Study On Fault Diagnosis Of Low-speed Rotating Machinery Using Stress Waves And Wavelet Analysis

Condition monitoring through the use of vibration analysis is an established and effective technique for detecting the loss of mechanical integrity of a wide range and classification of rotating machinery. Equipment rotating at low rotational speeds presents an increased difficulty to the diagnostician, since conventional vibration measuring equipment is not capable of measuring the fundamental frequency of operation. Also, component distress at low operational speeds does not necessarily show an obvious change in vibration signature.Against the difficulty of the fault diagnosis for low-speed rotating machinery, this paper presents a study of high-frequency stress wave analysis as a means of detecting the early stages of the loss of mechanical integrity in low-speed machinery. The background noise was eliminated using wavelet decomposition and the feature frequency of fault stress waves was extracted.In this paper, 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.On the base of finite element analysis for low-speed rolling bearing, the fault stress wave signals which obtained from a steel mill were analyzed in the paper. Firstly, the fault stress wave signals of rolling bearing were analyzed using FFT, but it was failed to extract the feature frequency of the fault signals. Then, according to the characters of the fault stress waves, through the comparison of several mother wavelet, and Db10 was selected to analyze the signals, because of DblO wavelet function have the best similarity with the fault stress waves. Multi-scaled decomposition was carried out in the analysis, and then, the D₃ and D₄ wasreconstructed based on the comparison of the scale energy. At last, the feature frequency of fault stress waves was extracted successfully.