because they are in a parts pool programme, or have components contracts based on time on wing (power by the hour). For the latter, there is an economical incentive to classify parts removed based on predictions as no-fault-found (NFF). After all, the part did not fail on wing ‘yet’.
“Also, many airlines are looking into predictive maintenance; some with OEM’s, some independently. Currently it seems a race for the best possible algorithm and platform, meaning each initiative is siloed. To make predictive maintenance work we need OEMs & local CAA’s to approve changes to the MPD, airlines to make available operational data, MRO’s to make available maintenance records and solution providers to provide algorithms and calculations,” says de Bree.
Data Platforms & Infrastructure
One way to connect data from different applications and departments is via a data platform; a repository that can exchange information between applications and systems – for instance between an ETL and an operator’s M&E system. “The most data-driven often work with a provider that can cover their entire fleet,” says Melin, “which for many airlines consists of multiple aircraft types from multiple aircraft OEMs.”
“The responsibility for the maintenance of an airline is of the operator and its CAMO and not the OEM’s expertise,” explains Martens. “More airlines realise the potential of digital solutions and the requirement to adapt these solutions to the specific needs of their fleet and operations. Open digital platforms like AVIATAR enable operators to provide digital interfaces to MRO’s and other players in the market, who help them in maintaining their fleet.”
Elliott explains that airlines are starting to work on their own data platforms to get in on the benefits of sharing engineering data. These platforms were initially pioneered by airframe and engine original equipment manufacturer (OEMs) in order to support OEM-developed applications that are often chosen when operators order new aircraft types. Furthermore, OEM platforms benefit from having access to global customer data, thereby bolstering their analytical data provisions. “Airbus launched its cloud-based data platform, Skywise, in 2017 which collects data such as work orders, spares consumption and flight schedules from multiple sources across the industry for MRO operators to perform predictive and preventative maintenance. Early adopters included easyJet, Air Asia, Emirates and Delta Airlines, all of which are using the platform for predictive maintenance,” says Elliott.
Not all data is so readily available. “Sensor data from aircraft is still “locked-up” with the OEM’s as it mostly uses OEM IP in order to be decoded,” highlights de Bree. “You do see independent flight data acquisition avionics becoming available to work around this issue.”
According to Ramco, an M&E MRO system provides the foundational block to support predictive maintenance capabilities. “With the recent advancement on data processing power and ability to store TB of data , the key challenge is agility to connect to the external eco systems and leverage with inhouse data for prediction,” adds Saran. “API based protocol is essential for the organization to achieve software collaboration and encourage data sharing.”
“The number of airlines using the latest big data solutions is limited but growing fast,” adds Martens. “Many airlines are looking at the solutions, but the offerings of real predictive maintenance are limited. Many offerings just provide digital results without direct connection to maintenance actions. Connecting a data platform such as AVIATAR with different M&E System vendors like AMOS or TRAX and other airline IT providers such as Netline help to create the necessary solution.”
Much of the data required for predictive maintenance suggests a high level of data transmission; but to what extent does this need to be performed in-flight, which incurs a great cost? “Data synchronized in flight is mainly linked to EHM/AHM parameters or ACARS data and contain fault messages once a situation has already occurred,” explains de Bree. For instance, while LHT’s AVIATAR ingests data from multiple data sources in-flight and on the ground; the extent of this is defined by the operator. Engines and other components can send data via aircraft interfaces. “In many cases data such as fault messages is sent via ACARS in flight and Wifi/GSM on the ground, but this is up to the airline to define it, based on requirements,” says Martens. “For engineers it can be very helpful to receive these while the aircraft is inflight, since manpower, tools and spare parts can be ordered ahead of landing. This helps operators to save costs by avoiding AOGs (aircraft-on-ground).”
In general, airlines transmit the bulk of their data once on the ground, saving cost. “Honeywell Forge Connected Maintenance has been able to predict component failures days and weeks in advance,” says Melin. “The process of detecting an impending failure and alerting the relevant maintenance engineers can be automated. Typically, the process of then deciding when to complete that maintenance action and submitting the work order is still manual so that the airline can remain in control of that final decision.” Airlines can reduce operational disruptions with the current generation of systems, transmitting data on the ground. Honeywell believes that in future there will be a shift towards transmission of a subset of key data during flights, utlilizing existing satcom connectivity, in order predict a wider set of ATA chapters with high accuracy.
The ETL can provide the means to notify of faults inflight. “An effective ETL allows pilots to communicate with the whole team involved in flying an aircraft on the day of operations—spanning mechanics, maintenance control centres, engineers and more,” continues Elliott. “Once a pilot is flying, if they encounter any problems, they can log the fault in the electronic technical logbook app. On aircraft with in-flight internet connectivity the maintenance organization will receive a push notification in real time outlining the fault and start preparing work orders and parts, so they are ready to address it the moment the aircraft lands. From a more preventative perspective, on aircraft without in-flight connectivity, an electronic technical logbook can push updates to the maintenance department when the aircraft lands.”
IFS’s customer, China Airlines, has been utilising IFS Maintenix to optimize data sharing of real-time management of line and heavy maintenance events, as well as data capture at the point of maintenance across the airline and its subsidiaries. This included expanding third-party MRO services for the airline’s customers. “In addition to reducing operating costs by $3.5 million, IFS Maintenix has helped China Airlines significantly decrease its aircraft layover due to more efficient scheduled and unscheduled line maintenance,” adds Elliott. “This means that its aircraft spend more time in the air and less time in the hangar.”
“While real-time data transmission in-air is a benefit for EHM/AHM fault messages, for predictive maintenance trend calculations an offline datafeed is sufficient,” agrees de Bree. “In terms of wider infrastructure, server capacity is going to be critical to ensure timely processing of data and visualizing outcomes. As an airline you don’t want to wait 4 hours for a calculation to finish prior to giving indications on component condition.”
Unnecessary Part Removal
Removing parts if a fault arises is the traditional business model of the industry, and reactive rather than proactive.
An issue of predictive maintenance lies in the clinical and rigid nature of data if intelligent parameters aren’t built in; we run the risk of incorrect forecasts and erroneous ‘fault’ messages. For instance, if an operator forecasts that a component will fail within 200 hours, based on historical behaviour, it might schedule removal to prevent failure in operation. However, upon removal the part tests no fault found (NFF), costing unnecessary time and money for the operator.
How do we prevent parts being taken off for testing, only to be NFF? And is there risk of oversensitive data, causing unnecessary time off wing for testing? “No algorithm can be 100% reliable,” says de Bree. “The key is feeding back MRO shop data of actual components removed based on prediction models. This is the only real evidence if a failure of that component was imminent. Feeding back such data will make models more reliable.”
“Parts pre-emptively removed need to undergo longer troubleshooting time due to non -availability of fault code or maintenance findings,” says Saran of Ramco. “High sensitivity on the Part removals and longer turnaround time (TAT) will also lead to increased investment in float for airlines. The sensitivity can only be reduced over the time by a continuous closed loop data flow on maintenance findings on the removed part back into the prediction algorithms. It is also imperative that parts are sent to shops with the data leading the predicted fault which reduces the troubleshooting and turnaround time.”
“Ultimately, condition-based removal avoids costly AOGs, improves the fleet’s reliability and ensures high rates of passenger satisfaction,” counters Martens. “If MRO providers don’t know the predictive reason of the removal, it may lead to NFF, but the operator will save on operational cost. An AOG at the wrong location can cost more than €100,000.
“There are several examples where predictors are used successfully. The parameter of these parts are continuously tracked and analyzed, resulting in a trigger/information when the fill level/temperature/pressure parameters start to shift without causing a real aircraft failure. This helps us to change or service these parts preventively to avoid AOGs. Very often the work order can be transferred automatically into the maintenance information system,” adds Martens.
According to IFS, Rolls-Royce has disclosed high expectations for the accuracy of its own predictive analytics strategy. The OEM targets a 100 percent success rate in terms of ensuring they never miss something they are looking for, at the same time as zero false predictions including NFFs.
Predictive Maintenance vs. MSG-3
What effect might predictive maintenance have on scheduled maintenance? For instance, will airlines that maximise its potential still follow an MSG-lead maintenance programme? Or will we see an evolution away from this and scheduled shop visits? “Going forward condition-based maintenance will be used more often, but requires close collaboration between the authorities, operators, MROs and OEMs,” says Martens. “Predictive maintenance should result in less unscheduled, high priority repairs and eventually, we can make many checks obsolete because we calculate figures and probabilities per system which previously were checked manually.”
“Ultimately this might become a new maintenance standard, however no airline today is allowed by CAA regulation to deviate by themselves from the (MSG-based) approved maintenance program and OEM MPD,” explains de Bree. “As long as these are still leading, predictive maintenance initiatives can only impact on-condition components of an aircraft.”
“In the next few years, predictive maintenance can eliminate airline determined soft time maintenance intervals in order to optimize costs and efficiencies, however, it is less likely that predictive maintenance would be a substitute for hard time service requirements,” says Melin at Honeywell. “Ultimately, the change in maintenance practices must be spearheaded by airlines maintenance teams.”
Rolls-Royce is pioneering the concept of an adaptive and evolving maintenance programme, that can effectively go a step further than MSG-3 logic. In 2019 IFS partnered with the OEM to support its data exchange program with airline customers operating the Trent Engine family.
“The IFS Maintenix Aviation Analytics capability enables the automated provision of field data, which ensures that Rolls-Royce receives timely and accurate information on its Trent 1000, Trent XWB and Trent 7000 engines,” explains Elliott. “IFS Maintenix then acts as a gateway to automatically push maintenance program changes from Rolls-Royce back to the airline operators. As a result, life-limited engine part maintenance deadlines can be updated based on actual operating conditions and life consumed by each engine in use.”
Artificial Intelligence (AI)
AI is increasingly referred in conjunction with predictive maintenance. “The use of AI revolves around algorithms being used for predictive calculations to be become more reliable over time by themselves,” explains de Bree. “It allows systems to detect possible failures to monitor purely based on data supplied without any relation between parameters and component failure being known,” explains de Bree.
“AI is a valuable tool for analytics, along with machine learning and neural networks,” says Melin of Honeywell. “AI can be used to determine the state of a system (how it operates in given conditions) and then detect anomalies through time series data which can then be used to predict remaining life and recommend mitigation strategies. AI is different from the historically human-based maintenance systems in that it enables integration of contextual data as well as behavioural parameters of assets.”
In addition while AI can be used in predicting the item removal through predictive maintenance, it is also expected that it can offer additional services to increase the intelligence of the predictive maintenance solution, Saran of Ramco explains. “For instance, AI might also assist an M&E systems in suggesting part replacement options and other parts which might also be needed in replacement, therefore streamlining maintenance downtime. The confluence of Predictive maintenance, AI and Big data drives maximum benefit.”
For large-fleet commercial operators and aviation MRO providers alike, AI is now an essential tool. “Recent examples of airlines such as Delta, and MROs such as Lufthansa Industry Solutions working on adopting AI and machine learning (ML) into their aircraft maintenance strategies highlight the transition organizations are already making towards digital and predictive-focused maintenance strategies,” continues Elliott. “The reduced maintenance technician and engineering labour hours spent analysing data makes intelligent maintenance strategies particularly desirable.”
Want to learn more? Several presentations will take a deeper dive into predictive maintenance at Aerospace Tech Week. See page 43 for the full Show Guide.
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