Fault Severity Classification and Size Estimation for Ball Bearings Based on Vibration Mechanism

Abstract

Severity identification and size estimation is a crucial part of the quantitative diagnosis for ball bearing faults. In this paper, novel fault severity classification rules and the size estimation model based on vibration mechanism for ball bearings are proposed for more accurate estimation of the fault size. A nonlinear dynamic model, with geometric properties and deformation of the ball considered, is established to analyze the vibration characteristics of ball bearing with outer race fault. It turns out that there are different features in vibration responses with different fault sizes, and then the fault severity is classified with vibration features. Based on the vibration mechanism analysis, functional relations and mathematical expressions between the vibration characteristics and fault sizes are inferred to realize the quantitative diagnosis of faulty bearings. The experiments are performed to verify the effectiveness of the proposed method of fault severity classification and size estimation. Deep groove ball bearings designated as 6308, with seeded square-shaped surface defects of different sizes, are chosen for the experiments. The results show that the proposed method can accurately estimate the fault sizes within the acceptable error range.

Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method

“…According to the analysis of the vibration mechanism, the functional model corresponding to the classification of fault severity was established. 1 The non-linear and non-stationary characteristics of the signal and the interference of external factors on the obtained vibration signal are all factors that affect the extraction of features from complex vibration signals. [2][3][4][5] For this purpose, a large number of articles on feature extraction methods can be found in the literature.…”

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

“…Adaptive impulse dictionary [8], combined time-frequency dictionary [9], matching pursuit [10], and dictionary learning method [11] were presented to diagnose faults of rotating machinery. In addition to the above mentioned two popular methods, many other research methods such as fault quantitative diagnosis [12], fault mechanism research [13,14], fault diagnosis of low speed machinery [15] and fault location diagnosis [16] also gained attention. Some traditional methods such as Fourier transform, envelope analysis method, empirical mode decomposition, wavelet transform, spectral kurtosis, and morphological filtering have shown their advantages on single fault detection [17][18][19].…”

A Theoretical Model with the Effect of Cracks in the Local Spalling of Full Ceramic Ball Bearings

“…The main failure modes of full ceramic bearings are spalling and cracking. Compared with steel, the ceramic material is higher in brittleness and lower in fracture toughness, so spalling occurs more frequently on the contact surface under high Hertz pressure, which leads to small cracks around the surface defects [6][7][8][9][10].…”