Abstract
Rolling bearing faults often lead to electromechanical system failure due to its high speed and complex working conditions. Recently, a large amount of fault diagnosis studies for rolling bearing based on vibration data has been reported. However, few studies have focused on fault diagnosis for rolling bearings under variable conditions. This paper proposes a fault diagnosis method based on image recognition for rolling bearings to realize fault classification under variable working conditions. The proposed method includes the following steps. First, the vibration signal data are transformed into a two-dimensional image based on recurrence plot (RP) technique. Next, a popular feature extraction method which has been widely used in the image field, scale invariant feature transform (SIFT), is employed to extract fault features from the two-dimensional RP and subsequently generate a 128-dimensional feature vector. Third, due to the redundancy of the high-dimensional feature, kernel principal component analysis is utilized to reduce the feature dimensionality. Finally, a neural network classifier trained by probabilistic neural network is used to perform fault diagnosis. Verification experiment results demonstrate the effectiveness of the proposed fault diagnosis method for rolling bearings under variable conditions, thereby providing a promising approach to fault diagnosis for rolling bearings.
Highlights
Rolling bearings are considered to be a critical mechanical component in industrial applications
Inspired by scale invariant feature transform (SIFT), this study proposes a novel fault diagnosis for rolling bearings under variable conditions
A novel rolling bearing fault diagnosis method under variable conditions, which was originally introduced for image recognition, is described in this paper
Summary
Rolling bearings are considered to be a critical mechanical component in industrial applications. Vibration signal analysis is vital for rolling bearings fault diagnosis due to its connection to fault feature extraction accuracy [3]. Many fault diagnosis methods have been proposed, such as fast spectral kurtosis based on genetic algorithms [10], multiscale entropy and adaptive neurofuzzy inference system [11], and time varying singular value decomposition [12]. Most of these methods are proposed based on the assumption that the rolling bearings operate under fixed conditions when performing fault diagnosis. It is important to investigate the fault diagnosis method suitable for varying conditions
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