NLR Reports Repository

Lemmers, A.J.J.

Mevassvik, Ole Martin

Huiskamp, Wim

Pink, Sönke

Towards a persistent capability for NATO MTDS

Vorst, J. van der

Booij, P.J.A.

Hakkaart, J.F.

Lee, T.I.

Qualification of KAI KUH-1M on board ROKN ships using the Dutch approach

Taamallah, S.

Model Order Reduction in the L2-Gap Metric

Amsterdam, E.

Grooteman, F.P.

The influence of stress state on the exponent in the power law equation of fatigue crack growth

Benthem, R.C. van

Grave, W. de

Harness Derating Test Facility for Thermal Testing of Aerospace Harnesses

Roelen, A.L.C.

Verstraeten, J.G.

Speijker, L.J.P.

Bravo Muñoz, S.

Heckmann, J.P.

Save, L.

Longhurst, T.

Risk models and accident scenarios in the total aviation system

Gelder, N. de

Bussink, F.J.L.

Knapen, E.G.

Veld, A.C. in 't

Interval Management Operations in the Terminal Airspace of Amsterdam Airport Schiphol

Vankan, W.J.

Brink, W.M. van den

Maas, R.

Validation and correlation of aircraft composite fuselage structure models

Müller, J.M.

 
 
 
 
 
 
 
 
 
 
 
 
NLR – Netherlands Aerospace Centre 
CUSTOMER:  Agency for Defence Development 
How to cope with more and more  
data 
  
NLR-TP-2016-411 | September 2016 
 
 
 
 
 
 
 
 
 
Netherlands Aerospace Centre 
NLR is a leading international research centre for 
aerospace. Bolstered by its multidisciplinary expertise 
and unrivalled research facilities, NLR provides innovative 
and integral solutions for the complex challenges in the  
aerospace sector. 
 
For more information visit: www.nlr.nl 
NLR's activities span the full spectrum of Research 
Development Test & Evaluation (RDT & E). Given NLR's 
specialist knowledge and facilities, companies turn to NLR 
for validation, verification, qualification, simulation and 
evaluation. NLR thereby bridges the gap between research 
and practical applications, while working for both 
government and industry at home and abroad. 
NLR stands for practical and innovative solutions, technical 
expertise and a long-term design vision. This allows NLR's 
cutting edge technology to find its way into successful 
aerospace programs of OEMs, including Airbus, Embraer 
and Pilatus. NLR contributes to programs, such as ESA's 
IXV re-entry vehicle, European programs, including SESAR 
and Clean Sky 2. 
Founded in 1919, and employing some 650 people, NLR 
achieved a turnover of 73 million euros in 2014, of which 
three-quarters derived from contract research, and the 
remaining from government funds. 
  
UNCLASSIFIED 
EXECUTIVE SUMMARY 
 
  
Problem area 
The last decade saw a large increase in the growth of air traffic. To stay 
competitive, airlines will want to continuously reduce inspection and maintenance 
to a minimum to allow for fast turnaround times and keep their aircraft safely 
flying for as long as possible before maintenance is necessary. Both Boeing and 
Airbus have large backlogs for the 787 and A350, indicating a continued growth of 
air traffic. If these rates are maintained, it can be expected that operators will have 
higher demands on the inspection and monitoring of their aircraft to reduce 
turnaround times. One likely aspect is that systems will be developed that allow for 
more efficient inspection and monitoring. Most likely operators would want 
increasingly to have monitoring carried out during flight to save time on the 
ground. Many structural health monitoring and related systems are being 
developed which probably will be implemented in the future.  
 
The amount of data generated by monitoring and inspection systems is therefore 
likely to grow. One important question is how to evaluate and manage this growing 
amount of data to be able to take a decision in an efficient way: can an aircraft 
continue to fly safely or should be kept on the ground for additional inspection or 
maintenance.  
Description of work 
With the expected increase in the amount of available data, questions on decision 
making based on the data, reliability of the systems which generate the data, data 
processing and the necessity of having so much data is discussed.  
Results and conclusions 
Continuous monitoring by (SHM) systems will generate an increasing amount of 
data. This data has to be efficiently processed with attention to the relevant 
How to cope with more and more data 
 
 
REPORT NUMBER 
NLR-TP-2016-411 
 
AUTHOR(S) 
J.M. Müller 
 
REPORT CLASSIFICATION 
UNCLASSIFIED 
 
DATE 
September 2016 
 
KNOWLEDGE AREA(S) 
Structures and 
Manufacturing Technology 
Health Monitoring and 
Maintenance of Aircraft 
 
DESCRIPTOR(S) 
Data management 
Evaluation 
Monitoring 
Decision making 
            
  
UNCLASSIFIED 
EXECUTIVE SUMMARY 
GENERAL NOTE 
This report is based on a keynote presentation held at the 3rd International 
Conference on Advances in Structural Health Management and Composite 
Structures ASHMCS, Jeonju, Republic of Korea, August 23-25, 2016. 
 
NLR 
Anthony Fokkerweg 2 
1059 CM  Amsterdam 
p ) +31 88 511 3113  f ) +31 88 511 3210 
e ) info@nlr.nl  i ) www.nlr.nl 
operating conditions, but also on the validity of the data, as vital decisions will be 
based on the data. 
 
A “Big Data” approach may reveal possible relationships and dependencies 
previously not accounted for in more traditional data evaluation and in aerospace 
lead to a more conservative design approach, but it could also help in 
understanding the consequences of currently used design approaches. 
 
Currently, there is still much development in the area of SHM and related 
disciplines and many questions are asked. SHM offers a possibility for increased 
efficiency by improving reliability, safety and reducing costs, as well as a potential 
change in design philosophy leading to optimized structural efficiency. The 
potential for improvement is large, and research into this area should yield greater 
efficiency for aircraft structures in the long term. 
Applicability 
This subject discussed in this report can be used in the definition of an approach 
how to tackle the increasing amount of data that will be generated in the future by 
structural health monitoring and related systems. 
  
 
 
 
 
 
 
 
 
NLR - Netherlands Aerospace Centre 
AUTHOR(S): 
J.M. Müller Netherlands Aerospace Centre 
    
    
    
    
    
    
    
    
    
    
    
    
    
    
 
How to cope with more and more 
data 
  
NLR-TP-2016-411 | September 2016 
CUSTOMER: Agency for Defence Development 
2 
September 2016  |  NLR-TP-2016-411 
CUSTOMER Agency for Defence Development 
CONTRACT NUMBER U14098JF 
OWNER NLR 
DIVISION NLR Aerospace Vehicles 
DISTRIBUTION Unlimited 
CLASSIFICATION OF TITLE UNCLASSIFIED 
APPROVED BY : 
AUTHOR REVIEWER MANAGING DEPARTMENT 
J.M. Müller H.G.S.J. Thuis H.G.S.J. Thuis 
DATE DATE DATE 
This report is based on a keynote presentation held at the 3rd International Conference on Advances 
in Structural Health Management and Composite Structures ASHMCS, Jeonju, Republic of Korea,  
August 23-25, 2016. 
The contents of this report may be cited on condition that full credit is given to NLR and the authors. 
 
 
 
3 
NLR-TP-2016-411  |  September 2016 
 
Contents 
ABSTRACT 5 
 
1.  INTRODUCTION 5 
 
2.  DISCUSSION 6 
 
3.  CONCLUSION 7 
 
REFERENCES 7 
 
 
 
 
4 
September 2016  |  NLR-TP-2016-411 
 
 
This page is intentionally left blank. 
 
 
 
 
 
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NLR-TP-2016-411  |  September 2016 
 
 HOW TO COPE WITH MORE AND MORE DATA? 
 
J. Marcelo Müller 1 
 
1 Structures Technology Department, Netherlands Aerospace Centre NLR, 
Voorsterweg 31, 8316 PR, Marknesse, The Netherlands 
e-mail: marcelo.muller@nlr.nl  
 
 
 
ABSTRACT 
The last decade saw a large increase in the growth of air traffic. To stay competitive, airlines will want to 
continuously reduce inspection and maintenance to a minimum to allow for fast turnaround times and keep 
their aircraft safely flying for as long as possible before maintenance is necessary. Both Boeing and Airbus 
have large backlogs for the 787 and A350, indicating a continued growth of air traffic. If these rates are 
maintained, it can be expected that operators will have higher demands on the inspection and monitoring of 
their aircraft to reduce turnaround times. One likely aspect is that systems will be developed that allow for 
more efficient inspection and monitoring. Most likely operators would want increasingly to have monitoring 
carried out during flight to save time on the ground. Many structural health monitoring and related systems 
are being developed which probably will be implemented in the future.  
The amount of data generated by monitoring and inspection systems is therefore likely to grow. One 
important question is how to evaluate and manage this growing amount of data to be able to take a decision 
in an efficient way: can an aircraft continue to fly safely or should be kept on the ground for additional 
inspection or maintenance.  
With the expected increase in the amount of available data, questions on decision making based on the 
data, reliability of the systems which generate the data, data processing and the necessity of having so much 
data will be discussed. 
Keywords: Data management, Evaluation, Monitoring, Decision making 
 
 
1. INTRODUCTION 
Over the next 20 years, Boeing is forecasting a need for over 39,600 aircraft and expects the number of 
aircraft in service to double [1] and air traffic is expected to double in the next 15 years [2]. This growth rate 
obviously depends on various social and economic factors, but it may be assumed that the amount of air 
traffic will increase significantly in the future.  
With an increase in air traffic, operators will not only be expanding their fleet, but will also look into methods 
to increase the efficiency of their fleet.  
In order to maintain structural integrity and hence ensure safety, inspections into the state of aircraft 
structures are still mainly carried out at scheduled intervals or when an unexpected occurrence, such as 
impact with ground vehicles, takes place.  
 
Visual inspection and conventional non-destructive testing (NDT) methods, such as ultrasonic, X-ray, 
thermography and eddy current inspection can be used for the detection of damage in structures. However, 
these NDT methods can be labour-intensive and time-consuming, especially for large-scale structures and 
still very often allowing only “off-line”, local inspection of the structure using dedicated equipment. [3] 
 
 
 
 
6 
September 2016  |  NLR-TP-2016-411 
 
It is clear that an inspection method that continuously registers the state of a structure would offer improved 
reliability and safety if it enables to clearly define the integrity or “health” of a structure in more or less real-
time. Action can be taken based on the status or condition of the structure, potentially saving cost by 
skipping unnecessary inspection and increasing maintenance intervals. Structural health monitoring (SHM), 
combines advanced sensor technology with intelligent algorithms to interrogate the structural ‘health’ 
condition. Different from NDT, the real-time and on-line damage detection via in-situ sensors can be 
achieved in SHM. The potential benefits of SHM include improving reliability, safety and reducing lifecycle 
costs.[3] 
 
Currently, especially for composite structures, a conservative design approach is taken, as there is 
uncertainty over the failure mechanisms and damage tolerance behaviour. In case SHM systems can be 
developed that allow for a more detailed and accurate monitoring of a structure, a different design philosophy 
may employed. E.g. if a more reliable detection of impact damage can be achieved, weight can be saved by 
taking a less conservative approach.[4] 
 
2. DISCUSSION 
A large number of SHM systems is being currently developed which can continuously monitor the state of a 
structure. Due to the increasing availability of inexpensive sensors, systems will generate a large amount of 
data which has to be processed and evaluated in an efficient way. Filtering methods to separate events that 
are related to damage occurrence from normal operating conditions must be developed further. As operating 
conditions can vary, this must be accounted for using knowledge about operating conditions or by statistically 
validated measurements. 
Another aspect is the evaluation of the validity of the data obtained. How can one be certain that the data 
generated is representative and not due to a sensor error? Human insight may still be necessary to 
determine if a result is, e.g., due to a sensor that came loose or an actual event during operation. Ageing of 
sensors can be an issue: how does e.g. an embedded fibre optic sensor behave after 20 years in service? 
Material models need to be extended to deal with the complexity which is introduced when failure criteria are 
applied on structural level instead of coupon level. In this way, sensor results measuring complex 3D stress 
states in the structure can be properly evaluated. 
 
The increasing amount of data becoming available may raise questions. If the data available cannot be 
properly evaluated due to its sheer size, will it perhaps raise more doubt? Using a “Big Data” approach, it 
may be possible to reveal relationships and dependencies previously not accounted for in more traditional 
data evaluation. For an aerospace structure, this could perhaps lead to a more conservative approach in 
design e.g. when a possible failure is predicted based on a Big Data approach, even though in actual 
practice such a failure may have never occurred. However, a Big data approach can also lead to a better 
understanding and quantification of the current design approaches by identifying details previously not 
noticed.  
 
At a certain moment, sufficient data will be available to take a decision. This brings the question on how a 
decision will be made: in an automated way, e.g. based on a pre-established threshold, or will an human 
intervention always be necessary? Ideally, this decision should be taken automatically. However, there may 
be circumstances an automated system does not account for which a human would be flexible enough to 
evaluate the exception and take appropriate action.  
For a decision to be taken automatically, it is necessary to be certain that when an action is taken, that this is 
based on the right grounds. Do the systems used provide the right data at the right accuracy? How is it 
 
 
 
7 
NLR-TP-2016-411  |  September 2016 
 
ensured that the signal from the sensor is reliable? How are malfunctions handled? How does the systems 
react to undefined situations? These are vital questions.  
For land based structures such as bridges or buildings, an inadequate decision will probably have smaller 
direct consequences than that of an airborne system or other transportation systems such as trains or 
automobiles. Automated systems need an extensive amount of testing and validation and there will can 
always be a situation that was not foreseen which can lead to catastrophic situations, e.g. as occurred with a 
car with automated driving options [5]. 
 
As stated before, SHM has the potential to improve reliability, safety and reduce lifecycle costs. However, 
aircraft operators will require from new SHM systems at least the same performance as currently available 
systems and methods at an equivalent level of safety or better.  
They will also probably prefer systems that do not increase workload for technical personnel, although it is 
likely that more trained maintenance staff will be necessary as complexity of systems increases [6]. 
 
Cost benefits when implementing new SHM systems should be considerable, as a large effort is necessary 
to have a new monitoring system qualified and certified for use in aircraft [7]. One estimate is that up to 20% 
of current maintenance and inspection cost can be saved by integrated on-line systems [6]. However, 
several disciplines (NDT, structures, avionics) are involved in the development of SHM systems and a large 
amount of effort is still required to have SHM techniques as well-established technologies. 
 
3. CONCLUSION 
Continuous monitoring by SHM systems will generate an increasing amount of data. This data has to be 
efficiently processed with attention to the relevant operating conditions, but also on the validity of the data, as 
vital decisions will be based on the data. 
 
A “Big Data” approach may reveal possible relationships and dependencies previously not accounted for in 
more traditional data evaluation and in aerospace lead to a more conservative design approach, but it could 
also help in understanding the consequences of currently used design approaches. 
 
Currently, there is still much development in the area of SHM and related disciplines and many questions are 
asked. SHM offers a possibility for increased efficiency by improving reliability, safety and reducing costs, as 
well as a potential change in design philosophy leading to optimized structural efficiency. The potential for 
improvement is large, and research into this area should yield greater efficiency for aircraft structures in the 
long term. 
  
REFERENCES 
[1] Boeing Commercial Airplanes, “Current Market Outlook 2016-2035”, 5 (2016) 
[2]  Leahy, J., “Airbus Global Market Forecast 2016-2035”, 14, (2016) 
[3] Jian Cai, Lei Qiu, Shenfang Yuan, Lihua Shi, PeiPei Liu and Dong Liang, “Structural Health Monitoring 
for Composite Materials, Composites and Their Applications”, (2012) 
[4] Roesner, H., Speckmann, H., ”Structural Health Monitoring: A contribution to the intelligent Aircraft 
Structure”, 9th European Conference on NDT - September 2006 - Berlin  
[5] The Tesla Team, News item at https://www.tesla.com/blog/tragic-loss. 
[6] Bartelds, G., Heida, J. H., McFeat, J. and Boller, C. “Introduction, in Health Monitoring of Aerospace 
Structures: Smart Sensor Technologies and Signal Processing” (eds W.J. Staszewski, C. Boller and 
G.R. Tomlinson), John Wiley & Sons, Ltd, Chichester, UK. (2003) 
 
 
 
8 
September 2016  |  NLR-TP-2016-411 
 
[7] Piotrowski D., Roach D., Melton A., Bohler J., Rice T., Neidigk S., Linn J., “Implementation of Structural 
Health Monitoring (SHM) into an Airline Maintenance Program”., Proceedings of the Tenth International 
Workshop on Structural Health Monitoring, September 1–3, 2015, Stanford, U.S.A.
 
 
 
 
 
  
 
 
 
 
NLR 
Anthony Fokkerweg 2 
1059 CM  Amsterdam, The Netherlands 
p ) +31 88 511 3113  f ) +31 88 511 3210 
e ) info@nlr.nl  i ) www.nlr.nl 
 
 


How to cope with more and more data

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National Aerospace Laboratory

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