An Intelligent Approach for Cooling Radiator Fault Diagnosis
Based on Infrared Thermal Image Processing
Amin Taheri-Garavanda,d*, Hojjat Ahmadia, Mahmoud Omida, Seyed Saeid Mohtasebia, Kaveh
Mollazadeb, Alan John Russell Smithc, Giovanni Maria Carlomagnod
a Department
of Mechanical Engineering of Agricultural Machinery, University of Tehran, Karaj, Iran
of Biosystems Engineering, University of Kurdistan, Sanandaj, Iran
c Department of Mechanical Engineering, University of Melbourne, Melbourne, Australia
dDepartment of Industrial Engineering, Naples University of Federico II, Naples, Italy
b Department
Abstract
This research presents a new intelligent fault diagnosis and condition monitoring system for classification
of different conditions of mechanical equipment that produces distinct thermal signatures for different
fault conditions. This will be illustrated by considering the classification of six types of cooling radiator
conditions: radiator tubes blockage, radiator fins blockage, loose connection between fins and tubes,
radiator door failure, coolant leakage, and normal conditions. The proposed system consists of several
distinct procedures including thermal image acquisition, image preprocessing, image processing, twodimensional discrete wavelet transform (2D-DWT), feature extraction, feature selection using a genetic
algorithm (GA), and finally classification by artificial neural networks (ANNs). The 2D-DWT is
implemented to decompose the thermal images. Subsequently, statistical texture features are extracted
from the original images and are decomposed into thermal images. The significant selected features are
used to enhance the performance of the designed ANN classifier for the 6 types of cooling radiator
conditions (output layer) in the next stage. For the tested system, the input layer consisted of 16 neurons
based on the feature selection operation. The best performance of ANN was obtained with a 16-6-6
topology. The classification results demonstrated that this system can be employed satisfactorily as an
intelligent condition monitoring and fault diagnosis for a class of cooling radiator.
Keywords: Cooling Radiator, Condition Monitoring, Thermal Images, Discrete Wavelet Transform, Genetic
Algorithm, Artificial Neural Networks.
Introduction
The radiator is a key component of an engine’s cooling system, playing an important role in
maintaining the operating temperature of the engine. The temperature of an internal combustion engine
can reach 2700°C (combustion gases) when it is operating at full throttle. Most engine component
materials are not be able to endure this temperature and would rapidly fail if they are not properly
cooled. Overheating of the engine can cause oil to thin, engine parts to expand, lubrication to break
down, and engine moving parts to be damaged. Therefore removing heat from an engine is
indispensable for the appropriate operation of engine. Most of the heat is removed by convection [1, 2]
to environmental air. The radiator is a kind of heat exchanger and important element in the cooling
system of vehicles. Its main purpose is moving the excessive heat from the engine block to the
surrounding air, which ensures reliable operation of the engine [3-5].
The importance of thermal studies of radiators arises principally from the acknowledged difficulty of
detecting the root cause of crack-induced leakage and other types of failures in radiators [6]. Condition
monitoring aims to prevent unplanned breakdowns, make the most of the plant availability and
decrease associated hazards. There are some non-destructive techniques that are often used for
condition monitoring such as vibration analysis, eddy-current testing, radiography, ultrasonic testing,
and acoustic emission[7].Temperature is one of the most useful parameters that indicates the structural
health of a machine. Hence, temperature monitoring of equipment or processes has been identified as
one of the best predictive maintenance methodologies [8].
Infrared radiation is emitted from the surface of any physical object with temperature above absolute
zero. The infrared (IR) energy is not visual since IR radiation is not in the visible range of the
electromagnetic spectrum for human and regular cameras. Infrared thermography is a technique used
for converting invisible heat energy into a visual thermal image that shows the thermal energy emitting
from the object surface. Based on this trait, thermography is currently applied to machine condition
monitoring and diagnosis fields where the temperature represents a key parameter [9].
IR thermography has been used for the nondestructive evaluation of joints[10].Kim et al.[11] used IR
thermography for fault diagnosis of ball bearings when rotational machinery had foreign material
inside the bearing sunder a dynamic loading condition. Lee and Kim [12] employed thermal imaging
for the early detection and condition monitoring of the leakage from the closure plug of heavy water
reactors during on-site inspections. They reported that the location of the leakages could be identified
and the leak status could be monitored in real-time with IR thermography. Ge et al. [13] inspected the
temperature distributions of air-cooled condensers and calculated the influence of ambient air
temperature, natural air flow, and surface defects on the performance of the units by IR thermography.
The thermography technique has evolved as a useful method for real-time temperature monitoring of
machines or processes in a non-contact and non-intrusive way for various condition monitoring
applications, which can decrease breakdowns or emergency shutdowns, maintenance costs and risk of
accidents, augment the performance and increase productivity. By applying modern image processing
methods to the acquired IR thermal images with artificial intelligence (AI) based approaches, better
decisions may be made rapidly without human intervention [8]. Younus and Yang [14] presented an
intelligent fault diagnosis system for classification of different rotary machine conditions that utilized
the processing of IR thermal images. They used a two-dimensional discrete wavelet transformation
(2D-DWT) to decompose the thermal image. In order to assist in diagnosing the different machine
conditions, they utilized support vector machines (SVMs) and linear discriminant analysis (LDA)
methods as classifiers.
Artificial neural networks (ANNs) are robust, adaptive and strong numerical models for pattern
recognition and classification [15]. ANNs are very powerful tools that can be trained to solve complex
non-linear classification problems. Huda and Taib [9] applied IR thermography for predictive and
preventive maintenance of thermal defects in electrical equipment. They utilized statistical features, a
multilayer perceptron (MLP) neural network, and a discriminant analysis classifier to allocate the
hotspot thermal status into ‘defect’ and ‘no defect’ categories. Abu-Mahfouz [16] used ANNs for the
detection and classification of malfunction, wear and damage of a gearbox operating under steady state
conditions. ANNs were applied for the damage indices classification of aerospace structures with the
use of Lamb waves [17].
Fault diagnosis of machinery can be handled as a task of pattern recognition and classification that
includes data acquisition, feature extraction, feature selection and final condition classification steps
which are the demandable tasks of fault diagnosis. To obtain correct data (normal or abnormal), it is
important to complete all steps of signal processing whatever the signal type such as vibration, thermal
image, current, ultrasonic, or acoustic. Moreover, fault pattern classification from images typically
consists of these steps: image acquisition, pre-processing, segmentation, feature extraction or
dimension reduction, feature selection, classification and decision [18, 19].In order to use condition
monitoring and fault detection of machines, signal processing techniques are initially required to
process the data acquired from the machinery. Wavelet transform is an early technique that has been
employed for one-dimensional signal processing. Recently, 2D-DWT is frequently considered as a
decomposition algorithm in the image processing field. 2D-DWT is a tool that is applied for analysis of
2D signals such as X–ray images, magnetic resonance images (MRI), synthetic aperture radar (SAR),
and RGB images [20].However, the data obtained from the decomposition process of a wavelet
transform are seldom practical because of the huge dimensionality which causes difficulties of data
storage and data mining for the next procedures. Representing data as features or dimensionality
reductions is a process of extracting the functional information from the dataset to remove artifacts and
reduce the dimensionality. However, it must protect the characteristic features which show faults and
conditions of the machinery as far as possible. Dimensionality reduction is an important data
preprocessing procedure for classification tasks and commonly falls into two categories: feature
compression and feature selection [14].
The engine cooling system and the radiator, in particular, as its main component to maintain the
temperature of engine are vitally important to the operation of an engine. A radiator that is defective
will cause the engine to be stopped or it will reduce engine performance. Thus fault diagnosis and
condition monitoring of a radiator is very important. In this paper, a thermography-based technique is
considered for fault detection and condition monitoring of radiators because temperature is a key
parameter in defining a radiator’s condition. Accordingly, the aim of this work is the development and
implementation of a new intelligent fault diagnosis and condition monitoring system for classification
of common faults occurring in a cooling radiator using IR thermal images. In the present study, the
intelligent condition monitoring system has a number of procedures that must be applied sequentially,
including: IR thermal image acquisition, preprocessing, image processing via2D-DWT, feature
extraction, feature selection, and classification. The 2D-DWT is applied to thermal image
decomposition. Consequently, statistical texture features are extracted from the original and
decomposed thermal images. In the next step, the significant features are selected based on a genetic
algorithm (GA) to enhance the performance of the ANN classifier in the final stage. Details of the
methodology adopted for collecting the infrared images, and for analyzing these are provided in the
next section.
2. Materials and Methods
2.1. Test setup and experimental procedure
To simulate faults in the cooling radiator a test apparatus, as shown inFig.1, was prepared. The setup
consists of a radiator, thermal infrared camera, cooling fan, flow meter, reservoir with heating
elements, pump, thermocouple with control circuit, velocity sensor, temperature sensors, a PC for
sensor data acquisition and another PC for image capture from the thermography camera with
analyzing software(See Figs. 1 and 2).More information about experimental setup is summarized in
Table 1.
Table 1. Details of experimental setup.
Thermography
camera
(ULIRvision
TI160)
Radiator
Fan
Water pump
Flow meter
Reservoir water
heating system
velocity sensor
temperature
sensor
Focal plane array (FPA) uncooled micro bolometer detector,
8-14 m spectral range
±2% accuracy
<65 mk at 30 C thermal Sensitivity
Emissivity correction Adjustable from 0.1 to 1.0
copper radiator for U.T.B. Diesel engine
suction fan with 3-phase electromotor that controlled by inverter
Centrifugal pump
Rotameter with Metering range 8-70 lit/min and ±2 lit/min accuracy
9 kW heating element heating elements fixed into the reservoir container with 100 liter
volume, water is heated up in the range of 50-120C
hot wire velocity sensor with ±2% accuracy
LM35 with ±1°C accuracy
Fig. 1 A schematic diagram of experimental setup for thermographic fault diagnosis of cooling radiator
Fig. 2 Experimental setup for thermal fault diagnosis and condition monitoring of cooling radiator
In this setup test, the water heating system acts as a supply of heat which simulates the operation of an
engine. The heating element heats up water to a temperature range of 50 OC -110OCin which a simple
algorithm is used to control and adjust the temperature of the coolant. After heating, hot coolant is
pumped by the centrifugal water pump into the radiator. The rotameter is installed between the pump
and radiator and measures the flow rate of the hot coolant. The rotameter's flow is controlled by
controlling bypass valves achieving different flow rates of the hot coolant. Then the inlet water
temperature to the radiator is sensed by a temperature sensor. The hot coolant flows through the
radiator core. The cold air is sucked in by a fan and decreases the temperature of the coolant flowing
through the radiator. The velocity of air is controlled with a 3-phase inverter electromotor. Then the
outlet coolant is returned to the reservoir where the heating element heats it up again and is recirculated
in the flow circuit to maintain the continuity of flow.
Six types of radiator condition including radiator tubes blockage (TB), radiator fins blockage (FB),
loose connections between Fins & tubes (LC), radiator door failure (DF), coolant leakage (CL) and
normal (N) conditions were investigated. Fault diagnosis experiments were conducted for these
conditions at three coolant temperatures (70, 80 and 90°C), three flow rates (40, 55 and 70 lit/min), and
two suction air velocities (2.0 and3 m/s).Figure 3 shows some of the acquired IR thermal images of the
radiator conditions.
Fig. 3. Sample of the acquired infrared thermal images of the six condition of radiator (A: radiator fins blockage,
B: loose connections between fins &tubes, C: coolant leakage, D: radiator door failure, e radiator tubes
blockage: and F: normal)
In order for the thermography camera to provide reliable temperature measurements, certain parameters
must be set. The most important of these parameters is the emissivity of the object; the other
parameters are scale temperature, relative humidity, focal length of camera, and distance. According to
the experimental conditions all of these parameters were adjusted accordingly.
2.2. Feature extraction
Wavelets, as mathematical functions, decompose data into different frequency components and then
analysis every component with a resolution matched to its scale (multi-resolution time-scale analysis).
Two dimensional discrete wavelet transform (2D-DWT) is a useful tool to image processing in a
multiscale representation structure and to capture details of localized image in space and frequency
domains together[21]. Mi and Lan [22]proposed Haar wavelet for image processing that has good
advantages for image analysis such as fast, simple processing, high ratio of image compression, good
de-noising effect and good image features to maintain the characteristics. In this study, the 2D-DWT
with Haar wavelet was used to decompose the original image. The results in each thermal images was
decomposed into a first level approximation component, and detailed components that include of
horizontal, vertical and diagonal details. Diagram of wavelet decomposed according to decomposition
algorithm of Gonzalez et al [20] is shown in Fig. 4.
Texture analysis is important in several applications of digital image analysis for classification,
detection or segmentation of images based on local spatial patterns of intensity or color. Texture
features play an important role in the classification at many machine vision applications such as
biomedical image analyzing, automated visual inspection, content based image retrieval, remote
sensing applications, etc. Most of the textural features are generally obtained from the application of a
local operator, statistical analysis, or measurement in a transformed domain like Wavelet [23, 24].
Wavelet transform attains consistently good performance and ranks among the best approaches.
Commonly, the result of wavelet transform cannot be used for character calculation, just statistics result
from the result of wavelet transform can be used to indicate the texture character. Accordingly, texture
features like mean, variance, moments, gradient vectors and energy density strings are extracted from
wavelet sub-images at wavelet decomposition achieve the efficient results [25].
Fig 4. Two dimensional discrete wavelet decomposition using filters bank for thermal image.
Wavelet transform attains consistently good performance and ranks among the best approaches.
Commonly, the result of wavelet transform cannot be used for character calculation, just statistics result
from the result of wavelet transform can be used to indicate the texture character. Accordingly, texture
features like mean, variance, moments, gradient vectors and energy density strings are extracted from
wavelet sub-images at wavelet decomposition achieve the efficient results [25].
Discrete wavelet transform was used for multiple resolutions image processing. The 2D-WTand first
decomposition level were applied in the decomposition of IR thermal image data from different
conditions of the radiator. By performing decomposition, four types of wavelet coefficients could be
obtained from each IR thermal image. Original thermal image and four kinds of wavelet
(approximation component, and detailed components that include horizontal, vertical and diagonal
details) were considered for feature extraction because they may be useful for radiator fault diagnosis.
The aim of feature extraction is to find a simple and effective transform signal or image to fault
diagnosis and condition monitoring.
An approach which is used frequently for feature extraction is based on statistical properties of the
intensity histogram [20].The histogram features are considered as the most basic feature extraction
method of texture analysis which present information in relation to the characteristic of the gray level
distribution for the image. Histogram on gray scale images is defined as follows:
�
=
� ��
�
(1)
where z is a random variable indicating intensity, H(zi) is image histogram, N is the number of all
pixels in a grey level image and P(zi) is the normalized histogram.
First order statistical features, and useful texture features of the image can be obtained from the
histogram mean which is the average value of the intensity. It gives some information about general
brightness of the image. The variance expresses the intensity variation around the mean and measures
average contrast, Smoothness measures the relative smoothness of the intensity in a region. Skewness
measures the asymmetry about the mean in the gray level distribution. The energy measures
uniformity, when all intensity values are equal (maximally uniform) its value is maximum and
decreases from there, in fact it represents about how the gray levels are distributed. The entropy is a
measure of randomness of the histogram. The following equations are the first order statistical features
of the image that were obtained using the histogram [20, 26]:
Mean:
Standard deviation:
Smoothness:
Skewness:
Uniformity (Energy):
Entropy:
= ∑�−
=
� = √∑�−
=
�
−
= 1 − 1⁄ 1 + �
= ∑�−
=
� = ∑�−
= �
(2)
−
� = − ∑�−
= P
�
�
(3)
(4)
(5)
(6)
P
(7)
Textural features were measured from thermal original image and sub-images of the wavelet
decomposition by histogram based features. Therefore, altogether 30 features were extracted for each
thermal image.
2.3. Feature Selection
After completing the feature extraction process the superior features were chosen from the extracted
feature vector. All features may not be relevant to the problem, and some of them might reduce
accuracy or cause over fitting. Thus in order to have a sufficient feature vector, features which do not
improve classification accuracy should be discarded from the feature vector [27]. Feature selection
aims to select a small subset of the relevant features from the original ones according to certain
relevance evaluation criterion, which commonly leads to higher learning accuracy for classification,
lower computational cost, and better model interpretability [28]. So, feature selection may be viewed as
an important pre-processing technique to remove irrelevant and redundant data. It can be applied in
both supervised and unsupervised learning methods. In supervised learning, feature selection aims to
maximize classification accuracy[29].Genetic algorithm (GA)is a biological approach for performing
subset selection and is based on the wrapper method but it needs a classifier such as ANN, SVM, KNN,
etc to evaluate each individual of the population [30, 31]. Samanta et al. [32] used GA and ANN as
feature selection and classifier for fault detection of bearing. Gamarra and Quintero [33] applied GA
feature selection in an ANN classification system for image pattern recognition.
The proposed approach is based on a hybrid system that uses both GA and ANN. GA was used to
select subsets of genes (features) and ANN classifiers were used to classify cases and returned a metric
of the error which was used as a fitness function for selected subset of genes. GAs are iterative
processes in which the best individuals of a population are selected to reproduce and pass to the next
generation. These steps stop after many generations, optimal or near optimal solutions are described as
follows:
1. The feature selection method starts with random generation of an initial population of
chromosomes.
2. The evaluation of the fitness function for an individual population begins with the application
of the chromosome’s mask. For each chromosome representing selected features, training
dataset is used to train the ANN classifier, while selected features of the testing dataset are used
to calculate classification accuracy. When the classification accuracy is obtained, each
chromosome is evaluated with cost function according to the following formula:
Cost function = 1 − overall classification accuracy
(8)
3. Parent selection: Select parent chromosomes from population (among the current population
and the generated children) according to their fitness.
4. Crossover: With a crossover probability, generate the parents to form new offspring, that is,
children. Crossover operator with some constraints has been implemented in order to maintain
acceptability of the solution. Crossover probability was set to 80%
5. Mutation: The generated children in the previous stage can suffer mutations in some of their
mask’s pixels with a probability of 5%.
6. Place new offspring in the new population. Use new generated population for a further run of
the algorithm if the end condition is satisfied, stop, and return the best solution in current
population. Otherwise the algorithm pass to the next generation, beginning at step 2.
So the feature mask for the best solution is determined as a string like “0010010110011011…”. Here,
values of “1” or “0” suggest that the feature is selected or removed, respectively.
Fig 5. Proposed framework for intelligent fault diagnosis and condition monitoring of cooling radiator
Six kinds of texture features (mean, standard deviation, smoothness, skewness, energy and entropy)
were extracted from all thermal images and wavelet data. So altogether, 30 features were extracted for
each sample. After completing feature extraction process, the size of data matrix was 1620*30*6 (1620
samples, 30 features and 6 classes). Therefore, in order to reduce dimensions, lower computational cost
and higher learning accuracy for classification, feature selection was performed by combining GA and
ANN techniques. The proposed framework for intelligent fault diagnosis and condition monitoring of
cooling radiator is shown in Fig. 5.
In order to do feature selection, a MATLAB program was developed to select the best features by GAs,
and then the radiator condition diagnosis classification was done by ANN classifier.
2.4. Intelligent classification and condition monitoring of the radiator
Classification is the final step in the condition monitoring process of the radiator. Moreover
classification is the process of training in order to assign a sample to pre-determined classes. The aim
of classification is to find a rule, based on selected features or training elements, that allows assigning
each thermal image of radiator to any possible classes. The classification process includes training,
cross-validation, and testing steps. Therefore the features data have to be divided into three subsets:
training set, cross-validation set, and testing set. The training set is used to train the ANN, while
crossvalidation set is used to prevent the overtraining and the testing set is assigned to test validity of
the classifier. Here, 60% of dataset was randomly selected as training set (972 samples), 20% for
crossvalidation (324 samples), and the remaining(20%of data set or 324samples) were used as testing
set. Multilayer perceptron (MLP) is one of the most popular ANN architectures which is based on
supervised learning algorithm and is used for classification. This network consists of input, hidden and
output layers. The input layer has nodes which represent the normalized extracted and selected features
from the measured thermal images. The number of input nodes was varied from 1 to 30 according to
selected feature by GAs. So the number of input nodes is equal to number of the selected features. The
number of nodes in the output layer corresponds to the number of target classes. As previously
described, there are six classes for condition monitoring of the radiator.
The MLP network was built, trained and implemented using MATLAB's neural network toolbox
(2013b). Levenberg-Marquardt (LM) algorithm was used as training algorithm. The ANN was
iteratively trained to minimize the performance function (MSE) between the ANN outputs and the
corresponding target data. The gradient of performance function of MSE was used to adjust the
network weights and biases in each iteration. In this study, a MSE of 10-4, a minimum gradient of 10-10,
a maximum validation number increase of 10, and maximum iteration number (epoch) of 1000 were
used as stopping criterion. The training process would stop if any of these conditions were met. The
initial weights and biases of the network were generated automatically by the program.
Table 2: confusion matrix for classification of six classes
C*1
n11
...
n16
...
n66
C*6
n61
...
C6
...
...
...
C1
The performance analysis of classification can be evaluated by the semi-global performance matrix,
known as the confusion matrix. This matrix contains information about actual and estimated
classification data obtained by the ANN classifier. Table 2 shows the confusion matrix for a six class
classifier which represents how the instances are distributed over actual (columns) and estimated
(rows) classes.
The terms (nij) correspond to the pixels that are classified into class number i by the ANN classifier (i.e.
C*i), when they actually belong to class number j (i.e. Cj). Accordingly, the right diagonal elements
(i=j) correspond to correctly classifiedinstances, while off-diagonal terms (i≠j) represent incorrectly
classified ones. When considering one class i in particular, one may distinguish four kinds of instances:
true positives (TP) and false positives (FP) are instances correctly and incorrectly identified as C *i,
whereas true negatives (TN) and false negatives (FN) are instances correctly and incorrectly rejected as
C*i, respectively.The corresponding counts are determined as �� = , , �� = ,+ − , , �� =
− �� − �� − �� where ,+ �
+, −
, and �� =
+, are the sums of the confusion matrix
elements over row i and column j, respectively (Labatut and Cheri, 2011).
The classification performances were measured based on the values of the confusion matrix, such as
percentage of specificity, sensitivity, precision, accuracy and area under the curve (AUC). The
following equations present them for classification:
�
�
�
� �
�� �
� � �
��� =
=�
��� +���
�� +��� +��� +���
���
=�
�� +���
=�
���
�
���
=�
�� +���
��� +���
+�
���
�� +���
���
�� +���
(9)
(10)
(11)
(12)
(13)
Accuracy focuses on overall effectiveness of ANN classifier. Precision evaluates class agreement of the
data labels with the positive labels defined by the classifier. Sensitivity shows the effectiveness of the
ANN classifier to recognize positive labels and how effectively a classifier identifies negative labels
and AUC is indicative ability of classifier to avoid false classification (Sokolova and Lapalme, 2009).
3. Results and Discussions
Thermal IR image acquisition from the different condition of radiator was done by thermal camera at
three coolant temperatures (70, 80 and 90°C),three flow rates(40, 55 and 70 lit/min) and two suction air
velocities (2.0 and3 m/s), respectively. Altogether, 1620 samples were obtained from IR thermal
images of all different conditions of the radiator and all experimental conditions. After gray scaling and
auto-cropping to eliminate the background, some of the acquired thermal images of different conditions
of radiator are presented in Fig.6.
When radiator fins are blocked in different areas, the air flow is severally restricted. So, irrespective of
the type of material that blocks the radiator's surface, the heat transfer rate in these zones is reduced.So
there will be hot spots in the thermal image in these areas (higher intensity in the gray level) compared
to the normal condition (compare Figs. 6A and 6F).Where there are loose connections between fins and
tubes in different areas, the fins do not participate in the heat transfer. Thus, there will be cold spots in
the thermal image in these areas (lower intensity of gray level) compared with normal condition (see
Fig. 6B). When the radiator has a leakage, that is a gradual loss of coolant, the coolant slightly flows
out; because of the higher heat transfer coefficient of water than air or may be due to evaporation of the
fluid. There will be cold spots in leakage areas (see Fig. 6C). Figure 6D shows the effects of radiator
door failure in the IR thermal image in which the temperatures of the top parts are hotter than in the
normal condition. The radiator tube blockage condition in different areas leads to clogged coolant flow
in these tubes. Therefore, these tubes do not participate in heat transfer. So there will be cold spots in
the thermal image in these regions compared with the normal condition (see Fig. 6E).The proposed
system is expected to be able to detect the difference between the IR thermal images for each condition
of the cooling radiator and also to classify and recognize the different conditions of radiator.
Fig. 6. Some of the IR thermal images of the six conditions of radiator after gray scale and auto-cropping (A:
radiator fins blockage, B: loose connections between fins &tubes, C: coolant leakage, D: radiator door failure, E:
radiator tubes blockage: and F: normal)
For feature selection, finding the best features manually is very time consuming so it is necessary to
build and investigate Q__ networks to check all the cases. The GA assisted in the selection of the best
features in a very short time and with checking 1020 networks only. Running the program took almost
30 min with a PC (core i5 CPU, TM4440 @ 3.10 GH).The result of program was a MLP network with
only one hidden layer that has 6 neurons. The final selected features were used as inputs of classifier by
optimizing accuracy of ANN's classifier. The selected features used as the inputs of ANN classifier are
shown in Table 3.
Table. 3. Final selected features, based on GA
Images/ Statistical texture features
Original Thermal Image
Wavelet Approximation Image
Wavelet horizontal Image
Wavelet vertical Image
Wavelet diagonal Image
Mean
1
0
1
0
1
Standard
Deviation
0
0
0
0
1
Smoothness Skewness
1
1
0
1
1
0
1
1
0
0
Energy
1
0
0
1
0
Entropy
0
1
1
1
1
The final and important stage for fault detection is classification. For the cooling radiator, after creation
the MLP network was trained by the LM back propagation training algorithm. Mean square error
(MSE) was used as the performance function and selected features were considered as the inputs of the
network. The trained network was implemented for classifying the test data. The topology of the ANN
is the main component in designing an optimal classifier, because structure impacts on the learning
ability and the accuracy of the final network in classifying data. The number of hidden layers and their
neurons are major factors for designing MLP networks. The number of neurons in the input and output
layers were fixed because they are dependent on the feature vector and the number of classes,
respectively. The input layer consisted of 16 nodes based on the feature selection operation (see Table
3). The output layer consisted of six neurons which were related to the six classes, i.e., radiator tube
blockage, radiator fin blockage, loose connection between fins &tubes, radiator door failure, coolant
leakage and normal, for fault diagnosis and condition monitoring of the radiator. The number of hidden
layers and their neurons depend on the difficulty of the investigated problem. Generally, one hidden
layer with a lower number of neurons is preferred, because it leads to a reduction in the network size
and an increase in the network’s learning ability. This matter is very important for online condition
monitoring. Several combinations of the number of neurons in the hidden layer varying from 2 to 15
and the number of epochs varying from100 to 1000were investigated by a trial and error method. To
find the best combination, the total classification accuracy was used as the selection criterion. The
results showed that the hidden layer with six neurons (i.e., a 16-6-6 topology) had the smallest size with
the highest total classification accuracy. Thus, the 16-6-6 network was selected as the best topology for
fault diagnosis and condition monitoring of the cooling radiator. This MLP network as the radiator
classifier is shown in Fig. 7.
Table 4 shows the confusion matrix as a result of ANN with primary feature vector inputs (without any
feature selection) using experimental data. Table 5 gives the performance parameters of the classifier
according to the above-mentioned confusion matrix, including classification accuracy, precision,
sensitivity, specificity and AUC for radiator tube blockage (TB), radiator fin blockage (FB), loose
connection between fins &tubes (LC), radiator door failure (DF), coolant leakage (CL) and normal (N)
classes. The classification accuracy of the ANN classifier for FB, LC, CL, DF, TB and N classes were
95.99%, 98.45, 94.13, 91.66, 99.38 and 97.53%, respectively. The overall accuracy of the ANN
classifier obtained was 88.58 %. The average per class accuracy, precision, sensitivity, specificity,
AUC were 96.62%, 87.38.68, 87.47, 97.77 and 92.62%, respectively.
Tabele.4. Confusion matrix obtained from the evaluation of 30-6-6 ANN classifier (without feature selection).
Estimated/
Actual
FB
LC
CL
DF
TB
N
FB
LC
CL
DF
TB
N
57
0
3
4
0
0
0
58
1
2
0
0
1
0
38
4
0
0
3
2
7
28
0
3
0
0
0
2
58
0
2
0
3
0
0
48
Table.5.Performance measurements of 30-6-6 ANN classifier (without feature selection)
Class
Accuracy
Precision Sensitivity
Specificity
AUC
FB
95.99
90.47
89.06
97.69
93.37
LC
98.45
96.66
95.08
99.23
97.16
CL
94.13
73.08
88.37
95.01
91.69
DF
91.66
70
66.17
95.73
80.42
TB
99.38
100
96.66
100
98.33
N
97.53
94.11
90.56
98.9
94.73
Average per-class
96.62
87.38
87.47
97.77
92.62
Table 6 shows the confusion matrix as a result of the ANN classifier with the 16-6-6 topology using
selected features as inputs (Table 3, feature selection based on GA) for the experimental dataset. Table
7 gives the performance measurements of the classifier according to the confusion matrix (Table 6),
including classification accuracy, precision, sensitivity, specificity and AUC for all classes. The
classification accuracy of the ANN classifier for FB, LC, CL, DF, TB and N classes were 99.07%,
99.07, 95.37, 95.37, 99.38 and 98.76%, respectively. The overall accuracy of the optimum ANN
classifier was obtained as 93.83%. The average per class of accuracy, precision, sensitivity, specificity,
AUC were 97.84%, 92.35, 92.93, 98.75 and 95.66%, respectively.
Tabele.6. Confusion matrix obtained from the evaluation of 16-6-6 ANN classifier (with GA feature selection).
Estimated/
FB
LC
CL
DF
TB
N
Actual
FB
0
1
0
0
0
60
LC
0
1
0
0
0
60
CL
0
0
9
0
1
36
DF
0
2
3
1
0
34
TB
0
0
0
0
0
66
N
2
0
0
0
1
48
Table.7.Performance measures of 30-6-6 ANN classifier (with GA feature selection).
Class
Accuracy
Precision Sensitivity
Specificity
AUC
FB
99.07
98.36
96.77
99.62
98.2
LC
99.07
98.36
96.77
99.62
98.2
CL
95.37
78.26
87.8
96.47
92.14
DF
95.37
85
79.07
97.86
88.47
TB
99.38
100
97.06
100
98.53
N
98.76
94.12
97.96
98.91
98.43
Average per-class
97.84
92.35
92.93
98.75
95.66
Comparing the results of for the two classifiers (classifying using primary feature vector and selected
feature vector via GA) indicted significant improvement of classification performance using the GA
feature selection. The GA feature selection method selected a small subset of the relevant features from
the original ones according to certain relevant evaluation criterion. This led to a higher classification
performance, lower computational cost and better model interpretability. Figure 8 shows the changes in
MSE with changing epoch number when MLP was used for determining the selected feature inputs,
over a training period of 203 epochs. The best performance of the ANN is seen at epoch 193 for MLP
with16-6-6 topology.
Fig 7. The best ANN model with 16-6-6 topology for fault diagnosis of cooling radiator.
Fig 8. Changes of MSE related to epoch numbers when MLP for selected feature inputs, is trained with 203
epochs
Therefore, the proposed intelligent system could classify and recognize IR thermal images for the
different conditions of radiator with high accuracy. This provides confidence that the system can be
employed for the intelligent condition monitoring and fault diagnosis of a cooling radiator.
4. Conclusions
This paper has presented a useful application of thermography for intelligent fault diagnosis and
condition monitoring, and applied it to a cooling radiator. In general, the application of intelligent
condition monitoring and fault diagnosis to detect fault types precisely is very complex and difficult,
but by combining image processing, genetic algorithm (GA) and artificial neural network (ANN)
techniques provides both diagnosis efficiency and accuracy gains.
In this study, a new intelligent diagnosis system has been developed and applied to the classification of
six types of cooling radiator conditions via using of infrared thermal images; namely, radiator tube
blockage, radiator fin blockage, loose connections between fins and tubes, radiator door failure, coolant
leakage and normal. The proposed system consisted of several subsequent procedures including
thermal image acquisition, preprocessing, image processing via two dimensional discrete wavelet
transform (2D-DWT), feature extraction, feature selection, and classification. The 2D-DWT was
implemented to decompose the thermal images. Subsequently, statistical texture features were
extracted from the original and decomposed thermal images. The feature selection based on GA was
used in selecting significant features in order to enhance the performance of the ANN in the final stage.
The classification results demonstrated that this system could be employed for the intelligent condition
monitoring and fault diagnosis of mechanical equipment that has a strong thermal signature indicating
its operating state. Since there is some initial training of the system, it is better suited to the ongoing
and periodic maintenance of multiple pieces of similar equipment. In such cases, there should be a
significant positive return on the investment.
Nomenclature
Cj
C*i
Actually Classes
Classifier Classes
FP
FN
False Positives
False Negatives
TP
TN
H(zi)
p(zi)
z
N
True Positives
True Negatives
Image Histogram
Normalized Histogram
Random variable of intensity
Number of all pixels
m
S
U
E
Mean
Standard deviation
Skewness
Uniformity
Entropy
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