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

Taheri‐Garavand, Amin; Ahmadi, Hojjat; Omid, Mahmoud; Mohtasebi, Seyed Saeid; Mollazade, Kaveh; Russell‐Smith, Jeremy; Carlomagno, Giovanni Maria

doi:10.1016/j.applthermaleng.2015.05.038

Cited by 81 publications

(27 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the structure of the artificial neural network, three layers were used, for the separation of any type of space, and there is never need to use more layers (Manhaj, 2005). The classification performances were measured based on the values of the confusion matrix, such as percentage of accuracy, precision, sensitivity, specificity area under the curve (AUC) as following formulas (Sokolova & Lapalme, 2009;Taheri-Garavand et al, 2015):…”

Section: Discussionmentioning

confidence: 99%

“…In the structure of the artificial neural network, three layers were used, since, three layers (includes input, hidden, and output layer) are suitable for the separation of any type of space, and there is never need to use more layers (Manhaj, ). The classification performances were measured based on the values of the confusion matrix, such as percentage of accuracy, precision, sensitivity, specificity area under the curve (AUC) as following formulas (Sokolova & Lapalme, ; Taheri‐Garavand et al, ):

Accuracy = \frac{N_{TP} + N_{TN}}{N_{TP} + N_{TN} + N_{FP} + N_{FN}}

Precision = \frac{N_{TP}}{N_{TP} + N_{FP}}

Sensitivity = \frac{N_{TP}}{N_{TP} + N_{FN}}

Specificity = \frac{N_{TN}}{N_{TN} + N_{FP}}

AUC = \frac{1}{2} (\frac{N_{TP}}{N_{TP} + N_{FN}} + \frac{N_{TN}}{N_{TN} + N_{FP}})

where N TP , N TN , N FP , and N FN are number of samples which are classified as true positive, true negative, false positive, and false negative, respectively. For ANN analysis, the data set was divided into 60, 20, and 20% for training, testing, and cross validation, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Using an E‐nose machine for detection the adulteration of margarine in cow ghee

Ayari

Mirzaee‐Ghaleh

Rabbani

et al. 2018

J Food Process Engineering

View full text Add to dashboard Cite

Diagnosis of adulteration in cow ghee is one of the key concerns of recent years. In this study, the aroma fingerprints of cow ghee were detected. For this purpose, an electronic nose system was developed and its ability for detection of different amounts of margarine mixed with pure cow ghee (10, 20, 30, 40, and 50% levels) was investigated. The system was equipped with eight sensors (MOS type), that each of them reacts to specific volatile compounds in the samples. The features of the signals were considered for data analysis. In this research, principal components analysis (PCA) and artificial neural networks (ANN) methods were used for data analyzing. According to the results, the PCA analysis explained 98% of the variance in the data set. Also, ANN analysis identified 85.6 and 97.2% of pure cow ghee from the adulteration ones when samples were classified in 7 and 2 classes, respectively. Practical applications Diagnosis and estimation of adulteration in cow ghee is one of the key concerns for consumers. Electronic nose is a new method that can be used for detection of products quality. We show that electronic nose system is a valuable tool for detection of margarine as a one of common adulteration in cow ghee.

show abstract

Section: Discussionmentioning

confidence: 99%

Accuracy = \frac{N_{TP} + N_{TN}}{N_{TP} + N_{TN} + N_{FP} + N_{FN}}

Precision = \frac{N_{TP}}{N_{TP} + N_{FP}}

Sensitivity = \frac{N_{TP}}{N_{TP} + N_{FN}}

Specificity = \frac{N_{TN}}{N_{TN} + N_{FP}}

AUC = \frac{1}{2} (\frac{N_{TP}}{N_{TP} + N_{FN}} + \frac{N_{TN}}{N_{TN} + N_{FP}})

Section: Methodsmentioning

confidence: 99%

Using an E‐nose machine for detection the adulteration of margarine in cow ghee

Ayari

Mirzaee‐Ghaleh

Rabbani

et al. 2018

J Food Process Engineering

View full text Add to dashboard Cite

show abstract

“…Moreover, features selected using relief algorithm based on the analysis of weights are greater than the thresholds, which probably could be affected by the selection of predetermined threshold in different experiments. Furthermore, Taheri et al [11] proposed a diagnosis method by using 2D-DWT, GA and Artificial Neural Networks (ANNs), in which four coefficients in the first level after 2D-DWT were used, and six features were then applied to extract fault feature vectors from those coefficients. The use of GA and ANNs is for quantifying a subset of the relevant features which can achieve best classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…In order to monitor the conditions as well as diagnose the machine faults, signal processing and feature extraction are two of important stages in the field of fault diagnosis [7]- [9]. The 2-Dimensional Discrete Wavelet Transform (2D-DWT) enjoys the advantages of good time and frequency resolution, which has been widely applied in IRTbased fault diagnosis of rotating machinery [10], [11]. In addition, time and frequency domain features as well as non-linear features are popularly applied to discriminate fault information in thermal images acquired from machine [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive fault diagnosis of roller bearings using infrared thermal images

Huo

Zhang

Sath

et al. 2017

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

Abstract-Fault diagnosis of roller bearings in rotating machinery is of great significance to identify latent abnormalities and failures in industrial plants. This paper presents a new selfadaptive fault diagnosis system for different conditions of roller bearings using InfraRed Thermography (IRT). In the first stage of the proposed system, 2-Dimensional Discrete Wavelet Transform (2D-DWT) and Shannon entropy are applied respectively to decompose images and seek for the desired decomposition level of the approximation coefficients. After that, the histograms of selected coefficients are used as an input of the feature space selection method by using Genetic Algorithm (GA) and Nearest Neighbor (NN), for the purpose of selecting two salient features that can achieve the highest classification accuracy. The results have demonstrated that the proposed scheme can be employed effectively as an intelligent system for bearing fault diagnosis in rotating machinery.

show abstract

“…By applying modern image processing methods to the acquired images with artificial intelligence (AI)based approaches, an increased estimation of food characteristics may be made rapidly without human intervention. Novel image processing techniques (image processing based on artificial intelligence methods) on images can be used as a rapid and automatic approach to estimate food characteristics (Taheri-Garavand et al, 2015). According to the recent studies, CV and ANNs can be utilized as new methods to determine the quality properties of various kinds of meat, such as fish freshness (Dowlati et al, 2012), meat color (Girolami, Napolitano, Faraone, & Braghieri, 2013), and pork meat freshness (Xiao, Gao, & Shou, 2014).…”

mentioning

confidence: 99%

A nondestructive intelligent approach to real‐time evaluation of chicken meat freshness based on computer vision technique

Taheri‐Garavand

Fatahi

Shahbazi

et al. 2019

J Food Process Engineering

View full text Add to dashboard Cite

In this study, the capability of a procedure based on combination of computer vision (CV) and artificial intelligence techniques examined for intelligent and nondestructive prediction of chicken meat freshness during the spoilage process at 4°C. The proposed system comprises the following stages: capture images, image preprocessing, image processing, computing channels, feature extraction, feature selection by a hybrid of genetic algorithm (GA) and artificial neuronal network (ANN), and prediction by using ANN. The number of neurons in input layer was determined 33 (selected features) and freshness used as the output. The ideal ANN model was obtained with 33‐10‐1 topology. The high performance of the model was provided with a correlation coefficient of 0.98734 and MSE of 0.002045. The encouraging results of the current study obviously indicated the high potential of CV‐based system combined with an intelligence method as a smart, nondestructive, and reliable technique for online evaluation of chicken meat freshness. Practical application Diagnosis and estimation of chicken meat freshness are considered a significant concern in meat quality for consumers. Computer Vision as a novel nondestructive technique can be utilized to evaluate the quality of products. We present the potential of computer vision‐based method as a smart, nondestructive, and reliable method for online prediction of the freshness of chicken meat.

show abstract

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

Cited by 81 publications

References 24 publications

Using an E‐nose machine for detection the adulteration of margarine in cow ghee

Using an E‐nose machine for detection the adulteration of margarine in cow ghee

Self-adaptive fault diagnosis of roller bearings using infrared thermal images

A nondestructive intelligent approach to real‐time evaluation of chicken meat freshness based on computer vision technique

Contact Info

Product

Resources

About