Considering the practical necessity of realizing the aircraft structure health monitoring (SHM), not only some advanced sensors must be adopted, an appropriate signal processing method is also necessary, in order to achieve the goals of signal characteristic extraction and health assessment. Based on the fact that piezoelectric (PZT) sensor signals extracted from the monitored structure carry a lot of useful health related information, this paper proposes to utilize the power characteristics of PZT signals for health assessment of the aircraft structure. The Hilbert-Huang Transform (HHT) algorithm is employed to calculate the power characteristic vector, after that, the self-organizing map (SOM) is used to map the corresponding characteristics into a confidence value (CV) which represents the health state of the monitored structure. The experimental results demonstrate that the proposed method is reliable

Jinhao Qiu

Qian Sun

Youning Tang

Zhenbang Lv

English

Vibroengineering PROCEDIA

  © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. NOVEMBER 2014. VOLUME 4. ISSN 2345-0533 11 
Signal processing and health assessment techniques in 
structural health monitoring 
Youning Tang1, Qian Sun2, Zhenbang Lv3, Jinhao Qiu4 
1, 2, 3Aeronautic Computing Technique Research Institute, Aviation Industry Corporation of China,  
Xi’an, China 
4College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Jiangsu, China 
1Corresponding author 
E-mail: 1yntang@aero-software.com, 2sunqiansmile@163.com, 3lvzhenbang@tom.com, 4qiu@nuaa.edu.cn 
(Accepted 17 June 2014) 
Abstract. Considering the practical necessity of realizing the aircraft structure health monitoring 
(SHM), not only some advanced sensors must be adopted, an appropriate signal processing 
method is also necessary, in order to achieve the goals of signal characteristic extraction and health 
assessment. Based on the fact that piezoelectric (PZT) sensor signals extracted from the monitored 
structure carry a lot of useful health related information, this paper proposes to utilize the power 
characteristics of PZT signals for health assessment of the aircraft structure. The Hilbert-Huang 
Transform (HHT) algorithm is employed to calculate the power characteristic vector, after that, 
the self-organizing map (SOM) is used to map the corresponding characteristics into a confidence 
value (CV) which represents the health state of the monitored structure. The experimental results 
demonstrate that the proposed method is reliable. 
Keywords: structural health monitoring (SHM), signal processing, health assessment, 
Hilbert-Huang transform, self-organizing map.  
1. Introduction 
Structural health monitoring (SHM) technique is integrated smoothly by information science 
and engineering with materials science, which utilize embedded or surface pasted sensor system 
as the neural system, capturing health related real-time information online, in order to monitoring 
and predicting the structural damages by using information processing and fault diagnosis 
methods. The real-time monitoring of the aircraft components can be achieved by utilizing the 
SHM technique, which saves the time for disassembling and checking the related components. As 
a result, the flight time can be guaranteed, and the maintaining cost is sure to be largely reduced. 
Recently, the applications of SHM are mainly concentrated in structural hardness and fatigue 
test on ground. Around the year 2013, Airbus and Boeing Company planned to work on the online 
monitoring of the aircrafts, and hoped to realize the goal of the whole aircraft monitored by the 
year of 2018. Since the 1990s, our country started to investment in researches on complicated 
large-scale aircraft SHM technology of the 863 Aerospace hi-tech program. Some research units 
like NUAA, Chongqing University, HUST, HIT, etc. have got a lot of achievements on SHM 
work, such as self-adaptive composite materials, self-diagnosis and self-repairing of fiber smart 
structural, as well as damage localization using acoustic emission, wavelet analysis and neural 
network [1]. These methods mainly focus on the signal itself, less considering about digging into 
the inner characteristics of the monitoring signals. This paper proposes to utilize the power 
characteristics of PZT signals for health assessment of the aircraft structure, and the experimental 
results demonstrate that the proposed method is reliable. 
2. Experimental setup 
Sensor system acts as the info-source of the SHM, considering the aircraft structural 
characteristics, the chosen sensors should be light, small, low-power, large range of working 
temperature, good environmental adaptability and inference resistance, arranged at the points that 
well reflect the changes of related parameters, and finally respond to the health state of the aircraft 
SIGNAL PROCESSING AND HEALTH ASSESSMENT TECHNIQUES IN STRUCTURAL HEALTH MONITORING.  
YOUNING TANG, QIAN SUN, ZHENBANG LV, JINHAO QIU 
12 © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. NOVEMBER 2014. VOLUME 4. ISSN 2345-0533  
structural. In this experiment, we use the piezoelectric material sensor PZT [2] to monitor the 
structural health state of a CFRP stiffened panel, the experimental set up is shown in Fig. 1, 
showing the panel bonded with PZT sensors. 
 
Fig. 1. Experimental setup 
 
Fig. 2. Distribution of PZT sensors on the panel 
Compared with traditional sensors, PZT sensors possess the advantages of electromagnetic 
interference resistance, waterproof, high temperature resistance and corrosion resistance. In order 
to perform the multi-site damage detection, the monitoring area is divided into several subareas. 
Considering the property of the Lamb wave in specific material and the geometric shape of the 
CFPR stiffened panel, the distribution of PZT sensors is optimized, shown in Fig. 2. 
In this study, we set the No. 3 area as an example, under the impulse sine wave of sensor 4c at 
a rate of 60 kHz, the signal of sensor 4a is used to monitor the structural health of this area. After 
the signal feature extraction process, the extracted Power feature vectors from the sensor 4a are 
input to a SOM neural network, which will help to get a normalized confidence value (CV) that 
indicates the health state of the No. 3 area.  
3. Power feature extraction from the monitored signal 
 
Fig. 3. The flowchart of empirical mode decomposition 
The HHT method is composed of empirical mode decomposition (EMD) and Hilbert transform 
(HT) [3]. Its core part is to decompose the signal through EMD method and get the intrinsic mode 
functions (IMF) of signal; the detailed process of EMD is shown in Fig. 3. For the IMF 
components, they must satisfy the following two conditions:  
1) The number of zero-crossing points and the number of extreme points must be equal or at 
most differ by one; 
2) The mean value of the envelope defined by the local maxima and the envelope define by 
the local minimum is zero. Once the IMF components are obtained, then do the Hilbert transform 
SIGNAL PROCESSING AND HEALTH ASSESSMENT TECHNIQUES IN STRUCTURAL HEALTH MONITORING.  
YOUNING TANG, QIAN SUN, ZHENBANG LV, JINHAO QIU 
 © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. NOVEMBER 2014. VOLUME 4. ISSN 2345-0533 13 
on them so as to obtain the instantaneous frequency and amplitude of signal. 
EMD method is based on local feature analysis of the monitord signal, which explodes the 
complicated signal into numbered IMF components that reflect their simple oscillaation modes 
respectively, containing the frequency conponets from high frequency to low frequency in due 
order [4]. Besides, EMD method is complete and the IMF components perfom a good 
orthogonality, which makes the IMF Power feature vectors aviliable to be utilized for health 
assessment. Fig. 4 and Fig. 5 show the EMD results of normal state and damaged state of the 
panel’s No. 3 area, respectively. 
 
Fig. 4. EMD result of the normal state panel 
 
Fig. 5. EMD result of the damaged state panel 
 
a)  
b)  
Fig. 6. a) HHT spectum of the normal state and b) damaged state panel 
Through the decomposition process mentioned in Fig. 3, we got the wanted IMF components. 
After doing the Hilbert Transform on each IMF component ܿ௜, with the Hilbert spectrum defined, 
then the Hilbert marginal spectrum can also be be expressed as follows: 
ܪሺ߱, ݐሻ ൌ Re෍ܽ௜ሺݐሻ݁௝׬ ఠ೔ሺ௧ሻௗ௧
௡
௜ୀଵ
, ݄ሺ߱ሻ ൌ නܪሺ߱, ݐሻ݀ݐ
்
଴
. (1) 
In Eq. (1), ܶ  is the sampling time. The marginal spectrum offers a measure of the total 
amplitude contribution from each frequency value. This spectrum represents the accumulated 
amplitude over the entire data span in a probabilistic sense. 
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-5
05
Am
pli
tud
e EMD-panel in good health state
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-5
05im
f1
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.5
00.5im
f2
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.2
00.2im
f3
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.1
00.1im
f4
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.05
00.05im
f5
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.05
00.05im
f6
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.05
00.05r6
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-2
02
Am
pli
tud
e EMD-panel in damaged state
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-2
02im
f1
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.2
00.2im
f2
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.05
00.05im
f3
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.01
00.01im
f4
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-5
05
x 10-3
im
f5
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.01
00.01im
f6
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2-0.01
00.01im
f7
1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 20
0.010.02r7
SIGNAL PROCESSING AND HEALTH ASSESSMENT TECHNIQUES IN STRUCTURAL HEALTH MONITORING.  
YOUNING TANG, QIAN SUN, ZHENBANG LV, JINHAO QIU 
14 © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. NOVEMBER 2014. VOLUME 4. ISSN 2345-0533  
HHT spectrum displays the rules of the signal power distrubuting along with the instant 
frequency, Fig. 6 shows the HHT spectrum of normal state and damaged state of the panel’s No. 3 
area, respectively. 
For the purpose of realizing the health assessment of the panel, the first six IMF components 
are selected to perform the HHT process, after that, the correspond power values are calculated 
and used as the feature vector for the SOM-based health assessment. The EMD-based Power 
feature extracted process is shown in Fig. 7. 
Fig. 7. EMD-based Power feature extracted process 
 
Fig. 8. Structure of SOM network 
4. Structural health assessment based on SOM 
There have been some techniques that are often used in healing systems, such as the distance 
function distinction, the artificial neural network, the support vector machine, the multi-agent 
technology, etc. 
Self-organizing map (SOM) network is a kind of competitive artificial neural network that is 
suitable for data clustering [5]. The SOM network combines an input layer with a competitive 
layer of processing neurons, which typically organized as a two-dimensional grid. The structure 
of the SOM network is shown in Fig. 8. As shown in Fig. 8, the SOM network is an array of  
ܯ ൌ ݉ ൈ݉  processing neurons, which maps high dimensional input vectors onto a 
two-dimensional surface, in which each neuron is represented by one-dimensional weight vector. 
Neurons of the map are connected to adjacent neurons by a neighborhood relation, which indicates 
the topology of the map. 
The weight vector is enhanced during training procedure by the learning rule: 
ݓ௜ሺݐ ൅ 1ሻ ൌ ݓ௜ሺݐሻ ൅ ߙሺݐሻ ⋅ ݄஻ெ௎,௜ሺݐሻ൫ݔሺݐሻ െ ݓ௜ሺݐሻ൯,	 (2)
where ݓ௜ሺݐሻ  is the weight vector, ߙሺݐሻ , the learning rate of SOM network, ݄஻ெ௎,௜ሺݐሻ , the 
neighborhood function determined by the distance between the Best Matching Unit (BMU) and 
the neighbor neurons.  
All the neurons are grouped in clusters by their distances through the training process, which 
form a new kind of topology structure. After that, each group of test data inputted to the 
well-trained SOM can match with a BMU. Finally, the Minimum Quantization Error (MQE) can 
be obtained by calculating the Euclidean distance between the BMU and the real-time 
measurement data. 
According to the principles of SOM, the normal signals with similar features as the training 
dataset will gather around the normal cluster, which should have a minimum value of MQE. Along 
with the structural health state degradation of a panel area, its features from the vibration signal 
turn to be more and more different from those of the normal condition. Accordingly, the values of 
MQEs will have an increasing trend over time as the structural health state of a panel deteriorates. 
Above all, the Power feature extraction and health assessment process based on SOM neural 
network can be visualized as Fig. 9.  
 
Fig. 9. The process of health assessment based on SOM 
SIGNAL PROCESSING AND HEALTH ASSESSMENT TECHNIQUES IN STRUCTURAL HEALTH MONITORING.  
YOUNING TANG, QIAN SUN, ZHENBANG LV, JINHAO QIU 
 © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. NOVEMBER 2014. VOLUME 4. ISSN 2345-0533 15 
In this study, the confidence value (CV), which can be formulated as below, is used to represent 
the structural health state of a panel area: 
ܥܸ ൌ ݁ି
ඥெொா
ఈ ,	 (3) 
where, ߙ is a scale parameter determined by the averaged MQE under normal conditions and the 
corresponding CV, and the MQE is generated by using the online input measurement data, which 
will be normalized into the CV ranging from 0 to 1. It can be clearly seen that the CV is inverse 
proportional to the MQE, meaning that worse structural health state generates a higher MQE and 
a lower CV.  
As discussed, in instead of failing suddenly, most mechanical system components usually 
undergo gradual degradation processes up until the time they fail, or scheduled to be replaced. If 
a component’s degrading state is continuously examined, a structural performance state indicator 
(CV) curve can be obtained as shown in Fig. 10.  
 
Fig. 10. Different structural performance states during the life cycle 
The CV ranges between 0 (unacceptable) and 1 (normal). Incremental changes in the CV are 
indicative of a gradual degradation process. No matter what type of degradation pattern the 
component goes through before it fails, the CV is always initially stable (in the range of 0.9 to 
1.0). When an incipient fault occurs, the CV will show a decreasing trend. After the initial fault 
propagates to a severe level, and the component is close to failure, the decrease in the CV becomes 
more obvious and quickly drops to 0. Once the CV drops below the predetermined threshold, we 
can judge that certain fault occurs. 
The SOM-based health assessment method can realize an online health assessment for the 
panel, using only the dataset under normal condition. The proposed method has no requirements 
on the full life cycle of structural performance degradation dataset.  
5. Experimental verification 
In this experiment, the power feature vectors of 60 groups of normal data were used to train a 
SOM for establishing the health assessment model. For model verification, 30 groups of normal 
data and 30 groups of faulty data were taken and sequentially imported into the trained SOM-based 
health assessment model. The SOM topology graph of the model training process is shown in 
Fig. 11, as well as the model testing one shown in Fig. 12. And then, the panel structural health 
assessment result is shown in Fig. 13. 
 
Fig. 11. SOM topology of model training process 
 
Fig. 12. SOM topology of model testing process 
SIGNAL PROCESSING AND HEALTH ASSESSMENT TECHNIQUES IN STRUCTURAL HEALTH MONITORING.  
YOUNING TANG, QIAN SUN, ZHENBANG LV, JINHAO QIU 
16 © JVE INTERNATIONAL LTD. VIBROENGINEERING PROCEDIA. NOVEMBER 2014. VOLUME 4. ISSN 2345-0533  
The structural performance degradation is clearly shown. An appropriate threshold should be 
pre-determined to indicate the degradation of structural health state, and it is set as 0.65 according 
to previous experience. The CVs of the first 30 samples, collected from the normal data, are highly 
above the predetermined failure threshold, which shows a stable health state. However, the other 
CVs from the faulty data are below the threshold. Thus, it is distinctly proved that the health 
assessment can be achieved based on SOM algorithm by using CV. 
 
Fig. 13. The health assessment result by CV of the test model 
In summary, it has been seen that the corresponding power features extracted from the panel 
PZT data are available for health monitoring. Moreover, the proposed SOM-based health 
assessment method has no dependencies on failure samples or structural performance degradation 
data for its model establishment, and could be more pragmatic for practical applications. 
6. Conclusions 
This paper proposed an aircraft structural health assessment scheme based on power 
characteristics of PZT signals. The HHT algorithm is employed to calculate the power 
characteristic vector, after that, the SOM is used to map the corresponding characteristics into a 
confidence value (CV) which represents the health state of the monitored structure. The 
experiment results demonstrate that the proposed method is reliable. 
The SHM technique, as a new concept that concerns the safety running of the aircraft, has been 
adopted by more and more research institutes. Although it is on the way of developing from a 
conceptual model to a prototype system, but the current situation is still far from practicability. 
Thus, further reach must be carried out on the theory of SHM, as well as the technique 
development and the project application.  
References 
[1] Zetao Xiong, Lei Qiu, Bin Liu The prognostic and health management of aircraft structure and its 
key technologies. Measurement and Control Technology, Vol. 31, 2012, p. 198-201. 
[2] Lei Qiu, Shenfang Yuan, Qiang Wang Research and application of an integrated health monitoring 
scanning system based on PZT sensor array. Measurement and Control Technology, Vol. 27, 2008, 
p. 218-222. 
[3] Dejie Yu, Junsheng Cheng, Yu Yang Hilbert-Huang Transform Method for Machinery Fault 
Diagnosis. Beijing, Science Press, 2006.  
[4] Cheng J. S., Yu D. J., Tang J. S., Yang Y. Application of SVM and SVD technique based on EMD 
to the fault diagnosis of the rotating machinery. Shock and Vibration, Vol. 16, 2009, p. 89-98. 
[5] Kumar K. P., Rao K. V. N. S., Krishna K. R., Theja B. Neural network based vibration analysis 
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Signal processing and health assessment techniques in structural health monitoring

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