**Design of SHM-Embedded Structures for Space Operation Vehicles**

Fu-Kuo Chang, Johannes F.C. Markmiller, Jinkyu Yang, Yujun Kim

Department of Aeronautics and Astronautics

Stanford University, Stanford CA

**ABSTRACT**

Structural health monitoring plays an essential role in the integrated
vehicle health management system for space transportation vehicles. Due
to the natural characteristics of space structures, the requirements for
SHM can be quite different from aircraft structures. This paper examines
the potential impact of SHM on the space structures and their
requirements for such applications. Attention is focused on diagnosing
damage autonomously in a space environment and the technology needs for
developing such a system at the vehicle design stage.

**INTRODUCTION**

Integrity, reliability and safety are critical for space operation
vehicles (SOV), particularly when human beings are involved. Knowledge
of the health condition of the vehicle in orbit and on the ground is
crucial for maintaining safe operation of the vehicle. As lessons
learned from the shuttles, traditional tear-down inspection techniques
would make the new vehicle not only too costly to maintain and operate,
but most importantly, they would be very difficult, if not impossible,
to perform while the vehicle is in orbit. Accordingly, traditional
inspection techniques could hardly ensure the integrity, reliability, or
safety of the vehicles while they are in orbit.

Recent advances in structural health monitoring (SHM) technology have
resulted in significant promising techniques to offer autonomous
solutions for detecting and assessing the health condition of
structures, providing a real-time early warning capability to mitigate
damage, and reducing considerably downtime and maintenance cost. SHM has
been demonstrated potentially for a wide range of applications from
civil infrastructures to transportation systems. It is obviously well
suited to applications for space operation vehicles.

However, there exist significant discrepancies in performance among
these structures as well as their operation environments, so the
requirements of SHM techniques for each application can be quite
different in order to accommodate their unique demands and needs. For
instance, an SOV experiences a much harsher environment than traditional
aircraft during ascent and decent. While SOV vehicles fly less often,
they are remote and inaccessible during each flight. Furthermore, there
will be only a few SOV to be produced compared to aircraft in the
hundreds. The loss of a vehicle can be detrimental to the entire space
operation.

Table 1 lists a survey by Boeing Company which identified the top five
high payoff areas for SHM application in SOV and characterized 10 common
damage types in space vehicles \[1\]. High on the list are thermal
protection panels and fuel tanks. Unlike aircraft structures, which
produce well-known "hot-spot" areas due to fatigue/cyclic loads
resulting from frequent operations, SOV is prone more to encountering
unexpected events such as foreign object impacts and a harsh environment
during its operation. To detect foreign-object impact, the exposed
surface of the entire SOV under consideration must be monitored (for
instance, the 2003 shuttle disaster); however, to prevent the attack of
harsh environment such as elevated temperatures, knowledge of the
integrity of joints and connectors where material property mismatch is
the highest is critically important (for instance, the 1986 shuttle
disaster). For SHM to be effective for space application, it must not
only be able to identify the location of damage and quantify it; but
also it shall interface with an appropriate failure analysis to estimate
the effect of such damage on the integrity of the vehicle for its
remaining operation in space.

+-----------------------+-----------------------------+----------------+
| **Identified Top 5**  | **Rankings by Area**        |                |
|                       |                             |                |
| **High Payoff Areas** |                             |                |
+-----------------------+-----------------------------+----------------+
|                       | **Area**                    | **Rank**       |
+-----------------------+-----------------------------+----------------+
|                       | TPS                         | 1              |
+-----------------------+-----------------------------+----------------+
|                       | Tank                        | 2              |
+-----------------------+-----------------------------+----------------+
|                       | Wing Structures             | 3              |
+-----------------------+-----------------------------+----------------+
|                       | Leading Edges               | 4              |
+-----------------------+-----------------------------+----------------+
|                       | Thrust Structures           | 5              |
+-----------------------+-----------------------------+----------------+
|                       |                             |                |
+-----------------------+-----------------------------+----------------+
| **Identified Top 10** | **Rankings by Damage/Data   |                |
|                       | Types**                     |                |
| **Damage/Data Types** |                             |                |
+-----------------------+-----------------------------+----------------+
|                       | **Damage/Data Types**       | **Rank**       |
+-----------------------+-----------------------------+----------------+
|                       | Crack Growth                | 1              |
+-----------------------+-----------------------------+----------------+
|                       | Fuel Leak                   | 2              |
+-----------------------+-----------------------------+----------------+
|                       | Over Temperature            | 3              |
+-----------------------+-----------------------------+----------------+
|                       | Impact Damage               | 4              |
+-----------------------+-----------------------------+----------------+
|                       | Bondline Failure            | 5              |
+-----------------------+-----------------------------+----------------+
|                       | Composite Delamination      | 6              |
+-----------------------+-----------------------------+----------------+
|                       | Load Monitoring             | 7              |
+-----------------------+-----------------------------+----------------+
|                       | Temperature Monitoring      | 8              |
+-----------------------+-----------------------------+----------------+
|                       | Leading Edge Mass Loss      | 9              |
+-----------------------+-----------------------------+----------------+
|                       | Corrosion                   | 10             |
+-----------------------+-----------------------------+----------------+
| Note: The ranking     |                             |                |
| shown in the tables   |                             |                |
| may vary depending on |                             |                |
| the                   |                             |                |
|                       |                             |                |
| engineering           |                             |                |
| judgement, but        |                             |                |
| basically with the    |                             |                |
| range                 |                             |                |
+-----------------------+-----------------------------+----------------+

Table 1: Ranking of critical areas and common damage types in SOV for
SHM applications

In this paper, the framework of an SHM-embedded system is proposed for
diagnosing impact events and for detecting joint failure in thermal
protection panels for SOV applications.

**Impact Monitoring**

For an SOV to be capable of diagnosing an impact event, the SHM system
shall use the embedded sensor measurements to detect the impact and then
determine the effect of such an impact on the integrity of the
structures for their remaining operation. Numerous sensor-based impact
identification techniques have been developed in the literature \[2-8\],
which can be classified into two types typically: a neural network-based
approach and a model-based approach. The former is good for identifying
impact location but is not efficient for assessing the impact force. It
requires collecting exhausted impact data set through training tests
throughout the external surface of the vehicle, which may not be
feasible practically to perform. The latter uses a model of the
structure to estimate the impact force. While it is straightforward to
calculate local strains for a given impact force with an accurate
physical model, the inverse solution cannot be obtained easily.
Iterative methods have been proposed by some researchers \[9\] to solve
this problem but such an approach is slow and difficult to use for
complicated structures.

Recently, a mixed approach based on system identification was proposed
which requires only limited training data on the actual structure \[9\].
This approach produces a system model, which then could be used to
effectively estimate the impact force and location in nearly real time.
This approach has been demonstrated successfully on various types of
structures from stiffened panels to TPS panels \[9-10\].

For an SOV application, the number of impact training data on actual
vehicles should be minimal or eliminated because of the delicateness of
the structures, which can also be quite complicated in geometry and
material properties. In order to further minimize the number of training
data, it is proposed to utilize finite element analysis to generate the
system model from a mixed approach instead of using training data from
physical tests. If the same finite element analysis that is used for the
vehicle structure design could be utilized to develop the SHM model,
then a linkage between traditional damage tolerance analysis and SHM
prediction could be established. Accordingly, a schematic of the
proposed SHM-embedded design for impact detection and diagnosis is
illustrated in Figure 1.

As shown in Figure 1, a transient dynamic FEM model, which can simulate
the strains at sensor locations upon impact is used to train a system
model required for the SHM system. In the first step arbitrary impact
forces are applied to the structure and the sensor signals are
calculated using the FEM model. The force and the signals of the sensors
are then used to obtain a transfer function (the system model) as
described in \[9\].

![](media/image1.jpeg){width="4.433333333333334in"
height="3.3986111111111112in"}

Figure 1: The proposed SHM-embedded design for impact diagnosis in space
operation vehicles

In the second step, if an actual impact occurs, the impact force is
estimated from the system model with the actual sensor data. The
estimated impact event is then fed back to the FEM model, and the
stresses during the impact event can be calculated. Finally, the
stresses allow an estimate of the damage size and of the residual
strength of the structure using appropriate failure criteria from a
traditional damage tolerance analysis.

To verify the approach tests have been performed with a plate made of
unidirectional graphite/epoxy composites. The dimension of the
\[0~4~/90~4~\]~s~ plate is 12 inches by 12 inches with a surface-mounted
sensor network of eight piezoelectric sensors made by Acellent
Technologies (SMART layer) \[11\]. The plate was subjected to a
drop-weight test with a weight of 3.5 lbs dropped from a height of 8.2
feet. A drawing of the plate showing the sensor location, the boundary
conditions and the impact location can be seen in Figure 2. An
X-radiograph was used to examine impact damage induced by the impactor,
along with visual inspection.

![](media/image2.jpeg){width="4.039583333333334in"
height="3.0069444444444446in"}

Figure2: Plate with SMART layer

The commercial ABAQUS code was utilized to simulate the impact dynamics
of the plate. Figure 3 shows the finite element mesh used in the
simulations of an arbitrary impact by the impactor. The code calculated
the responses of the piezoelectric sensors, which were used to determine
the system model of the plate based on the mixed impact identification
approach \[9\].

![](media/image3.jpeg){width="2.8652777777777776in"
height="2.0854166666666667in"}

Figure 3: FEM model of the plate and the impactor

Once the system model was determined independently, the actual impact
signals generated from the impact tests were used within the system
model to estimate the impact force from the actual steel impactor. The
signal measured during the drop test from one of the sensors can be seen
in Figure 4. The location of this sensor can be found in Figure 2.
Figure 4 also shows the impact force estimated from the system model and
this measured signal.

  ------------------------------------------ ------------------------------------------
  **Measured sensor signal**                 **Calculated impact force**

  ![](media/image4.jpeg){width="2.79375in"   ![](media/image5.jpeg){width="2.79375in"
  height="2.095138888888889in"}              height="2.095138888888889in"}
  ------------------------------------------ ------------------------------------------

Figure 4: Measured sensor signal and calculated impact force

The reconstructed impact force-time history was then fed back as an
input to the FEM model to estimate potential impact damage induced by
the impactor on the plate. In the study, an impact-induced delamination
criterion proposed by Choi and Chang \[12\] was selected because of its
simplicity. Figure 5 shows a comparison of the predicted damage size and
the X-ray picture taken from the specimen. It can be seen that the
damage size could be estimated with good accuracy by combining the SHM
system model with the traditional FEM analysis. More studies are being
performed to further verify this approach in much more complicated
structures.

  ---------------------------------------- -----------------------------------------------------
  ![](media/image6.jpeg){width="2.675in"   ![](media/image7.jpeg){width="2.9916666666666667in"
  height="1.7118055555555556in"}           height="1.7243055555555555in"}

  Calculated damage size                   X-ray
  ---------------------------------------- -----------------------------------------------------

Figure 5: Calculated damage size and X-ray

**Detection of Bolt Loosening in TPS**

Loosening of bolts in mechanically fastened thermal protection panels
can be detrimental to the integrity of the entire vehicle due to harsh
environments. Hot air can enter the gap at joints and destroy the
vehicle. Therefore, the ability to early detect bolt loosening or damage
such as cracks at joints is critically important for an SHM system.

Detection of bolt loosening/damage is very much different from
monitoring an impact event. The former (referred to as active SHM)
requires input excitation from transducers or actuators in order to
produce diagnostic sensor signals, while the latter (passive SHM) uses
the signals generated automatically through the external impact energy.
There are numerous bolt loosening/damage techniques proposed in the
literature, most of which are based on the impedance method or wave
propagation techniques \[13 - 18\].

Recently, Yang and Chang developed a built-in bolt-loosening detection
technique by analyzing signal attenuation of sensors based on
propagation stress waves generated by an actuator located inside a
washer \[19\]. Figure 6 shows a schematic description of a smart washer
which was designed specifically for this application. The washer
contains a piezoelectric transducer to generate propagating waves as
well as to receive signals. The washers were then used to replace the
existing washers without altering the original design and configuration
of the TPS structures. The washer is placed at the bottom of each leg of
the supporting bracket as shown in Figure 7 to be as far away from the
surface TPS panels as possible to minimize temperature effect but still
be effective for monitoring when the panels are exposed to extreme high
temperatures. One smart washer is used to generate diagnostic
propagating waves and the washer at the other leg of the bracket is to
collect the signals. By comparing the signals generated from the same
actuator at different times, an algorithm was developed based on the
stress wave attenuation to estimate the degree of bolt loosening at the
base bracket or at the top TPS panel.
![](media/image8.wmf){width="5.352777777777778in"
height="1.4756944444444444in"}

![](media/image9.wmf){width="1.8688976377952755in"
height="1.5200787401574802in"}

Figure 7: Smart washer design

The technique has been demonstrated successfully to identify the bolt
loosening in the TPS panel \[19\]. A closer view of the smart washer
design is shown in Figure 7. Clearly, the success of an active SHM
system depends not only on the diagnostic algorithms, but also on the
design of the sensor/actuator network system where the diagnostic
signals are generated. The detectability of bolt loosening can be very
much dependent upon the location and configuration of the sensors and
the actuators, particularly as most mechanical joints and connectors may
involve multiple materials with various thermal expansion coefficients
and can be complicated in configuration.

Therefore, a retrofit of an active SHM system to an existing space
structure can be quite challenging if not impossible because the
detectability of the sensor/actuator network could be severely
compromised in the existing configuration since any major modification
to the existing components is normally prohibited. It is therefore
necessary for space structures that the installation of an active SHM
system must be considered at the initial design phase in order to be
effective in detection and to be properly protected from harsh
environments.

Accordingly, for SHM integration, it is proposed here to integrate the
active SHM technique with a proper analysis in the initial design phase
to maximize the system detectability. The result of the SHM predictions
will then be fed back to the failure analysis to determine the residual
strength and remaining life of the structures. A schematic description
of the proposed approach is illustrated in Figure 8.

![](media/image10.wmf){width="3.877083333333333in"
height="3.3854166666666665in"}

**Design of built-in SHM system**

The success of a built-in SHM system that deduces in-situ "health"
through active sensors and real-time data processing relies heavily on
sensor network design. In addition, the durable integration of sensors
with structures needs to enhance the integrity of the system. In order
to establish a fundamental understanding of the interactions not only
between diagnostic wave and damage, but also between sensor and
structure, numerical approaches in design and analysis are necessary.
There exist numerous numerical techniques such as finite element method
(FEM), finite difference method (FDM) and boundary element method (BEM)
for modeling the dynamic response of structures \[20-24\].

However, none of these methods are effective in analyzing stress wave
propagations in complex structures with built-in sensors and actuators.
Most of them would require excessive computational resources such as
computing time and memory, which would preclude them from being utilized
in the initial design phase.

Recently, Kim and Chang \[25\] have demonstrated that the spectral
element method (SEM) could be a powerful alternative to the existing
numerical methods for analyzing structures with built-in sensor and
actuator networks. The SEM first developed in the mid 1980s \[26\] is a
weighted residual method like FEM. Main features of the SEM are a
subdivided whole domain, called elements, and high-order interpolation
with particular quadrature rules (see Figure 9). Therefore, the SEM
conserves advantages of FEM such as geometric flexibility and interface
modeling in addition to an exponential rate of the solution convergence
that means drastic reduction of the required computational resource
\[25\].

With the SEM as a computational engine, the traditional computer-aided
design/engineering (CAD/CAE) can be utilized more rapidly and less
expensively to analyze and characterize sensor and actuator responses in
the SHM-embedded structures. Such simulations are crucial for
understanding fundamentally the detectability of the SHM system and for
optimal design of the system with minimal trial and error from the
laboratory, which is presently the common practice. Through a standard
design iteration, the sensor/actuator network configuration could be
constructed to be effective for the particular structures. Figure 10
describes the proposed essential design flowchart for constructing
SHM-embedded structures. With the SEM analysis, the SHM techniques could
potentially be integrated and interfaced with the traditional design
process. The diagnostic algorithms such as digital imaging analyses and
a damage index for estimating damage could be integrated with SEM
analysis to simulate SHM detectability without physically constructing
the system in the laboratory \[27 -- 31\]. This process will
significantly improve the reliability and accuracy of the SHM system in
practice and expedite the implementation process.

![](media/image11.wmf){width="2.0in" height="1.1041666666666667in"}

\(a\) 3D linear finite element (b) 3D 6-th order spectral element

Figure 9: Comparison between finite element and spectral element

![](media/image13.wmf){width="5.469444444444444in"
height="3.654166666666667in"}

Figure 10: Simulation and optimization by CAD/CAE interfaced with
digital imaging method

**CONCLUSION**

SHM techniques can have a significant impact on the safety and
reliability of space operation vehicles. Concerns about the design of
SHM systems for the detection of foreign-object impact and monitoring
the integrity of thermal protection panels were discussed. The SHM
systems for space applications must not only be capable of detecting
impact and monitoring damage, but must also be able to predict the
integrity of the structures with estimated damage in a timely manner. It
is recommended that the integration of SHM techniques into space
operation vehicles shall be performed at the initial design phase to
maximize its effectiveness in detection and to provide proper protection
to the system. New techniques and innovative tools that are still
fundamentally lacking need to be developed for implementing the SHM
system in structures.

**ACKNOWLEDGEMENT**

The authors are grateful for the support of Boeing Commercial Airplane
Group, Lockheed Martin, and the National Science Foundation for this
research. Eric Haugse, Suraj Rawal, and S.C. Liu are the program
monitors, respectively.

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