Fault Diagnosis for Rolling Bearing under Variable Conditions Based on Image Recognition

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.

Bearing fault diagnosis method based on the generalized S transform time–frequency spectrum de-noised by singular value decomposition

“…6. The 2D time-frequency matrix of signal is reconstructed by formula (9) and then the noise is suppressed by applying the generalized S inverse transformation.…”

“…Image is an important form of fault diagnosis information, including the application of Charge Coupled Device(CCD) acquired image, spectral image and sound image. 8–10…”

“…Image is an important form of fault diagnosis information, including the application of Charge Coupled Device(CCD) acquired image, spectral image and sound image. [8][9][10] Starting with the idea of short-time Fourier transform and continuous Wavelet transform, a timefrequency analysis method called S transform was proposed by Stockwell et al 11 Later, Pinnegar improved the Gauss's window function by introducing adjustment factor and developed the generalized S transform. 12 The generalized S transform has the multiresolution property of wavelet transform.…”

Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery

“…Usually, the capability of classification model has a significant influence on diagnostic results, indicating the need to attempt appropriate pattern recognition algorithm for rotating machinery (Han and Jiang, 2016). The most used classifiers in this field are artificial neural networks (ANNs) including back propagation neural network (BPNN), radical basis function (RBF), learning vector quantization (LVQ) (Jiang and Liu, 2011), wavelet neural network (WNN) (Lei et al, 2011), extreme learning machine (ELM) (Tian et al, 2015), probabilistic neural network (PNN) (Zhou and Cheng, 2016; Dou and Zhou, 2016) and so forth, and support vector machine (SVM) (Li and Zhang, 2011).…”

Improving Rolling Bearing Fault Diagnosis by DS Evidence Theory Based Fusion Model

“…Sanz et al [2] presented a method for detecting the states of rotating machinery with vibration analysis. Zhou and Cheng [3] proposed a fault diagnosis method based on image recognition for rolling bearing to realize fault classification under variable working conditions. Li et al [4] presented a model for deep statistical feature learning from vibration measurements of rotating machinery, and the results showed that deep learning with statistical feature extraction has an essential improvement potential for diagnosing rotating machinery faults.…”