B-1 “Digital Twin” project will be the first time a complete scan has been performed on a military aircraft. The process began when a B-1 was for a complete teardown, hich means every nut and bolt, all the skin panels are all coming off. “They’re going to do a complete inspection of the aircraft for us, looking for fatigue cracks.”
As a heavy aircraft flies, gravity and air turbulence make it feel its weight. Fatigue cracks form on stress points and grow under the load of the jostled plane.
“We want to find those fatigue cracks and how they’re growing and why they’re growing and prevent them and how we can repair them if we need to.
Making a digital twin is an opportunity to get inside of a jet and see all of the internal parts not exposed during regular maintenance, allowing us access to areas we’ve never looked at before.
Every component of the B-1 will be painstakingly scanned using laser equipment which triangulates its position on the part to create a digital copy that can then be turned into a 3D model.
“It creates a digital representation on a computer and then a technician runs a verification against it to make sure that all the points have been scanned and there’s enough data. And then they can take that and turn it into the solid model for maintenance and manufacturing.”
Starting at the top of the B-1, the technicians will scan and peel away layers of the plane, then scan again. Rinse and repeat.
“It’s a long process. So, they’re scanning as they go along because we want to make sure we understand how everything is stacked up and put together. Once finished, the Air Force will have a complete digital clone of the B-1 it can use to make predictions about damage the jet could potentially sustain in different scenarios.
“It can fly right along with the fleet, so we can actually take real-world data and put it into our digital model and see what’s going on in our fleet as a whole. “And so what that does for us is allows us to fly ahead of the fleet in the digital environment. I can simulate more hours on that aircraft without ever breaking one and I can see what’s going to happen in the future.”
The modeling will allow the Air Force to begin planning repairs before a plane is damaged.
“So we get out of our current reactive state, which means an aircraft breaks then I have to start building and repair it at inspection into a standpoint of ‘This is the next area we’re pretty sure something’s going to break. Why don’t we plan now for what type of inspection and start planning a type of repair. So this will help us with downtime and we’ll be able to turn jets quicker because we already know what to expect, where to go look, what we should be finding, why it occurred.”
Having a digital aircraft also means a manufacturer can be given the 3D renderings to have components built more quickly.
“In the past, all we had was the 2D digital drawings. A lot of aircraft technicians don’t know how to read these dated 2D manufacturer drawings anymore. This is an opportunity for us, for once, to have high-fidelity, 3D CAD drawings that can be put right into maintenance and manufacturing.”
"What’s new is that we’re reclaiming an aircraft out of the Boneyard and then tearing it all the way down, having everything scanned and built back up so you get an entire aircraft. Further, we’re taking that to do the loads analysis. We’re going to simulate it actually flying, so that we can understand the future stresses on the aircraft.
Another possible use for a digitally cloned plane is virtual reality. We have only just begun to imagine a scenario where the technology can be used in regular aircraft maintenance and training.
“You can highlight it in the digital aircraft and maintainers could pull it up on the computer and pull it up on a set of VR goggles and say ‘OK, I know exactly where I need to be,’ as maintainers walks out to the aircraft he’s going to be working on,”
Then we can do the same thing for virtually prototyping repairs. You can do a much better test fit since the aircraft is fully represented in the digital environment. You can actually build a repair and test it, take it to get 3D printed so that our first repair is going to have a much higher level of success.
There must be a high state of materiel readiness across the force. In addition to appropriately funding the sustainment of equipment, and the establishment of appropriate stocks in appropriate areas to enable operational contingencies, the means of sustaining equipment must be as appropriate for support operations as they are for efficiency in garrison.
Failures in materiel readiness are often replicated in major sustainability issues on operations, and necessitate consequential actions such as switching parts between aircraft to achieve desired operational readiness outcomes."
Job site completed the initial phase of a novel virtual roll-out of its squadron-level maintenance improvements to units at Naval Air Stations. Using a combination of technology, innovation and dedication to mission, the team involved prevented a delay in this important implementation.
The roll-out involves the Organizational-level (O-level) Reform efforts of the Naval Sustainment System-Aviation (NSS-A), an initiative that began approximately two years ago as a way to improve readiness across Naval Aviation. At the direction of the then-Secretary of Defense, the F/A-18 and EA-18G served as the initial communities to introduce NSS-A.
The F/A-18E-F NSS-A efforts produced around 90 additional mission capable Super Hornets—the equivalent of returning $5.5 billion in assets back to the flight line. The Super Hornet community reduced the turnaround time of special inspections by more than 40%, meaning more aircraft were available for the flight schedule. After the F/A-18EF successes—which include the highest mission-capable rates in decades—the NAE began to turn attention to rolling out O-level Reform to other squadrons and communities.
“ We were able to get this implementation done on schedule using virtual methods, but we were also able to introduce and ‘normalize’ new software tools for our folks. “We’re vigilant about maintaining the balance between operational readiness and personnel safety. This virtual option allowed us to get into place improvements we were already planning without pushing the timeline. We’re looking forward to seeing benefits in turnaround time and overall maintenance operations.”
“The virtual roll-out benefitted us because it let us implement the reforms on our time line, but it also saved us time from having to find a place on our schedule to do this later in the year, possibly impacting our planned operations. The community is used to being flexible, independent and prepared for any scenario, so we’re a natural fit to try this virtual option.”
O-level Reform changes involve a variety of adjustments to procedures including delegating ownership over aircraft being repaired to individual crew leads with white boards next to aircraft that show anyone going by the current repair status of that aircraft; keeping all tools, parts and other materials necessary to fix a specific aircraft kept in a dedicated space with the aircraft instead of in other storage areas; and daily meetings to keep everyone informed of the status of every aircraft in for repairs.
Another major emphasis of the reform is a reinvigoration of Continuous Process Improvement (CPI) across Naval Aviation maintenance.
“This is much more than coming in with some proven changes and adjusting squadron maintenance,” “In fact, we’re looking re-moniker this effort as we go forward and call it O-Level Maintenance Management. It’s not just about reform, it’s about a new way of doing business and constantly evaluating our approach so we can constantly find ways to do it better.”
The team will have the chance to look for new CPI opportunities. With the initial phase complete, the TYCOM team hands ownership of the implementation process to the type wings and then re-engage directly to evaluate progress and make adjustments. The hope is this time, if it’s not possible to visit the squadrons in person, virtual methods they already proved work to complete their efforts will continue.
In future Multi-Domain conflict, maintenance and logistics requirements follow operators, so we’ve got to be able to have the sense, orient and respond tools required to posture the aircraft forces in supporting the missions.
Aircraft programs need to replace old network systems with modern capabilities, including data fusion from multiple sensors — whether those be onboard an aircraft or from a machine doing specific maintenance.
Air Force plans to expand its “predictive maintenance” using artificial intelligence (AI) and machine learning. “Predictive maintenance is a real game changer for us “There’s a lot of power in moving unscheduled maintenance into scheduled maintenance, and we’re firmly convinced that it will improve our readiness and improve our combat capabilities by doing so.”
“We have long been a fly-to-fail force, simply waiting for aircraft to quit working and then trying to fix them by moving parts to wherever the planes were grounded. But today’s unpredictable and relatively slow approach to getting fighters and bombers back in the air simply won’t be possible in future conflicts.
Maintainers think they can now start to put some problems behind them using Condition-based maintenance (CBM) to enable crews to, Instead of replacing parts after they’ve failed or relying on fixed schedules, they can predict failures with a high degree of accuracy and get ahead of problems when maintenance makes the most operational sense.
“That data really is the key to our awareness of what’s happening in the mission environment, and what’s happening in the broader enterprise, to include at home and the depots and the broader supply system.” Modern tools are enabling units to predict when an aircraft part will fail before it actually happens.
After the aircraft lands, information from the data is uploaded to one of two organically developed post-flight analysis tools, Data Analysis and Redistribution Tool, or DART, and the Embedded Diagnostic System Data Analysis Tool, EDAT. Aircraft engineers and maintainers can access the data on the web and use the information to troubleshoot and maintain the aircraft fleet.
In explaining the usefulness of DART, “It’s kind of like when the car ‘check engine’ light comes on. With DART, you can see what’s going on with the airplane.”
DART can show up to 18 months of data, while EDAT looks at just one flight at a time. Another predictive tool, Aging Fleet Integrity and Reliability Management application, or AFIRM, is being used as weapon system integrity program system of record for the Aircraft System Program Office.
“If a car breaks down on the road, a mechanic has to come, you have to haul the vehicle in and parts have to be ordered. It takes hours and hours to get the car back on the road. When you can predict when a part is going to break, you can replace that part and you won’t break down in the desert or somewhere there is a lack of support.”
The predictive measures tools allow for timely, planned maintenance processes. Time and money are saved while safety is greatly enhanced. CBM+ toolkit will soon be added to DART further decrease aircraft downtime and maintenance times.
By leveraging past years flight data stored in DART, CBM+ algorithms can establish a part's health and accurately predict when that part will fail. Before failure, maintenance crews will be alerted that the part is near the end of its life and can prepare for its replacement. Thus, all parts and personnel needed for maintenance can be acquired and in place at a scheduled maintenance date, not after the part has broken and left the plane stranded.
With CBM+ data from DART, the plane can be flown to the chosen maintenance station under its own power before failure, rather than having to scramble needed parts and maintenance personnel to wherever the plane has broken down. The plane can then be repaired and put into service in a vastly shorter timeframe. This saves valuable time, money and resources in an effort to keep the aircraft fleet healthy and supporting its mission.
EDAT is used by the engineers to diagnose aircraft issues by graphically displaying EDS data. The data provided by EDAT reduces the diagnostic process from days to minutes.
The most current iteration of EDAT includes a signal validation tool that shows the user which signals are exhibiting non-standard behavior for the engine bleed air system. As the CBM+ tools are expanded, EDAT will include modules for thrust reverser, air cycle refrigeration subsystem and cabin pressure control.
The tool will enable maintainers to identify the optimal time to replace parts before failure and ensure that both field maintainers and supply are aware of upcoming demands. “You can look into the future a little bit and see how the aircraft does,” he said. “It saves you a lot of headaches.”
“CBM+ variant is still in its infancy, but officials have high hopes for how it might cut maintenance costs and boost aircraft readiness, but many of the military airframes the service operates are decades old, and aren’t outfitted with the same number and quality of sensors that spit out detailed data about which components are coming due for service.
In addition to CBM, another predictive maintenance initiative, “enhanced reliability centered maintenance (ERCM) an initiative that is really laying that artificial intelligence and machine learning on top of the information systems that we have, the maintenance information system data, that we have today, and understanding failure rates and understanding mission characteristics of the aircraft and how they fail.”
Because of that, at Air Mobility Command is planning to include much of the CBM program to rely on eRCM. Instead of depending on data feeds from an individual aircraft’s sensors, algorithms will crunch through detailed records the Air Force already has about how a particular part has historically performed across the fleet — and on a particular airplane — and determine the ideal time to replace or repair it.
“We’re able to forecast up to two years in advance when parts need to come off of aircraft,” “What eRCM allows us to do is, component by component, position by position, tail number by tail number, get a specific look at what’s going on to give us a much more accurate picture and then be able to again adjust our removal forecast based on what’s actually happening out there instead of just using a generic average.”
Air Force can take advantage of the performance data it does have from its newer airframes that are outfitted with more modern sensors. Sensor data will feed into a separate line of effort — the other 20% of the CBM+ approach — called Predictive Algorithm Development (PAD).
“We’re focusing on eRCM just because it’s going to get us our biggest return on investment, but eventually we will go back to the PAD side, because that completes the holistic view of CBM. “But it’s going to be very challenging for some of the aircraft that do not have those onboard diagnostics. But what we can do is look at things like the flight data recorder that’s typically used for safety investigations. We’re looking for some things that we may be able to tie back to the maintenance side.”
And even on some of its newer platforms that are outfitted with sensors, the data they collect is often encoded or encrypted by the original equipment manufacturer, because the Air Force wasn’t thinking about CBM at the time it signed the acquisition agreements for those systems.
But now that the service is starting to get a better handle on what sorts of data are useful for maintenance purposes, it’s beginning to use those lessons to inform its policies on the front-end of the acquisition process.
“Right now we’re in some significant conversations with new aircraft programs about things that they need to be writing into their contracts as the aircraft is developed to capture all of these lessons learned. “And as we go through some of our other platforms, as contracts come up for renewal with our equipment supplier partners, we’re having those same conversations just to make sure that we’ve got on-ramps and off-ramps going through as CBM+ matures over the next number of years.”
The Air Force’s gradual move toward condition-based maintenance has major implications for its supply chain. On one hand, DoD industry systems and processes that deliver parts to where they’re needed will have to adjust to a cadence that ensures they are ready to install well before they’ve failed.
But if all goes according to plan, it also means the Air Force and Defense Logistics Agency will be able to reduce the total number of spare parts they keep on hand just in case of unexpected problems.
Costs can be removed from the supply inventory by better predicting failures and reducing that ‘just in case’ inventory. So we expect to see results in the Air Force supply system as we are able to pinpoint where we need parts, when we need them.”
1. Design fault detection, fault isolation, and fault prediction capabilities
2. Ensure capabilities are sufficient to meet condition monitoring and predictive requirements.
3. Utilize built-in-test and off-equipment for prediction achieve desired functionality
4. Maximize the use of predictive maintenance strategies and implementation of CBM+
5. Enable improvements in failure prediction capabilities.
6. Develop and apply digital system tools for more accurate condition-based monitoring
7. Integrate maintenance and other functional logistics information systems across the enterprise
8. Design prognostic analytics with flexibility to accept many different sources of data for accurate predictions
9. Develop integrated CBM+ architecture early in the implementation process
10. Use Architectural Framework to ensure accurate and timely condition monitoring results
Top 10 Practices Consider Tech Enablers when Developing AI Condition Based Maintenance Functionality Strategy
1. Perform business case analysis to determine where applications of CBM+ meet economic goals
2. Reliability centered maintenance approach provides balance of reactive, preventive, and predictive processes
3. Use reliability analysis to determine optimum maintenance task functionality
4. Utilize condition monitoring analysis information when evaluating potential investments in reliability improvements
5. Assess consequences of changes in equipment maintenance approaches
6. Invest in sensor, data collection, and analytic capabilities
7. Minimize condition monitoring errors and determine failure modes
8. Develop metrics driven by condition-based maintenance information to enhance equipment performance
9. Use Life Cycle guidelines when applying CBM+ throughout the equipment acquisition process.
10. Consider using modeling and simulation in CBM+ to determine best design approaches.
Top 10 Best Practices to consider Tech Enablers when developing CBM+ Design Strategy
1. Incorporate open system architecture when designing business processes of digital tools
2. Achieve maximum interoperability, portability, and scalability
3. Apply industry standards to achieve open systems architecture
4. Apply Enterprise Application Integration when designing data exchange and storage strategies
5. Choose applications to facilitate the sharing of data
6. Promote integration of maintenance and logistics information systems
7. Deign measurable, consistent, and accurate predictive parameters related to specific failure modes
8. Use automatic entry and retrieval to achieve more accurate data
9. Integrate data from different sources to achieve to achieve condition monitoring capability
10. Utilize shared databases to maximize the benefit of condition-based maintenance data.
Top 10 Real-time System Condition Management Collect Data for Enterprise Repositories
1. Configuration management data and applications
2. Operating history data collection and storage
3. Digital logbooks, an automated event recording system
4. Event driven network Message managers
5. Data base management system for asset condition management data
6. Diagnostic applications for analyzing causes of failures
7. Predictive maintenance forecasting
8. Single or multiple correlated sensor data trend analysis
9. Model of correlated predictive sensor and measurement data
10 .Interactive electronic technical manuals and interactive training
Top 10 Challenges for Implementation of CBM+ Technologies and Operational Applicability of Machine Assessments
1. Development and integration of self-powered or power-harvesting wireless micro sensors capable of operating in high thermal or high mechanical load environments.
2. Improved models and methods to predict condition and expected life based on physical, mechanical measurements.
3. Reliable methods to measure and predict corrosion degradation in unstable environments
4. Predictive tools for advanced materials, materials systems, and structures and design concepts for in-service monitoring
5. Design tools to assist in selecting the most appropriate monitoring approach for a specific mechanical or electrical/electronic system
6. Development of miniature sensors enable condition monitoring of debris in lubricating oils
7. Sensors enable the detection severity of hidden corrosion and general corrosion of environments
8. Sensors enable detection of acoustic and vibrational measures
9. Life-prediction methodologies and real-time computations
10. Signal processing and multi-sensor data fusion