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: Ovidio.Salvetti@isti.cnr.it

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: gcavaccini@aeronautica.alenia.it

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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