Six Innovation Areas Leading to the Model-Based MRO Enterprise (Manufacturing Operations Management Talk)

Maintenance, Repair and Overhaul (MRO) operations are facing a rapidly changing highly competitive environment. In the past, MRO shops could pass the cost of inefficiencies as a “cost of doing business” to the customer, but in the face of higher competitive pressures, MRO shops must look to modernize practices and leverage digital technologies to take operations to new levels of productivity.

In this article, we discuss the convergence of technologies enabled by a new Model-Based Enterprise (MBE) philosophy that leverages the engineering 3D models throughout MRO processes including spares management, inspection and work execution. In this new digital environment, MRO shops can leverage technologies like IIoT connected product and equipment, optimized 3D illustrated work packages, inspection drones, augmented reality, and improved intelligence and analytics to create the platform for those new productivity levels.

In a recent article [2], Airbus discussed a vision for the MRO hangar of the future. In this future vision, 3D models are leveraged in many ways as foundational data for a myriad of technologies like autonomous robotic inspectors and mechanics equipped with smart glasses for hands-free computer interaction.

In this article, we further discuss six innovation areas that are driving progress towards a future MBE at leading edge MRO hangars:

3D Printing of Spare Parts
Automation with Robotics and UAVs
Asset Sensor and Diagnostic Connectivity via the IoT
Digital Thread and Digital Twin
Advanced Analytical Capabilities
Advanced Guidance with Augmented Reality and Natural Language

3D Printing of Spare Parts

The most obvious use of a 3D model is to produce the input file for a 3D printer. 3D printing is becoming a “must have” for manufacturers rather than a luxury R&D project. Especially in the aerospace and defense (A&D) industry where companies like Boeing and Airbus have been using the 3D printing process to manufacture components for more than two years, with Airbus recently printing 1,000 parts to meet delivery deadlines for the A350.

While only 7% of companies use additive manufacturing to produce their end products today, 31% use it for prototyping, and it’s predicted that 42% will rely on 3D printing for mass manufacturing in the next few years. This means that more and more CAD and CAM software systems offer capabilities to handle 3D printing.

3D printing will not only revolutionize prototyping and production, but will also transform spare parts inventory practices for service management. Manufacturers of spare parts will store the digital data to 3D print parts and components in CAD software rather than storing the physical parts on shelves. In other words, inventory will be produced only when necessary. This will be of special interest to produce hard-to-find spare parts or components that aren’t manufactured anymore.

The growth in 3D printing methods will probably come from the larger OEM and MRO companies because they can cover the upfront investment to design, build, and test the multiple prototypes it takes to finally develop a part to the desired quality level for a customer.

The companies capable of developing almost instant spare parts and completing maintenance and repair tasks within hours will have a market advantage. Imagine an airline looking for an MRO able to repair an airplane that has broken down unexpectedly. Instead of waiting days or even weeks for parts to arrive, an MRO could manufacture the parts needed outright and fix the airplane within several hours.

The ability to manufacture parts on demand will affect inventory and logistics. MROs will no longer have to keep on-hand dozens of “just-in-case” replacement components until the right client comes along. Whenever they need a part, they can just make it. This will probably cause a collapse of the entire supply chain, making some part suppliers and even manufacturers vulnerable to extinction.

Automation with Robotics and UAVs

The rapidly expanding capabilities of autonomous equipment such as UAVs and robots are being leveraged in MRO shops and some of the first applications are in automated inspection.

Examples include the use of 3D scanners mounted on robots and UAV (Unmanned Aerial Vehicle) to inspect fuselage and wings for hail, lightning, or bird strike damage. UAVs can complete detailed aircraft checks, by comparing the images captured against the respective 3D models, and quickly report any damage requiring repairs. Checks that now take 4-6 hours can be performed in less than an hour.

For now, operators control the UAVs, either guiding the inspection or setting a predetermined pattern. The UAVs fly around aircraft, providing images of skin with the same detail as visual inspections. Engineers assess damage as they are presented images. The software continues to be enhanced to rapidly focus on possible problems areas and even suggest actions based on prior recommendations. Once the UAV learns the aircraft it is to inspect leveraging its 3D model, it can recognize a known point on the airframe, and fly a predetermined inspection pattern.

LHT’s Mobile Robot for Fuselage Inspection (Morfi) is automating inspection of metal fuselages for thermographic crack detection. Morfi moves over preprogrammed points on fuselages rapidly. Two coils heat the points with electric pulses and changes are recorded on infrared cameras, revealing cracks as shallow as 1 millimeter. Vacuum pads on the robot’s feet allow it to reach vertical and overhanging sections of the fuselage. Up to four areas can be inspected and results saved in less than 30 seconds.

Asset Sensor and Diagnostic Connectivity via the IoT

It is easier to embed micro computers in many devices and assets. The device will also be expected to easily connect and exchange data with other systems and centralized data repositories that will focus on analysis of exception and aggregate data in different dimensions including dimensions relating to the asset’s 3D model. By relating the intelligence gathered to the different 3D model areas, engineers can use performance data, not only to diagnose issues, but also to enhance future designs.

Today a simple engine designed for industrial use has hundreds of sensors recording thousands of measurements, along with enough memory to store months or even years of data. Until recently, the primary use of these embedded measurements and associated low level diagnostics was to help identify component failure to facilitate the identification of areas requiring repair.

Next in the evolution to a more digitally connected future is the ability to easily connect each device or asset to centralized or remote data processing capabilities using common internet networking protocols. This type of device connectivity is referred to as the Internet of Things (IoT). Will we be able to improve product service if we can remotely monitor the product and apply advanced analytics to predict potential issues before they happen? Can we optimize maintenance based on sensor and usage statistics versus prescribed intervals?

The increased capacity and lower cost of data storage makes it possible to store huge amounts of data. But these streams of data from sensors and diagnostic feeds need to be managed. Platforms for data management includes strategies for how much to store, locally versus centrally, raw versus aggregated, and for how long for different process orchestration, record keeping and analytical purposes.

Digital Thread and Digital Twin

In addition to the asset usage data collected via IoT or other techniques, MROs need to collect as-maintained data and cross reference the respective as-designed specifications. The flow of information between engineering specifications, requirements and actual service realization records is covered under the label of the “digital thread”.

The concepts of the digital thread and digital twin have been spearheaded by the military aircraft industry and their desire to improve the performance of future programs by applying lessons learned through these digital technologies in current and upcoming programs.

The digital twin refers to a digital model of a particular asset that includes design specifications and engineering models describing its geometry, materials, components and behavior, but more importantly it also includes the as-built and operational data unique to the specific physical asset which it represents. For example, for an aircraft, the digital twin would be identified to the physical product unit identifier which is referred to as the tail number. The data in the digital twin of an aircraft includes things like tail number specific geometry extracted from aircraft 3D models, aerodynamic models, engineering changes cut in during the production cycle, material properties, inspection, operation and maintenance data, aerodynamic models, and any deviations from the original design specifications approved due to issues and work arounds on the specific product unit.

The digital thread refers to the communication framework that allows a connected data flow and integrated view of the asset’s data throughout its lifecycle across traditionally siloed functional perspectives. The digital thread concept raises the bar for delivering “the right information to the right place at the right time”.

A typical as-is condition for many MROs without a digital thread:

Design 3D models are usually not shared between OEMs and asset owners
Some drawings might be included with OEM maintenance manuals and delivered via PDF files
Maintenance requirements are documented within the maintenance manuals to be parsed manually by asset owner
Asset owner creates their own maintenance requirements based on OEM maintenance manuals and refer to specific maintenance manual sections
MRO creates maintenance task cards based on asset owner maintenance requirements and maintenance manuals
As-maintained records are archived by MRO and delivered to asset owner as PDF package. Both asset owner and MRO must maintain these records archived for auditing purposes

Progress towards a full digital thread:

Asset’s 3D models are shared by OEM with asset owner by 3D technical packages and standards like 3D PDF and STEP.
Maintenance manuals are shared using standards like S1000D for easy parsing into downstream systems.
Maintenance requirements are managed online and cross referenced to sections of maintenance manuals
Maintenance task instructions are developed leveraging links to maintenance manuals and 3D models for illustrations.
Asset’s as-maintained data is delivered in parsable data format to asset owner along with finished asset

Advanced Analytical Capabilities

Humans will manage the exceptions and will soon only need to be involved when something goes awry. Integration between operational systems, business process management systems and analytics will become the norm. There are four general levels of analytic capabilities:

Descriptive Analytics: “What has happened?”
Diagnostic Analytics: “Why did it happen?”
Predictive Analytics: “What is likely to happen next?”
Prescriptive Analytics: “How it can be encouraged or prevented?”

Descriptive Analytics

Descriptive analytics collect and report on the changing state and diagnostic properties of devices over time. This data can be used to determine stability, trends, and patterns in those properties.

Descriptive analytics also allow comparative analysis between the performance of a specific asset and the performance of similar assets in order to identify unique deviations from the asset population.

Diagnostic Analytics

Diagnostic analytics are just a step beyond purely descriptive numbers. One big problem for the IoT is the massive number of alerts that it can generate. Alerts are generally intended to get humans to pay attention, but “alert fatigue” can set in fast if there are too many of them – as there are already coming out of today’s aircraft.

Diagnostic analytics can determine whether alerts really need attention and what is causing them. It involves correlating multiple properties and looking for escalation based on the grouping of multiple conditions.

Predictive Analytics

Predictive analytics is where we start seeing bigger payoffs from diagnostic data connectivity and processing. The idea is to analyze historical data patterns that are leading to device failures, find the leading indicators, and start using those patterns to predict failure before it happens again on the same device or other similar devices.

The more data we have from similar devices or assets, the more accurate the prediction algorithms are going to be. OEMs and asset owners are creating ways to accumulate the needed data from their assets as they go into service.

Prescriptive Analytics

The initial steps for prescriptive analytics, are systems that use artificial intelligence and expert system techniques to continuously learn from human maintenance prescriptions under different circumstances, to become an automated “second opinion” for the human that proposes several options based on analyzed past decisions.

The next prescriptive level would be for the machine to start making recommendations for actions directly, with high confidence levels, from analyzing the input data. For example, an airline pilot could be told, “Shut down engine number three now, before it overheats.”

Predictive and prescriptive analysis have the potential to radically improve how maintenance activities are planned in the future. Today, most maintenance activities are planned based on a combination of elapsed time and asset usage frequency. Today, we are probably doing a lot of maintenance that is not really needed or perhaps not enough maintenance in some cases. For example, for an automobile, the dealer prescribes the frequency of brake service based on miles driven. However, the mechanic checks the brake pad usage to verify if service is really needed because the real need depends on multiple variables like driving style and the number of hills in the area. We might have taken the car in for brake service too early and would probably go ahead and get it done to avoid an additional service visit. Enhanced sensors and analytics could feed future applications that will prescribe the ideal time to take a car in for service and group multiple needed services into fewer visits. If we can see the value of this type of app for our car, imagine the value of this type of analysis to optimize and minimize service downtime for airplanes.

Advanced Guidance with Augmented Reality and Natural Language

Augmented reality (AR) or mixed reality is the ability to layer digital 3D images and virtual objects on top of the real-world images when seen through technologies such as smart glasses or hand-held tablet computers.

Multiple companies are experimenting with the use of AR for support on inspections and repairs. AR may be able to recognize the asset a user looks at, overlay the points where service is needed, and integrate with a tablet that helps the user know what work needs to be done and instructions needed. Such capabilities could improve service efficiency; lowering the cost of labor; and reducing errors.

For example, some of the automated inspection methodologies mentioned above could be further enhanced with augmented reality technology by projecting the results, color coded for severity, on top of the physical surface so inspectors and repair personnel can quickly focus to the specific problem areas.

The two major technological trends for AR display today are (1) display on mobile tablets that requires the user to hold the device, (2) display on a head-mounted display like smart glasses. Smart glasses are getting closer to the form factor of safety glasses which is needed to really make this technology appealing at the shop floor.

The traditional way to interact with smart glasses has been with simple voice commands like Next, Yes, No, Check. But in parallel to the AR technology, there is much progress on natural language capabilities for easier voice driven interaction with these devices. Chat bot technology, like seen in Siri or Alexa for home use, is being married to AR capabilities to create a new generation of hands-free easier user interaction with the advanced AR display where the user can chat with the device in a more natural manner. For example, “Glasses, show me the illustration for the next step.” The system would know what to display based on the recognized asset, the completed prior work, the work assigned to the user, and the authored work instructions.

Everyday work cards and checklists can easily be performed through these wearable devices. Steps may be signed-off as they are performed and all information systems are updated with real-time information via Wi-Fi directly from the AR device.

The above technologies and innovations are examples of how leading MROs are paving the way to the fully model-based future of MRO operations. MRO shops need to continue this innovation path leveraging 3D models as they connect and optimize processes across engineering product specifications, maintenance requirements, and maintenance execution techniques.

References

[1] Model-Based Enterprise: An Innovative Technology Enabled Contract Management Approach, Mitzi Whittenburg, Journal of Contract Management, 2012

[2] MRO Hangar of the Future, Airbusgroup.com, 2016