system health state monitoring using multilevel artificial...
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CIMSA 2005 – IEEE International Conference on
Computational Intelligence for Measurement Systems and Applications
Giardini Naxos , Italy, 20 - 22 July 2005
System Health State Monitoring using Multilevel Artificial Neural
Networks
1S.Colantonio, 1M.G. Di Bono, 1G. Pieri, 1O. Salvetti,1Istituto di Scienza e Tecnologie dell’Informazione, CNR, Pisa, Italy
Phone: +39-050-315-3146, Fax: +39-050-315-2810,
Email: {Sara.Colantonio, Maria.Grazia.DiBono, Gabriele.Pieri}@isti.cnr.it
Phone: +39-050-315-3124, Fax: +39-050-315-2810, Email: [email protected]
2G. Cavaccini
2Alenia Aeronautica, Finmeccanica, Viale dell’Aeronautica snc, 80038 Pomigliano D’Arco, Napoli, Italy
Phone: +39-081-887-3546, Fax: +39-081-887-3546, Email: [email protected]
Abstract – The assessment of the health state of complex physical
systems is of key importance for maintaining the same systems safe,
less expensive, adequately equipped and operating.
In this work, a methodology is defined for evaluating the structure
and performance integrity of a physical system or its components.
The monitoring activity is based on a Multilevel Artificial Neural
Network for describing, diagnosing and predicting the state of the
monitored system. Following a coarse-to-fine paradigm, artificial
neural networks of different topologies and typologies are
modularly and hierarchically combined to firstly process and
validate the sensor measurements acquired on-field, then classify
the validated measures and, at the end, predict the state of the
system.
In course tests on experimental data furnished by Alenia and
regarding aircraft components have shown that the proposed
method is a promising aid for the evaluation of the health state of a
physical structure and that it can be integrated inside a single
aircraft life cycle monitoring system.
Keywords – Multilevel Artificial Neural Network, Structural HealthMonitoring, Life Cycle Monitoring, Aircraft Components
I. INTRODUCTION
Tools for monitoring systems or components health state
are devoted to obtain, under test, information about
durability, damage tolerance and other possible structural
problems. Moreover, state prediction can improve safety and
reduce maintenance costs, by eliminating unnecessary
inspections, and supporting decision-making processes
during repair operations.
Models developed for evaluating integrity of physical
systems rely on a set of sensor measurements, acquired on-
field, which are compared to reference parameters in order to
identify potential anomalies. Descriptive mathematical
models are generally developed to derive the reference
parameters and to validate the real-time measurements.
Different approaches are used to predict the system state.
When a deep knowledge of the system structure is available
and the functioning of all the constituent components can be
formalized, an accurate model of the system itself can be
drawn and used for making diagnosis and prediction (model
based systems [1]). However, this approach requires a fixed
number of diagnosis classes, with consequent low flexibility,
and, more important, in many practical situations, precise
models of the system are difficult to formalize or even
unavailable. In these cases, different approaches are
followed. Usually, sets of rules, drawn from expert’s
knowledge, are applied to evaluate the sensor measurements
and to supply a final diagnosis (expert systems [1,2]). Other
methods attempt to avoid the difficult task of eliciting the
expert’s knowledge by building a large repository of sample
diagnoses or cases (case-based systems [1,3]). The prediction
for the currently examined case is obtained by identifying, in
the library of cases, one or more scenarios with known
diagnoses that match the current situation. However, different
drawbacks affect this kind of methods: a functioning
instability of the system, under different and variable
conditions, cannot be considered in such models, which are
very rigid and not easily adaptable. Besides, they can suffer
of a poor validation of the measurements, since the correction
of the values obtained from malfunctioning sensors is not
generally performed. On the other hand, when the case-based
approach is adopted, excessive computational efforts can be
required when searching the best matching case in the
repository.
Inductive learning, including decision trees, statistical
classifiers and Artificial Neural Networks (ANN) are also
used for the life cycle prediction, with different performance
results [1, 4, 5]. These methods are endowed with the ability
of extracting and acquiring the knowledge necessary to the
prediction on their own, through experience.
In this paper, we introduce a novel methodology, based on
a Multilevel Artificial Neural Network (MANN) model,
suitable to monitor and predict the functioning state of a
system or its components.
The system under test is sensorized in order to obtain sets
of measurements. A MANN architecture has been designed
so that each level performs different tasks: validation and
reconcilement of the acquired sensor measurements,
classification of the validated measures and prediction of the
system state. Two ANN typologies are used, i.e. the Self-
Organizing Map (SOM) [6] and the Error Back-Propagation
(EBP) [7].
An experimental activity performed on aircraft
components has shown that the proposed method is a
promising aid for the evaluation of the health state of a
0-7803-9025-3/05/$20.00 ©2005 IEEE 50
physical structure and that it can be integrated inside a single
aircraft life cycle monitoring system.
The paper is organized as follows: in Section II, the
developed methodology is discussed, illustrating in detail the
MANN architecture; Section III reports the experimental
activity carried out on two different test cases of interest in
the aeronautical industry (EFA Pylon Housing Box and J-
Spar component).
II. STATE MONITORING AND PREDICTION
The monitoring and prediction activity can be modeled as
a multiphase process: first, the data obtained from the
sensorized system should be processed in order to correct
eventual erroneous measurements (pre-processing phase);
then, the validated data should be elaborated to identify and
describe the current condition of the system (classification
phase); finally, the health state of the system should be
evaluated, also using additional historical and statistical
information (prediction phase).
This multiphase process can be mapped on a MANN
architecture composed of ANNs of different topologies and
typologies that might be modularly and hierarchically
combined.
This model should be able to assure computational
advantages since each network can be specialized in solving
its respective task, in terms of learning speed, generalization
and representation capabilities [8,9,10]. In other words, the
MANN behaves more robustly, more efficiently, and, also,
can generalize better than a single neural network.
Figure 1 shows the layout of the developed multiphase
process, which is described in the following.
A. Pre-processing phase
Owing to some malfunctioning, on-field measurements
might be physically incongruent. The pre-processing phase is
then introduced to perform data validation and obtain a set of
validated and congruent data, on the basis of a mathematical
model, drawn from expert’s knowledge.
To speed up this validation procedure and to improve as
much as possible the quality of the acquired data, a set of
neural modules is employed. Two different steps are
performed: a filtering process is applied in order to identify
sensors temporarily or permanently out of service and to
correct, when possible, some input values (elementary
validation); then, the evaluation of measurements congruence
and their reconcilement is performed using a set of N neural
modules, called Reconcilement Networks (RN), which are
based on an EBP model (Multilayer Perceptron, trained
according to the Error Back-Propagation algorithm), and
represent the first level of the MANN architecture.
The number N of neural networks to be trained
corresponds to the number of correlated data groups that can
be identified among the sensors, i.e. clusters of sensors
whose outputs mutually influence each other. This choice has
the advantage that (i) a single RN per each group of related
sensors supplies the network all the useful information it
requires to perform a correct reconcilement, and, (ii) the
training activity is simplified by avoiding the expensive
training of a single, complex network for the reconcilement
of all the measurements.
Fig. 1. The layout of the multiphase approach and the corresponding MANN
architecture.
To each RN, a certain weight is associated so that, in the
case in which more RNs calculate the same measure, the
reconciled data is obtained as a weighted average. The
training procedure is performed using the sensor data and the
validated measures obtained from the mathematical model.
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The results of these neural modules consist of the
reconciled values which are the input to the second phase.
B. Classification phase
Once the sensor measurements have been validated, they
can be used to identify and evaluate the current condition of
the system. This task can be accomplished by classifying the
measurement values, obtaining, in this way, a description of
the system conditions: each class can be identified as a
particular situation of the system as suggested, for instance,
by an expert.
A set of SOM modules, which constitutes the second level
of the MANN architecture, is trained to perform the
classification activity, discovering how data spontaneously
group to form coherent clusters of system conditions.
The same sensors grouping of the pre-processing phase is
maintained in this phase to identify the number N of SOM
modules: each group of sensor data, which mutually
influence each other, is used as input vector for a SOM. One
more time, this choice assures conceptual and computational
simplifications: sensor measurements of the same group
concur to evaluate a particular facet of the system condition,
which is identified by the corresponding SOM module. The
global condition can, then, be univocally described by
integrating the results of all the N SOMs. Computational
advantages are also evident.
Each of the N maps is trained according to the Kohonen
training algorithm [6]. Once trained, the clustering results of
each SOM can be assessed by analyzing how map units
organize themselves for representing the cluster extracted
from data [11] (e.g, by inspecting the map unified distancematrix - U-matrix - [12] which visualizes distances between
neighboring map units, allowing the identification of the
cluster structure of the map itself). This information can be
then used to define the clusters associated to each group of
sensor measurements, which could be recognized and
characterized as belonging to particular system conditions
pointed out by an expert.
Besides, the dimensions of each map can be determined
by analyzing the asymptotic behavior of the portion of
neurons that are never excited by an input vector when
increasing the number of map units (Figure 2).
This analysis can then be used to find, for each sensors
group, the SOM able to recognize all the possible clusters.
C. Prediction phase
At the higher level of the MANN architecture, a single
EBP module is used to obtain the final prediction on the
health state of the system.
The output of the classification phase supplies an evaluation
of the conditions of the system. This information can be
combined with other support parameters which aid achieving
a global prediction.
Fig. 2. Percentage of exited (dashed) and non exited neurons as a function of
the total number of map units.
These parameters consist of historical data and regard the
past and recent operating conditions of the system, and also
previous structural and functional evaluations performed on
analogous systems. This information can be opportunely
coded and inputted to the EBP module.
The EBP module is realized as a Multilayer Perceptrontrained according to the Error Back-Propagation algorithm.
The final prediction can be expressed in terms of the health
degree of the system, its life expectancy or the corresponding
state class. Further processing should use this information to
monitor the functionality of the system and support decision-
making process for its maintenance and repairing.
III. A STUDY-CASE: AIRCRAFT COMPONENTS
A MANN prototype has been tested on data obtained from
measurements furnished by Alenia.
Two different Non-Destructive Testing experiments were
carried out regarding
1. EFA Pylon Housing Box
2. J-Spar structure.
In situ sensor measurements and historical-statistical data
(Service Bulletin) were used as input data.
D. EFA Pylon Housing Box
In this experiment, see Fig. 3a, three different test
conditions were chosen, maintaining a fixed temperature of
100°C (Fig. 3b):
1. a load charge on two jacks (A and B) with 112% of the
limit load
2. a load charge on five jacks (A, B, K, L, and M) with
112% of the limit load
3. a load charge on five jacks (A, B, K, L, and M) with
100% of the maximum load
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For each test condition, a stream of sensor measurements
was acquired, by applying progressive tensile or bending load
charges to each jack and measuring the material
deformations. A stream was composed of a set of cycles
consisting, in their turn, of 12 increasing percentages of the
limit load (ranging from 1% to 112%). For each load
percentage, a record of sensor measurements was acquired
by means of 17 strain gauges (SG), seven of which with one
channel, and the remaining ten with three channels (Fig. 4).
A total number of 250 cycles was acquired, each composed
of 12 records of the following 45 measures:
progressive load charge percentage with respect to the
limit one (1 measurements);
load charge on each jack (5 measurements);
deformations of each strain gauge for every channel
(total of 37 measurements);
temperatures revealed from two thermocouples (2
measurements).
The MANN was then trained to predict the class of state of
the component after each cycle.
The sensor measurements were divided in five groups,
individuated on the basis of the correlations among the SGs
pointed out by the experts (e.g. position). SGs with three
channels were then grouped into three different groups, while
the SGs with one channel were grouped in two different
groups. The temperature values were added to each group of
SGs measurements as well as the load percentage and the
charge values applied to each jack, since they are considered
as correlated parameters.
(a) (b)
Fig. 3. (a) The experimental set-up for EFA Pylon inspection; (b) drawing
indicating the jacks position.
Five RNs were developed to reconcile the acquired data
using the Neural Network Toolbox of MATLAB. Each
network was trained on 1800 records, corresponding to 150
cycles, and then tested on the remaining 1200 ones. Due to
the simple relations among the measurements, each RN
reached the better performance with only one hidden layer
and a number of hidden units ranging from 30, for the group
with four strain gauges with three channels, to 20, for the
group composed of three SGs with only one channel.
Fig. 4. The location of two groups of sensors: on the left, a group of three
SGs with three channels, on the right, a group of four SGs with one channel.
Five SOM modules (one for each group) were trained to
classify each cycle. Different dimensions for the maps were
experimented, controlling the asymptotic behavior of the
excited vs not-excited neurons (Fig. 3). At the end, a 10 10
lattice was chosen to process the three groups of SGs with
three channels, while an 8 8 lattice was selected for the two
groups of one-channel SGs. Three different component
conditions were identified by an expert and used to label
some of the training examples of the SOMs. This information
was used to characterize the clusters on the map obtained
after training. An example of the cluster identification can be
seen in Figure 5, for one of the sensors group of three-
channels SGs: bullets correspond to the first condition,
diamonds to the second and squares to the third one.
Fig. 5 Clusters on the map corresponding to the three component conditions
suggested by the experts.
For the prediction phase, the so-called Service Bulletinswere used as historical-statistical data, supplying information
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regarding the types of component monitored, of the
corresponding aircraft, and of the same aircraft rout and the
mean of the hours of flight. An opportune coding of the text
information was chosen on the basis of a simple classification
of the same information, e.g. the rout information was
classified as ‘mainly on seas/oceans’, ‘mainly on lands’ or ‘a
mixture’ and coded enumerating each class.
Combining the support data with the classification results
of the SOMs, that is vectors of five components, a training
set of 225 samples was used to train EBP networks of
different architectures, i.e. with different number of hidden
units. The target values were identified as three possible
prediction classes regarding the structural and functional
integrity degrees, ‘good’, ‘normal’, ‘bad’, i.e. three output
units of the network corresponding to the membership to
each of these classes. The test activity performed on the
remaining 200 cases pointed out that the best performance
was obtained with the EBP network with 25 hidden units
(Table I).
Table I. Performance in terms of recognition score of the EBP module used
for the final prediction
Prediction Classes
Good Normal Bad
96.7% 98.2% 97.3%
E. J-Spar Component
The second test case consisted in tensile and bending
stress of composite J-Spar components (Fig. 6), in both cases
of presence and absence of cracks. The experimental protocol
was similar to the previous case: cycles of progressive loads
were applied to the components and the resulting strains were
sequentially measured by using seven SGs with one channel
(Fig. 6), six of which were coupled (i.e. positioned on the
opposite sides of the component to measure strain of opposite
sign).
Fig. 6. Two views of a J-Spar component with the applied seven Strain
Gauges.
An example of the measurement results is reported in
Figure 7: the plot shows the sensor measurements for a
sequence of 50 tensile loads of increasing value.
Fig. 7. An example of sensor measurements in answer to 50 increasing
bending loads. Different lines correspond to different channels
(CH1,..,CH7), i.e. to different SGs. The three SGs couples are CH1, CH5;
CH3,CH7; CH2, CH6, respectively.
Due to the simplicity of experimental settings, only
elementary validation was performed for the pre-processing
phase. For the same reason, the classification task was
accomplished by a single SOM module, trained on input
patterns, each consisting in an entire test sequence of 50
vectors composed of the seven sensor parameters plus the
load charge value. A 10 10 map gave the best excited over
not-excited neurons ratio. After training, five clusters were
identified on the map according to the distances between
neighbor neurons (Fig. 8).
Fig. 8. The five clusters determined on the SOM structure after training
In this case, the support information consisted in the tests
history of the examined component, i.e. the load charges
applied to the component during previous tests. The mean
and standard deviation of these data were computed as
historical-statistical information and appended to the
classification result of the SOM for training the EBP
network. This network was trained on a data set of 150
samples to predict the life expectation of the component
(only one output unit, which returned the life expectation
value). Different architectures, with different numbers of
hidden units, were trained and tested on a set of 80 samples.
The best results were obtained with only one layer of 15
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hidden units, obtaining a percentage of correct predictions
equal to 97,6%.
IV. CONLUSIONS
A methodology has been proposed for evaluating the
structure and performance integrity of a physical system. In
particular, a multilevel artificial neural network architecture
has been designed to process measurements obtained from a
sensorized system and then obtain a prediction of the
integrity of the system itself.
The developed architecture has been applied to a real
study case, i.e. aircraft components non-destructive testing,
using sensor data supplied by Alenia. Two system
components were considered in different experimental
settings, pointing out the capability of the MANN to adapt
itself to the specific needs of each application. Results
obtained showed effectiveness and reliability of the proposed
methodology.
ACKNOWLEDGMENT
This work has been partially supported by a CNR-
Finmeccanica agreement and EU MUSCLE NoE Project.
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