Abstract
The online detection and diagnosis of sliding bearings is one of the critical questions in the failure diagnosis of large power machine. At present, many methods such as vibration diagnosis, spectrometric oil analysis (SOA), etc. have been tried to detect and diagnose the failure of sliding bearing. But all of these methods have met some questions in the practice of industry, and can not monitor its failure in the initial period, so the acoustic emission method (AE) to diagnose its failure was introduced in this paper, including the introduction of the AE mechanism of sliding bearings in rotating machinery and the influence factors of AE signal intensity, and the synchronous detection of acoustic emission signals and bearing vibration signals in the abnormal situation. In addition, the AE signal characteristics and vibration signal characteristics are extracted. Furthermore, the relationship between AE parameters with the influence factors of sliding bearing is established. At the same time, a comparison between AE signal characteristics and vibration signal characteristics in the same conditions is presented. After experiment, it is concluded that AE sliding bearing fault diagnosis method is with high rates of diagnosis and accuracy, and the fault can be diagnosed early, compared with the sliding bearing vibration diagnosis method.
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