Abstract
In order to diagnose bearing faults under different operating state and limited sample condition, a fault diagnosis method based on adjusted spectrum image of vibration signal is proposed in this paper. Firstly, the Davies–Bouldin index (DBI) is employed to select a proper capture focus (CF) and image size, and the spectrum of vibration signal is computed via fast Fourier transformation (FFT) and adjusted according to the average rotating speed. Then, the spectrum is plotted and captured as a two-dimensional (2D) image with the optimized CF and image size. Two-dimensional principal component analysis (2DPCA) is used to reduce the dimension of images, and finally a nearest neighbour method is applied to classify the faults of bearings. Two experiments are carried out to validate the effectiveness of the proposed method. Besides, a further investigation on the effect of spectrum frequency resolution is conducted and a recommended selection method of frequency resolution is given based on the experimental performances. In our method, the training samples could be from only one operating condition, while the testing samples are from all possible operation conditions. All experiment results have demonstrated that the proposed method could achieve high classification accuracy even with very limited training samples.
Highlights
As the key element of rotating machinery, the faults of rolling bearing could lead to mechanical breakdown and great economic loss
With c 0.9 defined in Equation (8) for dimension reduction, the diagnosis performance using the proposed method is tested and shown in Table 2, where different number of samples are used for training the nearest neighbour classifier (NNC) and the average accuracies are presented for 20 randomized trials
The adjusted spectrum image is proposed as feature for bearing fault diagnosis under different operation conditions
Summary
As the key element of rotating machinery, the faults of rolling bearing could lead to mechanical breakdown and great economic loss. The operating speed of rolling bearing is slightly fluctuant due to the influences from the load, controller, and other components; its vibration signals are commonly considered approximately stationary. Rolling bearing may operate in different speeds, which results in great challenges for its accurate fault diagnosis, especially when the number of fault samples is small. Most fault diagnosis methods are based on vibration signals [1,2,3], and the diagnostic procedure mainly includes two steps: (1) extracting features from the vibration signal; and (2) classifying features with a classifier. Many methods were proposed to improve the diagnosis accuracy in two different ways. One is to find proper features in order to represent the characteristics of faulty vibration signals; and the other one is to find proper classifiers with strong classification capability
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