Study On Fault Diagnosis Of Slow-speed Rolling Bearing Using Stress Waves And Wavelet Analysis

Fault diagnosis of the slow-speed rolling bearings is difficult, due to special structure, peculiar working condition and complex fault mechanism. A new method applying stress waves and wavelet analysis for the fault diagnosis of slow-speed rolling bearings was presented by analyzing the fault features of the bearings in the paper. The stress wave technique has been applied initially in the fault diagnosis as a new dynamic detecting method. So thoroughly understanding stress wave characteristics and its transmission discipline in slow-speed rolling bearings is a key of detecting surface defects on bearing components. At the same time it plays an important role for the stress wave technique to process stress wave signals, but at present there are many problems to be resolved in the field. In the paper, studies were profoundly made on the actual detecting problems happened in the topic of Natural Science Foundation of China - "study on condition monitoring and fault diagnosis of slow-speed rotating machinery using stress waves and intelligent computation" .Firstly, take example for 01B65EX Cooper rolling bearing with slow speed the three-dimension contact models of good and fault bearing were set up, which was discussed with using finite element method. Then the stress, the strain and contact stress distribution were computed. The stress and strain distribution law of the outer race and the contact stress distribution law of the interface on good and fault bearing were compared. The numerical analysis shows that faults and their locations can be judged by observing stress distribution law. Therefore it lays the foundation for further investigation of fault diagnosis based on stress waves of rolling bearing with slow speed.Secondly, the properties and characters of wavelet analysis were introduced in brief, the application of which to fault diagnosis of slow-speed rolling bearings was emphasized. As a time-frequency analysis method, the wavelet analysis can make the signal"s resolving power high enough in the time domain and the frequency domain at the same time, by adjusting lengthand width of its windows. According to the characteristic of the wavelet analysis and stress wave signals, Daubechies wavelet was applied to collected data signals processing, and the stress wave signals were decomposed.Finally, the wavelet transform of data signals collected in experiment was carried out with db6 wavelet and scale 4 on the basis of the stress wave analysis. The frequency D₃ and D₄ signals are stress wave characteristic signal. Then with reducing the low-frequent and high-frequent noise, D₃ and D₄ signals were reconstructed and characteristic frequencies of stress waves with the simulative faults were extracted. Meanwhile amplitudes of the simulative faults were analyzed. It has been found that stress waves associated with simulating grease contamination generated the highest values of amplitude, the lowest amplitude value was emitted from the inner race defect, as the sensor was placed on the bearing housing. It was corresponding to the conclusion of the conclusion of finite element analysis, i.e. the stress and strain distribution laws of the outer race were affected most by the outer race defect.