This present article evaluates the state of starvation in a journal bearing using acoustic emission (AE) and vibration measurement techniques. A journal bearing requires a constant supply of oil in an adequate amount to develop a hydrodynamic film, thick enough to separate the surfaces and avoid asperity contacts. On a microscopic level, the surface interaction under starved lubrication results in deformation and fracture of asperities. This causes a proportionate increase in AE and vibration. The AE activities resulting from asperities interaction have significant energy in the frequency range of 100–400 kHz with peak frequencies in the range of 224–283 kHz. Further, the peak frequency shifts from the higher to lower side as the asperity interaction transits from the elastic to plastic contact. This information derived from the spectral analysis of AE signals can be used to develop condition monitoring parameters to proactively control the lubrication and prevent bearing failure.
The ability to classify condition-monitoring data and make a decision can be imparted to a computer through the machine learning approach. In this article, the acoustic emission signals emerging from journal bearings under normal operating conditions and faulty states, namely cavitation, particle contamination and oil starvation, have been classified to develop fault-prediction model using the machine learning approach. Furthermore, an application has been developed that takes acoustic emission data as input and diagnoses the category of faults besides triggering an alarm under faulty states.
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