Abstract
Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. This article presents the use of IRT for the bearing fault diagnosis. A two-dimensional discrete wavelet transform (2D-DWT) has been applied for the decomposition of the thermal image. Principal component analysis (PCA) has been used for the reduction of dimensionality of extracted features, and thereafter the most relevant features are accomplished. Furthermore, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN) as the classifiers were considered for classification of faults and performance assessment. The results reveal that the SVM outperformed LDA as well as KNN. Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. The utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability.
Highlights
Condition-based maintenance and condition monitoring are associated with maintenance of equipment based on the real-time condition of subsystem(s) of the machine
Bearing fault (BF) is a widely recognized fault in any rotating machine. e bearing failure may be due to lack of lubrication, disproportionate greasing, corrosion, overheating, and so on. e malfunctioning of the bearing gives rise to process downtime as well as enhances the maintenance cost [6, 7]
For the diagnoses of such faults, numerous condition monitoring (CM) techniques have been utilized from the last few decades, namely, vibration-based CM, acoustic emission, and motor current signature analysis. ese traditional techniques are expensive since their setup includes sensors and data acquisition structure; mounting of sensors is quite difficult in such CM techniques
Summary
Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. A two-dimensional discrete wavelet transform (2D-DWT) has been applied for the decomposition of the thermal image. Principal component analysis (PCA) has been used for the reduction of dimensionality of extracted features, and thereafter the most relevant features are accomplished. Support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN) as the classifiers were considered for classification of faults and performance assessment. Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. E utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. e utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability
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