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Applied Thermal Engineering 87 (2015) 434e443

Contents lists avai

Applied Thermal Engineering

journal homepage: www.elsevier .com/locate/apthermeng

Research paper

An intelligent approach for cooling radiator fault diagnosis based oninfrared thermal image processing technique

Amin Taheri-Garavand a, d, *, Hojjat Ahmadi a, Mahmoud Omid a, Seyed Saeid Mohtasebi a,Kaveh Mollazade b, Alan John Russell Smith c, Giovanni Maria Carlomagno d

a Department of Mechanical Engineering of Agricultural Machinery, University of Tehran, Karaj, Iranb Department of Biosystems Engineering, University of Kurdistan, Sanandaj, Iranc Department of Mechanical Engineering, University of Melbourne, Melbourne, Australiad Department of Industrial Engineering-Aerospace Division, University of Naples Federico II, Naples, Italy

h i g h l i g h t s

� Intelligent fault diagnosis of cooling radiator using thermal image processing.� Thermal image processing in a multiscale representation structure by 2D-DWT.� Selection features based on a hybrid system that uses both GA and ANN.� Application of ANN as classifier.� Classification accuracy of fault detection up to 93.83%.

a r t i c l e i n f o

Article history:Received 13 March 2015Accepted 16 May 2015Available online

Keywords:Cooling radiatorCondition monitoringThermal imagesDiscrete wavelet transformGenetic algorithmArtificial neural network

* Corresponding author. Department of MechanicaMachinery, University of Tehran, Karaj, Iran.

E-mail addresses: amintaheri@ut.ac.ir, amin.taheGaravand).

http://dx.doi.org/10.1016/j.applthermaleng.2015.05.031359-4311/© 2015 Elsevier Ltd. All rights reserved.

a b s t r a c t

This research presents a new intelligent fault diagnosis and condition monitoring system for classifica-tion of different conditions of cooling radiator using infrared thermal images. The systemwas adopted toclassify six types of cooling radiator faults; radiator tubes blockage, radiator fins blockage, looseconnection between fins and tubes, radiator door failure, coolant leakage, and normal conditions. Theproposed system consists of several distinct procedures including thermal image acquisition, image pre-processing, image processing, two-dimensional 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 texturefeatures are extracted from the original images and are decomposed into thermal images. The significantselected features are used to enhance the performance of the designed ANN classifier for the 6 types ofcooling radiator conditions (output layer) in the next stage. For the tested system, the input layer con-sisted of 16 neurons based on the feature selection operation. The best performance of ANN was obtainedwith a 16-6-6 topology. The classification results demonstrated that this system can be employedsatisfactorily as an intelligent condition monitoring and fault diagnosis for a class of cooling radiator.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

The radiator is a key component of an engine's cooling system,playing an important role in maintaining the operating tempera-ture of the engine. The temperature of an internal combustion

l Engineering of Agricultural

ri49@gmail.com (A. Taheri-

8

engine can reach 2700 �C (combustion gases) when it is operatingat full throttle. Most engine component materials are not be able toendure this temperature and would rapidly fail if they are notproperly cooled. Overheating of the engine can cause oil to thin,engine parts to expand, lubrication to break down, and enginemoving parts to be damaged. Therefore removing heat from anengine is indispensable for the appropriate operation of engine.Most of the heat is removed by convection [1,2] to environmentalair. The radiator is a kind of heat exchanger and important elementin the cooling system of vehicles. Its main purpose is moving the

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443 435

excessive heat from the engine block to the surrounding air, whichensures reliable operation of the engine [3e5].

The importance of thermal studies of radiators arises principallyfrom the acknowledged difficulty of detecting the root cause ofcrack-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 associatedhazards. There are some non-destructive techniques that are oftenused for condition monitoring such as vibration analysis, eddy-current testing, radiography, ultrasonic testing, and acousticemission [7]. Temperature is one of themost useful parameters thatindicates the structural health of a machine. Hence, temperaturemonitoring of equipment or processes has been identified as one ofthe best predictive maintenance methodologies [8].

Infrared radiation is emitted from the surface of any physicalobject with temperature above absolute zero. The infrared (IR)energy is not visual since IR radiation is not in the visible range ofthe electromagnetic spectrum for human and regular cameras.Infrared thermography is a technique used for converting invisibleheat energy into a visual thermal image that shows the thermalenergy emitting from the object surface. Based on this trait, ther-mography is currently applied to machine condition monitoringand diagnosis fields where the temperature represents a keyparameter [9].

IR thermography has been used for the nondestructive evalua-tion of joints [10]. Kim et al. [11] used IR thermography for faultdiagnosis of ball bearings when rotational machinery had foreignmaterial inside the bearing sunder a dynamic loading condition. Leeand Kim [12] employed thermal imaging for the early detection andcondition monitoring of the leakage from the closure plug of heavywater reactors during on-site inspections. They reported that thelocation of the leakages could be identified and the leak status couldbe monitored in real-time with IR thermography. Ge et al. [13]inspected the temperature distributions of air-cooled condensersand calculated the influence of ambient air temperature, natural airflow, and surface defects on the performance of the units by IRthermography. The thermography technique has evolved as a usefulmethod for real-time temperature monitoring of machines or pro-cesses in a non-contact and non-intrusiveway for various conditionmonitoring applications, which can decrease breakdowns oremergency shutdowns, maintenance costs and risk of accidents,augment the performance and increase productivity. By applyingmodern image processing methods to the acquired IR thermal im-ages with artificial intelligence (AI) based approaches, better de-cisions may be made rapidly without human intervention [8].Younus and Yang [14] presented an intelligent fault diagnosis sys-tem for classification of different rotary machine conditions thatutilized the processing of IR thermal images. They used a two-dimensional discrete wavelet transformation (2D-DWT) to decom-pose the thermal image. In order to assist in diagnosing the differentmachine conditions, they utilized support vector machines (SVMs)and linear discriminant analysis (LDA) methods as classifiers.

Artificial neural networks (ANNs) are robust, adaptive andstrong numerical models for pattern recognition and classification[15]. ANNs are very powerful tools that can be trained to solvecomplex non-linear classification problems. Huda and Taib [9]applied IR thermography for predictive and preventive mainte-nance of thermal defects in electrical equipment. They utilizedstatistical features, a multilayer perceptron (MLP) neural network,and a discriminant analysis classifier to allocate the hotspot ther-mal status into ‘defect’ and ‘no defect’ categories. Abu-Mahfouz [16]used ANNs for the detection and classification of malfunction, wearand damage of a gearbox operating under steady state conditions.ANNs were applied for the damage indices classification of aero-space structures with the use of Lamb waves [17].

Fault diagnosis of machinery can be handled as a task of patternrecognition and classification that includes data acquisition, featureextraction, feature selection and final condition classification stepswhich are the demandable tasks of fault diagnosis. To obtain correctdata (normal or abnormal), it is important to complete all steps ofsignal processing whatever the signal type such as vibration,thermal image, current, ultrasonic, or acoustic. Moreover, faultpattern classification from images typically consists of these steps:image acquisition, pre-processing, segmentation, feature extractionor dimension reduction, feature selection, classification and deci-sion [18,19]. In order to use condition monitoring and fault detec-tion of machines, signal processing techniques are initially requiredto process the data acquired from the machinery. Wavelet trans-form is an early technique that has been employed for one-dimensional signal processing. Recently, 2D-DWT is frequentlyconsidered as a decomposition algorithm in the image processingfield. 2D-DWT is a tool that is applied for analysis of 2D signals suchas X-ray images, magnetic resonance images (MRI), syntheticaperture radar (SAR), and RGB images [20]. However, the data ob-tained from the decomposition process of a wavelet transform areseldom practical because of the huge dimensionality which causesdifficulties of data storage and data mining for the next procedures.Representing data as features or dimensionality reductions is aprocess of extracting the functional information from the dataset toremove artifacts and reduce the dimensionality. However, it mustprotect the characteristic features which show faults and condi-tions of the machinery as far as possible. Dimensionality reductionis an important data preprocessing procedure for classificationtasks and commonly falls into two categories: feature compressionand feature selection [14].

The engine cooling system and the radiator, in particular, as itsmain component to maintain the temperature of engine are vitallyimportant to the operation of an engine. A radiator that is defectivewill cause the engine to be stopped or it will reduce engine per-formance. Thus fault diagnosis and condition monitoring of aradiator is very important. In this paper, a thermography-basedtechnique is considered for fault detection and condition moni-toring of radiators because temperature is a key parameter indefining a radiator's condition. Accordingly, the aim of this work isthe development and implementation of a new intelligent faultdiagnosis and condition monitoring system for classification ofcommon faults occurring in a cooling radiator using IR thermalimages. In the present study, the intelligent condition monitoringsystem has a number of procedures that must be applied sequen-tially, including: IR thermal image acquisition, preprocessing, im-age processing via 2D-DWT, feature extraction, feature selection,and classification. The 2D-DWT is applied to thermal imagedecomposition. Consequently, statistical texture features areextracted from the original and decomposed thermal images. In thenext step, the significant features are selected based on a geneticalgorithm (GA) to enhance the performance of the ANN classifier inthe final stage. Details of the methodology adopted for collectingthe infrared images, and for analyzing these are provided in thenext section.

2. Materials and methods

2.1. Test setup and experimental procedure

To simulate faults in the cooling radiator a test apparatus, asshown in Fig. 1, was prepared. The setup consists of a radiator,thermal infrared camera, cooling fan, flow meter, reservoir withheating elements, pump, thermocouple with control circuit, ve-locity sensor, temperature sensors, a PC for sensor data acquisitionand another PC for image capture from the thermography camera

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.

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443436

with analyzing software (see Figs. 1 and 2). More information aboutexperimental setup is summarized in Table 1.

In this setup test, the water heating system acts as a supply ofheat which simulates the operation of an engine. The heatingelement heats up water to a temperature range of 50e110 �C inwhich a simple algorithm is used to control and adjust the tem-perature of the coolant. After heating, hot coolant is pumped by thecentrifugal water pump into the radiator. The rotameter is installedbetween the pump and radiator and measures the flow rate of thehot coolant. The rotameter's flow is controlled by controlling

Table 1Details of experimental setup.

Thermography camera(ULIRvision TI160)

Focal plane array (FPA) uncooled microbolometer detector,8e14 mm spectral range±2% accuracy<65 mk at 30 �C thermal sensitivityEmissivity correction adjustable from 0.1 to 1.0

Radiator Copper radiator for U.T.B. diesel engineFan Suction fan with 3-phase electromotor that

controlled by inverterWater pump Centrifugal pumpFlow meter Rotameter with metering range 8e70 l/min

and ±2 l/min accuracyReservoir water

heating system9 kW heating element heating elements fixedinto the reservoir container with 100 l volume,water is heated up in the range of 50e120 �C

Velocity sensor Hot wire velocity sensor with ±2% accuracyTemperature sensor LM35 with ±1 �C accuracy

bypass valves achieving different flow rates of the hot coolant. Thenthe inlet water temperature to the radiator is sensed by a temper-ature sensor. The hot coolant flows through the radiator core. Thecold air is sucked in by a fan and decreases the temperature of thecoolant flowing through the radiator. The velocity of air iscontrolled with a 3-phase inverter electromotor. Then the outletcoolant is returned to the reservoir where the heating elementheats it up again and is re-circulated in the flow circuit to maintainthe 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) andnormal (N) conditions were investigated. Fault diagnosis experi-ments were conducted for these conditions at three coolant tem-peratures (70, 80 and 90 �C), three flow rates (40, 55 and 70 l/min),and two suction air velocities (2.0 and 3 m/s). Fig. 3 shows some ofthe acquired IR thermal images of the radiator conditions.

In order for the thermography camera to provide reliable tem-perature measurements, certain parameters must be set. The mostimportant of these parameters is the emissivity of the object; theother parameters are scale temperature, relative humidity, focallength of camera, and distance. According to the experimentalconditions all of these parameters were adjusted accordingly.

2.2. Feature extraction

Wavelets, as mathematical functions, decompose data intodifferent frequency components and then analysis every

Fig. 3. Sample of the acquired infrared thermal images of the six condition of radiator (A: radiator fins blockage, B: loose connections between fins and tubes, C: coolant leakage, D:radiator door failure, E: radiator tubes blockage, and F: normal).

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443 437

component with a resolution matched to its scale (multi-resolutiontime-scale analysis). Two dimensional discrete wavelet transform(2D-DWT) is a useful tool to image processing in a multiscalerepresentation structure and to capture details of localized image inspace and frequency domains together [21]. Mi and Lan [22] pro-posed Haar wavelet for image processing that has good advantagesfor image analysis such as fast, simple processing, high ratio ofimage compression, good de-noising effect and good image fea-tures to maintain the characteristics. In this study, the 2D-DWTwith Haar wavelet was used to decompose the original image. Theresults in each thermal images were decomposed into a first levelapproximation component, and detailed components that includeof horizontal, vertical and diagonal details. Diagram of waveletdecomposed according to decomposition algorithm of Gonzalezet al. [20] is shown in Fig. 4.

Fig. 4. Two dimensional discrete wavelet decomp

Texture analysis is important in several applications of digitalimage analysis for classification, detection or segmentation of im-ages based on local spatial patterns of intensity or color. Texturefeatures play an important role in the classification at many ma-chine vision applications such as biomedical image analyzing,automated visual inspection, content based image retrieval, remotesensing applications, etc. Most of the textural features are generallyobtained from the application of a local operator, statistical anal-ysis, or measurement in a transformed domain like Wavelet[23,24].

Wavelet transform attains consistently good performance andranks among the best approaches. Commonly, the result of wavelettransform cannot be used for character calculation, just statisticsresult from the result of wavelet transform can be used to indicatethe texture character. Accordingly, texture features like mean,

osition using filters bank for thermal image.

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443438

variance, moments, gradient vectors and energy density strings areextracted from wavelet sub-images at wavelet decompositionachieve the efficient results [25].

Discrete wavelet transform was used for multiple resolutionsimage processing. The 2D-WT and first decomposition level wereapplied in the decomposition of IR thermal image data fromdifferent conditions of the radiator. By performing decomposition,four types of wavelet coefficients could be obtained from each IRthermal image. Original thermal image and four kinds of wavelet(approximation component, and detailed components that includehorizontal, vertical and diagonal details) were considered forfeature extraction because they may be useful for radiator faultdiagnosis.

The aim of feature extraction is to find a simple and effectivetransform signal or image to fault diagnosis and conditionmonitoring.

An approach which is used frequently for feature extraction isbased on statistical properties of the intensity histogram [20]. Thehistogram features are considered as the most basic featureextraction method of texture analysis which present information inrelation to the characteristic of the gray level distribution for theimage. Histogram on gray scale images is defined as follows:

PðziÞ ¼HðziÞN

(1)

where z is a random variable indicating intensity, H(zi) is imagehistogram, N is the number of all pixels in a gray level image andP(zi) is the normalized histogram.

First order statistical features, and useful texture features of theimage can be obtained from the histogram mean which is theaverage value of the intensity. It gives some information aboutgeneral brightness of the image. The variance expresses the in-tensity variation around the mean and measures average contrast.Smoothness measures the relative smoothness of the intensity in aregion. Skewness measures the asymmetry about the mean in thegray level distribution. The energy measures uniformity, when allintensity values are equal (maximally uniform) its value ismaximum and decreases from there, in fact it represents about howthe gray levels are distributed. The entropy is a measure ofrandomness of the histogram. The following equations are the firstorder statistical features of the image that were obtained using thehistogram [20,26]:

Mean : m ¼XL�1

i¼0

ziPðziÞ (2)

Standard deviation : s ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXL�1

i¼0

ðzi �mÞ2PðziÞvuut (3)

Smoothness : R ¼ 1� 1.�

1þ s2�

(4)

Skewness : S ¼XL�1

i¼0

ðzi �mÞ3PðziÞ (5)

Uniformity ðEnergyÞ: U ¼XL�1

i¼0

P2ðziÞ (6)

Entropy : E ¼ �XL�1

i¼0

PðziÞlog2 PðziÞ (7)

Textural features were measured from thermal original imageand sub-images of the wavelet decomposition by histogram basedfeatures. Therefore, altogether 30 features were extracted for eachthermal image.

2.3. Feature selection

After completing the feature extraction process the superiorfeatures were chosen from the extracted feature vector. All featuresmay not be relevant to the problem, and some of them mightreduce accuracy or cause over fitting. Thus in order to have a suf-ficient feature vector, features which do not improve classificationaccuracy should be discarded from the feature vector [27]. Featureselection aims to select a small subset of the relevant features fromthe original ones according to certain relevance evaluation crite-rion, which commonly leads to higher learning accuracy for clas-sification, lower computational cost, and better modelinterpretability [28]. So, feature selection may be viewed as animportant pre-processing technique to remove irrelevant andredundant data. It can be applied in both supervised and unsu-pervised learning methods. In supervised learning, feature selec-tion aims to maximize classification accuracy [29]. Geneticalgorithm (GA) is a biological approach for performing subset se-lection and is based on the wrapper method but it needs a classifiersuch as ANN, SVM, KNN, etc to evaluate each individual of thepopulation [30,31]. Samanta et al. [32] used GA and ANN as featureselection and classifier for fault detection of bearing. Gamarra andQuintero [33] applied GA feature selection in an ANN classificationsystem for image pattern recognition.

The proposed approach is based on a hybrid system that usesboth GA and ANN. GAwas used to select subsets of genes (features)and ANN classifiers were used to classify cases and returned ametric of the error which was used as a fitness function for selectedsubset of genes. GAs are iterative processes in which the best in-dividuals of a population are selected to reproduce and pass to thenext generation. These steps stop after many generations, optimalor near optimal solutions are described as follows:

1. The feature selection method starts with random generation ofan initial population of chromosomes.

2. The evaluation of the fitness function for an individual popula-tion begins with the application of the chromosome's mask. Foreach chromosome representing selected features, trainingdataset is used to train the ANN classifier, while selected fea-tures of the testing dataset are used to calculate classificationaccuracy. When the classification accuracy is obtained, eachchromosome is evaluated with cost function according to thefollowing formula:

Cost function ¼ 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 toform new offspring, that is, children. Crossover operator withsome constraints has been implemented in order to maintainacceptability of the solution. Crossover probability was set to80%

5. Mutation: The generated children in the previous stage cansuffer mutations in some of their mask's pixels with a proba-bility of 5%.

6. Place new offspring in the new population. Use new generatedpopulation for a further run of the algorithm if the end condition

Fig. 5. Proposed framework for intelligent fault diagnosis and condition monitoring ofcooling radiator.

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443 439

is satisfied, stop, and return the best solution in current popu-lation. Otherwise the algorithm is passed to the next generation,beginning at step 2.

So the feature mask for the best solution is determined as astring like “0010010110011011…”. Here, values of “1” or “0” suggestthat the feature is selected or removed, respectively.

Six kinds of texture features (mean, standard deviation,smoothness, skewness, energy and entropy) were extracted fromall thermal images and wavelet data. So altogether, 30 featureswere extracted for each sample. After completing feature extractionprocess, the size of data matrix was 1620 * 30 * 6 (1620 samples, 30features and 6 classes). Therefore, in order to reduce dimensions,lower computational cost and higher learning accuracy for classi-fication, feature selection was performed by combining GA andANN techniques. The proposed framework for intelligent faultdiagnosis and condition monitoring of cooling radiator is shown inFig. 5.

In order to do feature selection, a MATLAB program was devel-oped to select the best features by GAs, and then the radiatorcondition diagnosis classification was done by ANN classifier.

2.4. Intelligent classification and condition monitoring of theradiator

Classification is the final step in the condition monitoring pro-cess of the radiator. Moreover classification is the process oftraining in order to assign a sample to pre-determined classes. Theaim of classification is to find a rule, based on selected features ortraining elements, that allows assigning each thermal image ofradiator to any possible classes. The classification process includestraining, cross-validation, and testing steps. Therefore the featuresdata have to be divided into three subsets: training set, cross-validation set, and testing set. The training set is used to train theANN, while cross-validation set is used to prevent the overtrainingand 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 cross-validation (324 samples), and the remaining (20%ofdataset or 324 samples) were used as testing set.

Multilayer perceptron (MLP) is one of the most popular ANNarchitectures which is based on supervised learning algorithm andis used for classification. This network consists of input, hidden andoutput layers. The input layer has nodes which represent thenormalized extracted and selected features from the measuredthermal images. The number of input nodes was varied from 1 to 30according to selected feature by GAs. So the number of input nodesis equal to number of the selected features. The number of nodes inthe output layer corresponds to the number of target classes. Aspreviously described, there are six classes for condition monitoringof the radiator.

The MLP network was built, trained and implemented usingMATLAB's neural network toolbox (2013b). LevenbergeMarquardt(LM) algorithm was used as training algorithm. The ANN was iter-atively trained to minimize the performance function (MSE) be-tween the ANN outputs and the corresponding target data. Thegradient of performance function of MSE was used to adjust thenetworkweights and biases in each iteration. In this study, a MSE of10�4, a minimum gradient of 10�10, a maximum validation numberincrease of 10, and maximum iteration number (epoch) of 1000were used as stopping criterion. The training process would stop ifany of these conditions were met. The initial weights and biases ofthe network were generated automatically by the program.

The performance analysis of classification can be evaluated bythe semi-global performance matrix, known as the confusion ma-trix. This matrix contains information about actual and estimated

classification data obtained by the ANN classifier. Table 2 shows theconfusion matrix for a six class classifier which represents how theinstances are distributed over actual (columns) and estimated(rows) classes.

The terms (nij) correspond to the pixels that are classified intoclass number i by the ANN classifier (i.e. C*

i ), when they actuallybelong to class number j (i.e. Cj). Accordingly, the right diagonal el-ements (i ¼ j) correspond to correctly classified instances, while off-diagonal terms (i s j) represent incorrectly classified ones. Whenconsideringone class i inparticular, onemaydistinguish fourkinds ofinstances: true positives (TP) and false positives (FP) are instancescorrectlyand incorrectly identifiedasC*

i ,whereas truenegatives (TN)and false negatives (FN) are instances correctly and incorrectlyrejected as C*

i , respectively. The corresponding counts are deter-mined as nTP ¼ ni;i, nFP ¼ ni;þ � ni;i, nFN ¼ nþ;j � ni;i andnTN ¼ n� nTP � nFP � nFN where ni;þ and nþ;j are the sums of theconfusionmatrix elements over row i and column j, respectively [34].

The classification performances were measured based on thevalues of the confusion matrix, such as percentage of specificity,sensitivity, precision, accuracy and area under the curve (AUC). Thefollowing equations present them for classification:

Accuracy ¼ nTP þ nTNnTP þ nTN þ nFP þ nFN

(9)

Table 2Confusion matrix for classification of six classes.

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443440

Precision ¼ nTPnTP þ nFP

(10)

Sensitivity ðRecallÞ ¼ nTPnTP þ nFN

(11)

Specificity ¼ nTNnTN þ nFP

(12)

AUC ¼ 12

�nTP

nTP þ nFNþ nTNnTN þ nFP

�(13)

Accuracy focuses on overall effectiveness of ANN classifier.Precision evaluates class agreement of the data labels with thepositive labels defined by the classifier. Sensitivity shows theeffectiveness of the ANN classifier to recognize positive labels andhow effectively a classifier identifies negative labels and AUC isindicative ability of classifier to avoid false classification [35].

Fig. 6. Some of the IR thermal images of the six conditions of radiator after gray scale and aucoolant leakage, D: radiator door failure, E: radiator tubes blockage, and F: normal).

3. Results and discussions

Thermal IR image acquisition from the different condition ofradiator was done by thermal camera at three coolant temperatures(70, 80 and 90 �C), three flow rates (40, 55 and 70 l/min) and twosuction air velocities (2.0 and 3 m/s), respectively. Altogether, 1620samples were obtained from IR thermal images of all differentconditions of the radiator and all experimental conditions. Aftergray scaling and auto-cropping to eliminate the background, someof the acquired thermal images of different conditions of radiatorare presented in Fig. 6.

When radiator fins are blocked in different areas, the air flow isseverally restricted. So, irrespective of the type of material thatblocks the radiator's surface, the heat transfer rate in these zones isreduced. So there will be hot spots in the thermal image in theseareas (higher intensity in the gray level) compared to the normalcondition (compare Fig. 6A and F). Where there are loose connec-tions between fins and tubes in different areas, the fins do notparticipate in the heat transfer. Thus, there will be cold spots in thethermal image in these areas (lower intensity of gray level)compared with normal condition (see Fig. 6B). When the radiatorhas a leakage, that is a gradual loss of coolant, the coolant slightlyflows out; because of the higher heat transfer coefficient of waterthan air or may be due to evaporation of the fluid. Therewill be coldspots in leakage areas (see Fig. 6C). Fig. 6D shows the effects ofradiator door failure in the IR thermal image in which the tem-peratures of the top parts are hotter than in the normal condition.The radiator tube blockage condition in different areas leads toclogged coolant flow in these tubes. Therefore, these tubes do notparticipate in heat transfer. So there will be cold spots in thethermal image in these regions compared with the normal condi-tion (see Fig. 6E). The proposed system is expected to be able todetect the difference between the IR thermal images for eachcondition of the cooling radiator and also to classify and recognizethe different conditions of radiator.

to-cropping (A: radiator fins blockage, B: loose connections between fins and tubes, C:

Table 3Final selected features, based on GA.

Images/statistical texture features Mean Standard deviation Smoothness Skewness Energy Entropy

Original thermal image 1 0 1 0 1 0Wavelet approximation image 0 0 1 1 0 1Wavelet horizontal image 1 0 0 1 0 1Wavelet vertical image 0 0 1 0 1 1Wavelet diagonal image 1 1 1 0 0 1

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443 441

For feature selection, finding the best features manually is verytime consuming so it is necessary to build and investigate 230

networks to check all the cases. The GA assisted in the selection ofthe best features in a very short time and with checking 1020networks only. Running the program took almost 30 min with a PC(core i5 CPU, TM4440 @ 3.10 GHz). The result of programwas aMLPnetwork with only one hidden layer that has 6 neurons. The finalselected features were used as inputs of classifier by optimizingaccuracy of ANN's classifier. The selected features used as the inputsof ANN classifier are shown in Table 3.

The final and important stage for fault detection is classification.For the cooling radiator, after creation theMLP networkwas trainedby the LM back propagation training algorithm. Mean square error(MSE) was used as the performance function and selected featureswere considered as the inputs of the network. The trained networkwas implemented for classifying the test data. The topology of theANN is the main component in designing an optimal classifier,because structure impacts on the learning ability and the accuracyof the final network in classifying data. The number of hiddenlayers and their neurons are major factors for designing MLP net-works. The number of neurons in the input and output layers wasfixed because they are dependent on the feature vector and thenumber of classes, respectively. The input layer consisted of 16nodes based on the feature selection operation (see Table 3). Theoutput layer consisted of six neurons which were related to the sixclasses, i.e., radiator tube blockage, radiator fin blockage, looseconnection between fins and tubes, radiator door failure, coolantleakage and normal, for fault diagnosis and conditionmonitoring ofthe radiator. The number of hidden layers and their neuronsdepend on the difficulty of the investigated problem. Generally, onehidden layer with a lower number of neurons is preferred, becauseit leads to a reduction in the network size and an increase in thenetwork's learning ability. This matter is very important for onlinecondition monitoring. Several combinations of the number ofneurons in the hidden layer varying from 2 to 15 and the number ofepochs varying from 100 to 1000 were investigated by a trial anderror method. To find the best combination, the total classificationaccuracy was used as the selection criterion. The results showed

Fig. 7. The best ANN model with 16-6-6 topol

that the hidden layer with six neurons (i.e., a 16-6-6 topology) hadthe smallest sizewith the highest total classification accuracy. Thus,the 16-6-6 network was selected as the best topology for faultdiagnosis and condition monitoring of the cooling radiator. ThisMLP network as the radiator classifier is shown in Fig. 7.

Table 4 shows the confusion matrix as a result of ANN withprimary feature vector inputs (without any feature selection) usingexperimental data. Table 5 gives the performance parameters of theclassifier according to the above-mentioned confusion matrix,including classification accuracy, precision, sensitivity, specificityand AUC for radiator tube blockage (TB), radiator fin blockage (FB),loose connection between fins and tubes (LC), radiator door failure(DF), coolant leakage (CL) and normal (N) classes. The classificationaccuracy of the ANN classifier for FB, LC, CL, DF, TB and N classeswere 95.99%, 98.45, 94.13, 91.66, 99.38 and 97.53%, respectively. Theoverall accuracy of the ANN classifier obtained was 88.58%. Theaverage per class accuracy, precision, sensitivity, specificity, AUCwere 96.62%, 87.38.68, 87.47, 97.77 and 92.62%, respectively.

Table 6 shows the confusion matrix as a result of the ANNclassifier with the 16-6-6 topology using selected features as inputs(Table 3, feature selection based on GA) for the experimentaldataset. Table 7 gives the performance measurements of the clas-sifier according to the confusion matrix (Table 6), including clas-sification accuracy, precision, sensitivity, specificity and AUC for allclasses. 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 and98.76%, respectively. The overall accuracy of the optimum ANNclassifier 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.

Comparing the results of for the two classifiers (classifying usingprimary feature vector and selected feature vector via GA) indictedsignificant improvement of classification performance using the GAfeature selection. The GA feature selection method selected a smallsubset of the relevant features from the original ones according tocertain relevant evaluation criterion. This led to a higher classifi-cation performance, lower computational cost and better modelinterpretability. Fig. 8 shows the changes in MSE with changing

ogy for fault diagnosis of cooling radiator.

Table 4Confusion matrix obtained from the evaluation of 30-6-6 ANN classifier (withoutfeature selection).

Estimated/actual FB LC CL DF TB N

FB 57 0 1 3 0 2LC 0 58 0 2 0 0CL 3 1 38 7 0 3DF 4 2 4 28 2 0TB 0 0 0 0 58 0N 0 0 0 3 0 48

The bold entries (diagonal elements in the table) represent the number of points forwhich the predicted label is equal to the true label, i.e., the number of correctlyclassified instances of each class.

Fig. 8. Changes of MSE related to epoch numbers when MLP for selected feature in-puts, is trained with 203 epochs.

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443442

epoch number when MLP was used for determining the selectedfeature inputs, over a training period of 203 epochs. The best per-formance of the ANN is seen at epoch 193 for MLP with 16-6-6topology.

Therefore, the proposed intelligent system could classify andrecognize IR thermal images for the different conditions of radiatorwith high accuracy. This provides confidence that the system can beemployed for the intelligent condition monitoring and fault diag-nosis of a cooling radiator.

4. Conclusions

This paper has presented a useful application of thermographyfor intelligent fault diagnosis and condition monitoring, andapplied it to a cooling radiator. In general, the application ofintelligent condition monitoring and fault diagnosis to detect fault

Table 5Performance 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 93.37LC 98.45 96.66 95.08 99.23 97.16CL 94.13 73.08 88.37 95.01 91.69DF 91.66 70 66.17 95.73 80.42TB 99.38 100 96.66 100 98.33N 97.53 94.11 90.56 98.9 94.73

Average per-class 96.62 87.38 87.47 97.77 92.62

Table 6Confusion matrix obtained from the evaluation of 16-6-6 ANN classifier (with GAfeature selection).

Estimated/actual FB LC CL DF TB N

FB 60 0 1 0 0 0LC 0 60 1 0 0 0CL 0 0 36 9 0 1DF 0 2 3 34 1 0TB 0 0 0 0 66 0N 2 0 0 0 1 48

Table 7Performance 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 98.2LC 99.07 98.36 96.77 99.62 98.2CL 95.37 78.26 87.8 96.47 92.14DF 95.37 85 79.07 97.86 88.47TB 99.38 100 97.06 100 98.53N 98.76 94.12 97.96 98.91 98.43

Average per-class 97.84 92.35 92.93 98.75 95.66

types precisely is very complex and difficult, but by combiningimage processing, genetic algorithm (GA) and artificial neuralnetwork (ANN) techniques provides both diagnosis efficiency andaccuracy gains.

In this study, a new intelligent diagnosis system has beendeveloped and applied to the classification of six types of coolingradiator conditions via using of infrared thermal images; namely,radiator tube blockage, radiator fin blockage, loose connectionsbetween fins and tubes, radiator door failure, coolant leakage andnormal. The proposed system consisted of several subsequentprocedures 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 theoriginal and decomposed thermal images. The feature selectionbased on GA was used in selecting significant features in order toenhance the performance of the ANN in the final stage. The clas-sification results demonstrated that this system could be employedfor the intelligent condition monitoring and fault diagnosis ofmechanical equipment that has a strong thermal signature indi-cating its operating state. Since there is some initial training of thesystem, it is better suited to the ongoing and periodic maintenanceof multiple pieces of similar equipment. In such cases, there shouldbe a significant positive return on the investment.

Nomenclature

Cj actually classesC*i classifier classes

FP false positivesFN false negativesTP true positivesTN true negativesH(zi) image histogramP(zi) normalized histogramz random variable of intensityN number of all pixelsm means standard deviationS skewnessU uniformityE entropy

References

[1] T. Astarita, G.M. Carlomagno, Infrared Thermography for Thermos-fluid-dy-namics, Springer, 2013.

A. Taheri-Garavand et al. / Applied Thermal Engineering 87 (2015) 434e443 443

[2] G.M. Carlomagno, G. Cardone, Infrared thermography for convective heattransfer measurements, Exp. Fluids 49 (2010) 1187e1218.

[3] Y. Cao, K. Kengskool, An automotive radiator employing wickless heat pipes,in: MAESC Conference, Memphis, TN, 2009.

[4] S.D. Oduro, J. Ampofo, Effect of radiator fins blockage by clay soil on the enginecooling temperature, Int. J. Eng. Technol. 2 (6) (2012) 1002e1009.

[5] S.C. Pang, M.A. Kalam, H.H. Masjuki, M.A. Hazrat, A review on air flow andcoolant flow circuit in vehicles' cooling system, Int. J. Heat Mass Transfer 55(2012) 6295e6306.

[6] S. Mao, C. Cheng, X. Li, E.E. Michaelides, Thermal/structural analysis of radi-ators for heavy-duty trucks, Appl. Therm. Eng. 30 (2010) 1438e1446.

[7] R.A. Epperly, G.E. Heberlein, L.G. Eads, A tool for reliability and safety: predictand prevent equipment failures with thermography, in: IEEE Petroleum andChemical Industry Conference, Banff, Alta, 1997, pp. 59e68.

[8] S. Bagavathiappan, B.B. Lahiri, T. Saravanan, J. Philip, T. Jayakumar, Infraredthermography for condition monitoring e a review, Infr. Phys. Technol. 60(2013) 35e55.

[9] A.S. Nazmul Huda, S. Taib, Application of infrared thermography for predic-tive/preventive maintenance of thermal defect in electrical equipment, Appl.Therm. Eng. 61 (2013) 220e227.

[10] C. Meola, G.M. Carlomagno, A. Squillace, G. Giorleo, The use of infraredthermography for nondestructive evaluation of joints, Infr. Phys. Technol. 46(2004) 93e99.

[11] W.T. Kim, S. Ranjit, D.H. Park, M.Y. Choi, Infrared thermographic NDT for thefault diagnosis of bearing with foreign substances inside under loading con-dition, in: 11th European Conference on Non-Destructive Testing (ECNDT2014), Prague, Czech Republic, 2014.

[12] S.G. Lee, W.T. Kim, Inspection of the leakage for the closure plug of heavywater reactor using infrared thermography in nuclear on-site application, in:10th International Conference on Quantitative Infra Red Thermography,Qu�ebec (Canada), 2010.

[13] Z. Ge, X. Du, L. Yang, Y. Yang, Y. Li, Y. Jin, Performance monitoring of direct air-cooled power generating unit with infrared thermography, Appl. Therm. Eng.31 (2011) 418e424.

[14] A.M. Younus, B.S. Yang, Intelligent fault diagnosis of rotating machinery usinginfrared thermal image, Expert Syst. Appl. 39 (2012) 2082e2091.

[15] A. Darabi, X. Maldague, Neural network based defect detection and depthestimation in TNDE, NDT&E Int. 35 (2002) 165e175.

[16] I.A. Abu-Mahfouz, A comparative study of three artificial neural networks forthe detection and classification of gear faults, Int. J. Gen. Syst. 34 (3) (2005)261e277.

[17] Z. Dworakowski, L. Ambrozinski, P. Packo, K. Dragan, T. Stepinski, T. Uh,Application of artificial neural networks for damage indices classification with

View publication statsView publication stats

the use of Lamb waves for the aerospace structures, Key Eng. Mater. 588(2014) 12e21.

[18] S.E. Umbaugh, Computer Imaging: Digital Image Analysis and Processing,Taylor & Francis, UK, 2005.

[19] A.M. Younus, A. Widodoa, B.S. Yang, Application of thermal image: machinefault diagnosis using PCA and ICA combine with SVM, in: International Con-ference on Mechanical Engineering (ICME2009), Dhaka, Bangladesh, 2009.

[20] R.C. Gonzalez, R.E. Woods, S.L. Eddins, Digital Image Processing Using MAT-LAB, Pearson Prentice Hall, New Jersey, USA, 2004.

[21] S. Mallat, Theory for multi resolution signal decomposition: the waveletrepresentation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (7) (1989) 674e693.

[22] C. Mi, W. Lan, Haar Wavelet Transform Application in Image Processing,Ningxia Engineering Technology, 2003.

[23] N. Sebe, M. Lew, Wavelet based texture classification, in: Proc. 15th Int. Conf.Pattern Recogn. (ICPR), Barcelona, Spain, vol. 3, 2000, pp. 3959e3962.

[24] P.S. Hiremath, S. Shivashankar, Wavelet based features for texture classifica-tion, GVIP J. 6 (3) (2006) 55e58.

[25] P.W. Huang, S.K. Dai, Image retrieval by texture similarity, Pattern Recogn. 36(2003) 665e679.

[26] A. Materka, M. Strzelecki, Texture Analysis Methods e A Review (COST B11report), Technical University of Lodz, Institute of Electronics, Brussels, 1998.

[27] K. Mollazade, M. Omid, A. Arefi, Comparing data mining classifiers for gradingraisins based on visual features, Comp. Electron. Agric. 84 (2012) 124e131.

[28] J. Tang, S. Alelyani, H. Liu, Feature selection for classification: a review, in:Data Classification: Algorithms and Applications, CRC Press, 2014.

[29] J. Dy, C.E. Brodley, Feature selection for unsupervised learning, J. Mach. Learn.Res. 5 (2004) 845e889.

[30] A.A. Altun, N. Allahverdi, Neural network based recognition by using geneticalgorithm for feature selection of enhanced fingerprints, in: ICANNGA, 2007.

[31] K. Kim, I. Han, Genetic algorithms approach to feature discretization in arti-ficial 19 neural networks for the prediction of stock price index, Expert Syst.Appl. 19 (2) (2000) 125e132.

[32] B. Samanta, K.R. Al-Balushi, S.A. Al-Araimi, Bearing fault detection usingartificial neural networks and genetic algorithm, EURASIP J. Appl. Sig. Process.3 (2004) 366e377.

[33] M.R.A. Gamarra, C.G.M. Quintero, Using genetic algorithm feature selection inneural classification systems for image pattern recognition, Ing. Invest. 33 (1)(2013) 52e58.

[34] V. Labatut, H. Cheri, Accuracy measures for the comparison of classifiers, in:Al-Dahoud Ali (Ed.), The 5th International Conference on Information Tech-nology, May 2011, Amman, Jordan, Al-Zaytoonah University of Jordan, 2011.

[35] M. Sokolova, G. Lapalme, A systematic analysis of performance measures forclassification tasks, Inform. Process. Manag. 45 (2009) 427e437.