LAUSR

Fault identification is a recurrent topic in rotating machines field. The evaluation of fault parameters allows better maintenance of such expensive and, sometimes, large machines. Unbalance is one of the most common faults, and it is inherent to rotors functioning. Wear in journal bearings is another common fault, caused by several start/stop cycles – when at low rotating speed, there is still contact between shaft and bearing wall. Fault parameter identification generally uses deterministic model–based methods. However, these methods do not take into account the uncertainties inherently involved in the identification process. The stochastic approach by the Bayesian inference is, then, used to account the uncertainties of the fault parameters. The generalized polynomial chaos expansion is proposed to evaluate the inference, due to its faster performance regarding the Markov chain Monte Carlo methods. Deterministic and stochastic approaches were compared; all were based on experimental vibration measurements of the shaft inside the journal bearings. The Bayesian inference with the polynomial chaos showed reliable and promising results for identification of unbalance and bearing wear fault parameters.