| Bearings are generally used in important equipments of petrochemical, metallurgy and railway, etc. And as easily broken parts, almost 30% rotating equipments and vehicles fault accidents are originated from the bearings, which result in large lost. Thus, researchers pay more and more attention to the research on monitoring state of bearings on line and diagnose faults. At present, most methods combining vibration with acoustic testing on rolling bearing fault diagnosing are contacting detection; namely, the sensors used for testing are contacted with bearing seat or connected structure surface, which is not applicable for the testing of bearings under low rotating speed or moving bearings. The paper focus on the research of non-contacting rolling bearing fault diagnosing with acoustic theories and modern signals analysis method. All of these are valuable on theory and application for early diagnosis of rolling bearing fault and preventing failures.The common types of faults and their mechanisms in rolling bearings are analyzed in the paper. Especially, the mechanism that acoustic sources are generated is discussed, the result shows Acoustic Emission(AE) signals mainly occur in the formation and spread stage of wear and surface damage, also, large amount of signals can be found when there are friction and collisions at fault site. To acoustic waves, they propagate in the form of longitudinal wave in air. Their propagation is mainly influenced by the absorption attenuation, and absorption factor is proportional to the square of the frequency. Low frequency acoustic waves have relatively long propagation distance, but high frequency components decay quickly, which is favorable for the diagnosis and analysis of non-contacting rolling bearings with acoustic method.With established testing system for rolling bearing fault detecting, non-contacting acoustic testing experiments are carried out on non-fault bearings and bearings with three different faults. Through the comparative analysis between contacting testing and non-contacting testing, the result shows that for the attenuation when acoustic waves propagate in air, the amplitude and magnitude of signals from non-contacting testing are smaller than those from contacting testing, but the number of hits of them is closer to the theoretical value of components collision frequency under different fault. It provides the basis for fault pattern recognition in non-contacting bearing AE detection.After wavelet decomposition of AE signals from rolling bearings under different fault modes, typical bands are obtained according to spectrum distribution of every band, whose spectrum factors representing the acoustic sources characteristics. Based on this characteristic, the denoising of AE signals can be achieved. Local Hilbert marginal spectrum diagnosis method based on wavelet and EMD is advanced and can be used to recognize fault frequency, marginal spectrum peak frequency of non-fault bearing, bearing with faults in outer ring and bearing with faults in roller. The marginal spectrum frequencies of mixed fault bearing include the above-mentioned peak frequencies. All the above methods can be used to diagnose and recognize signals from non-contacting AE testing on rolling bearings, which lays the foundation for follow-up study.AE experiment and analysis method is established for multi-sensors non-contacting rolling bearing fault testing. In order to obtain complete and cyclic fault acoustic signals, large amount of signals from different sensors are analyzed with the method of correlation analysis, and the relationship is determined between signals from the sensors array, that is, they are corresponding or independent. According to the relationship between hit time and theoretical striking frequency of different faults, duplicated and ineffective parts are removed. After that, cyclic reconstructing method is established for multi-sensors acoustic signals. After reconstructing cyclic acoustic signals from two and three sensors moving non-contacting testing, and comparing with the reconstructing signal form single sensor inspection, the result shows that the established reconstructing method can be used to obtain complete periodic acoustic signals. On the basis of these, overlap information identification and extraction method is established for relevant sensors in acoustic arrays. Cyclic acoustic signals reconstruction is achieved for multi-sensors rolling bearing inspection, and integral cyclic information of moving bearings with faults is obtained. All of them provide basis for the denoising of bearing with different faults.The method of fault pattern recognition is one of the main contents in the paper. AE signals from non-fault rolling bearing are continuous, whose energy is small, and there are no obvious peak in the signals. While, bursting signals will be get during the detection of bearings containing faults, and the magnitude of peak energy in spectrum diagram is large. On the basis of this, the state whether there are faults in bearing can be identified. To the bearings with single fault, combining with the number of hits based on theoretical characteristic frequency, the type of the fault can be achieved. Rolling bearing fault diagnosis method can effectively recognize fault characteristics based on wavelet packet and BP neural network, so as to enhance the diagnosis efficiency and accuracy.Combining with AE signals characteristics of different bearing faults, pattern recognition method of multi-sensors signal reconstructing waveform is advanced based on fuzzy recognition theory. After the analysis of different waveforms belong to different faults, fuzzy recognition algorithm is achieved according to five different recognition elements in the waveform diagram, i.e., voltage of peak amplitude, risetime range width, waveform width, inflection point and largest remaining wave width. Through the exercise of known faults, fault signals from rolling bearings can be recognized properly.
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