BEARINGS are among the most important machine components in the vast majority of rotating machines and exigent demands are made upon their carrying capacity and reliability. Generally, a rolling bearing cannot rotate for ever.It often works well in non-ideal conditions, but sometimes minor problems cause bearings to fail quickly and mysteriously without any notable warning. The bearing failures are mainly resulted from excessive wear or damage in rolling ball elements as well as in the inner/outer races of the bearing. Presently real-time condition monitoring systems forbearing systems often fail to provide sufficient time between warnings and on the other hand, inaccurate interpretation of operational conditions may result in false alarms and associated unnecessary costs and downtime .Traditionally, the detection of faults has become possible by comparing the sensitive features of signals from sensors in the machinery while running in normal and faulty conditions.
This method of the detection of faults has showed considerable success and several techniques have been developed. The use of vibration signals is quite common in the field of condition monitoring of rotating machinery.Analyzing the vibration signals directly in the time domain is one among the simplest and cheapest diagnosis approaches . However, as the damage increase, the vibration sign all becomes more random and the temporary statistical values reduce to more like that of normal bearing levels. This is the most important shortcoming of this approach . In the frequency domain approach the major frequency components of vibration signals and their amplitudes are used for trending purposes. One of the drawbacks of frequency-domain approaches is that they require the bearing defect frequencies to be known or pre-estimated. The time-frequency domain approach use both time and frequency information allowing for the transient features, such as impacts. However, this approach fails to analyze the continuously smooth signal.In this paper, the powerful method of RQA is used to study and characterize the experimental sensor signals generated during the normal and faulty states of the bearing under study.
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