Technologies and Techniques for New Maintenance Concepts (TATEM)

1. Who we are:

The Technische Universität Darmstadt (TUD) is a public autonomous university in Germany.

Currently, the university has more than 17,000 students in the fields of engineering science,

natural science, humanities and social science.

Responsible for the work done in TATEM was the Institute of Flight Systems and Automatic

Control, which is part of the department of mechanical engineering of TUD. With 1.06% of

the total budget, the TUD had a small but noticeable contribution to the TATEM project.

2. TATEM Coverage

The TUD hat two major fields of work in TATEM:

In work package Aircraft Maintainability (Strand: Utilities and Actuation) the focus was on

research on diagnostic and prognostic algorithms for health assessment of aircraft actuators.

Additionally, through the innovation fund, a general study on fault observability was carried

out.

In the Ground Crew Support work package (Strand: Embedded Training) the focus was on

fully immersive virtual reality (VR) maintenance training applications. This activity was also

part of the innovation fund.

3. Technical Achivement

Actuation Systems – Diagnosis/Prognosis

In this task, diagnostic and prognostic algorithms for health assessment of the Uplock actuator

were studied, implemented and evaluated. Further, for the GEMA actuator, a model-based

concept for detection of faults and detection of external disturbances was proposed and tested

in simulation experiments.

The Uplock health assessment scheme is based on a process history data approach. Test rig

data of the motor input current has been recorded for a fault free and a degraded actuator.

Using signal transformations and pattern recognition schemes, a reliable state detection of the

Uplock is realised. Two fault states can be distinguished from the fault free state. Further, the

severity of the fault can be measured. Through this, a health index is obtained which can be

used as a basis for lifetime prediction.

For the second actuator, an electro-mechanical primary flight control actuator (GEMA), a

model-based approach was chosen. Based on test-rig data, a model of the actuator dynamics

was build and validated. Further, a recursive Bayesian estimation library was designed,

implemented and used for the estimation of external disturbances acting on the GEMA. A

hybrid Bayesian estimation method for fault detection was studied and tested through

simulation experiments.

Actuation Systems – Detectability of Faults…

A study on the observability of actuator faults in primary flight control actuators regarding

different flight phases was completed.

Based on an in-flight measured dataset containing the deflection of aileron, elevator and

rudder, usage profiles for these actuators were derived.

A generic concept to describe fault diagnosability was developed. The concept allows to

quantify the different influences on the performance of a diagnostic system. This concept was

applied to the in-flight dataset to quantify the detectability of actuator faults, resulting in

measures which can be used in a diagnostic system to increase integrity of the overall health

assessment system.

Embedded Training – Immersive VR Training

A fully immersive virtual reality maintenance training application was designed and

implemented. The application demonstrates the training of complex procedures, which are

performed in reality by multiple technicians, in VR however trained by a single trainee. This

leads to an enhanced understanding of the interrelations between jobs at different aircraft

locations.

Implemented is the removal of the landing gear brake. The procedure requires actions at two

locations, at the landing gear and in the cockpit. A standard rendering software was used to

display the two implemented locations. The complete VR training system consists of a

helmet, data gloves and a tracking system. Special focus was on the software interface used to

interact with the VR environment. The chosen and implemented interface encourages the

trainee to explore the environment, but still guides through the training lesson. The group

training capabilities are realised by a “change place” function. This enlarges the range of

activity of the trainee in VR. The whole distributed maintenance action is carried out entirely

by the trainee. Technical interrelations become visible by this approach.

4. What Was Innovative

As a result of the studies on diagnostic and prognostic methods for the Uplock actuator, a new

method of signal-based fault detection was developed. The combination of basic wavelet

features and Support Vector Classification resulted in an highly flexible and powerful

statistical method for detection of transient and stationary signal characteristics.

The work on the GEMA actuator enabled the implementation and validation of an estimation

library with focus on hybrid systems. Further, the separation of external disturbances and

internal failures was validated through simulation experiments.

Based on real in-flight measurements, a sound statistics of usage profiles for primary flight

control actuators was derived. This can be used as a-priory information on fault detectability

regarding flight phases to increase the integrity of the diagnostic system. A generic concept of

fault diagnosability was proposed which utilises the hybrid system methodology.

A demonstrator for fully immersive VR maintenance training was developed. The

demonstrator can be used to train a landing gear brake removal procedure. Innovative is the

inclusion of a “change place” functionality. This allows the trainee to easily change his

location in the VR environment. This allows him to play multiple roles of different

technicians. By this, technical interrelations between the different locations are made visible

and memorable.

5. Conclusion and Exploitation

Participation in the TATEM project enabled the TUD to apply and expand its knowledge on

condition-based maintenance systems in the field of aviation.

Techniques formerly used for test-rig based diagnosis were refined towards in-flight usage.

The state-of-the-art of diagnostic methods was captured and assessed regarding the needs of

aircraft maintenance. Certain methods were validated with measurements, others through

simulation experiments. The understanding of concepts for assessing diagnostic systems was

broadened and laid on a systematic approach. Enablers for usage of prognostic systems have

been found and partially exploited. A solid understanding of necessary next steps for doing

prognosis was established.

By inclusion of VR training technologies, the whole scope of TUD regarding aircraft

maintenance was extended. This led to a thorough basis for upcoming projects regarding

aircraft maintenance, diagnostic and prognostic methods as well as training of procedures in

VR and communication between technicians through VR and related 3D technologies.

New projects in this field will focus on prognostic methods, incorporation of fleet data for

prognosis, robust and on-line capable diagnostic methods, proof of integrity the whole health

assessment concept and evaluation of the cost-effectiveness of health monitoring; all these in

application to general aircraft actuation systems and similar dynamic systems.