Machine Learning-Based Fault Diagnosis of Self-Aligning Bearings for Rotating Machinery Using Infrared Thermography

Table 9

Comparative analysis of present work with similar research work using IRT.

References	Camera used	Fault examined	Image preprocessing	Classifiers	Accuracy (%)
Li et al. [36]	Fluke-Ti32	Unbalance, outer race fault, ball fault, inner race fault	Region selection	CNN	98.59
Huo et al. [37]	FLIR-A35	Outer race fault, inner race fault	2D-DWT	Naive Bayes, SVM	91.67
					90.67
Glowacz et al. [38]	FLIR-E4	Broken rotor bar	MoASoID	NN, K-mean	100
					100
Janssens et al. [24]	FLIR-SC655	Rotor imbalance, bearing fault, lubrication	Windowing and subsampling	SVM	88.25
Lim et al. [23]	FLIR-SC5000	Unbalance, ball bearing fault, shaft misalignment	2D-DWT	SVM	96.25
Eftekhari et al. [26]	FLIR-I60	Stator winding inner turn fault	Single Gaussian model	—	—
Younus et al. [22]	FLIR-A40	Shaft misalignment, bearing fault, unbalance	2D-DWT	LDA, SVM	—
Younus et al. [21]	FLIR-A40	Shaft misalignment, bearing fault	Image segmentation	SVM	—
Zhiyi et al. [28]	FLIR-A35	Outer race fault, ball fault, inner race fault	—	CNN	98.26
Glowacz et al. [29]	FLIR-E4	Faulty fan, damaged gear-train	BCAoID	NN, BNN	100
					97.91
Present work	FLIR-E60	Inner race fault, outer race fault	2D-DWT	LDA, KNN, SVM	94.4
					88.9
					100

MoASoID: method of area selection of image difference; BCAoID: binarized common areas of image differences; SR: softmax regression; CNN: convolutional neural network; NN: nearest neighbor; BNN: back propagation neural network.