| Abstract:Aiming at the problem of online condition monitoring and fault diagnosis of aeroengine main bearings under actual service
conditions, a method of main bearing condition monitoring based on information fusion of vibration and oil particles was proposed. The
frequency domain characteristics of the vibration signal measured at the designated position of the engine outer casing are used to define
the rolling bearing fault damage factor, and the metal debris information of the oil return circuit is collected to determine the growth rate of
the debris quantity. The two are combined through fuzzy reasoning to achieve online monitoring of the rolling bearing status. The typical
damage and flaking propagation test of the main bearing of an aeroengine under the condition of a component tester and the entire machine
test were carried out, and the vibration signal and oil debris information were synchronously tested, and the information fusion bearing
condition monitoring method was validated. The vibration and oil debris information during the mid-stage of bearing spalling were input
into the fuzzy inference model established in this paper, yielding an output of 0.59. According to the defined criteria, an output value in the
range of 0–0.25 indicates the bearing is in good condition, 0.25–0.75 indicates an abnormal condition, and 0.75–1 signifies a severe fault.
Since the output value falls within the abnormal condition range, it is determined that the bearing is faulty and requires timely maintenance.
The proposed method provides a reference for the condition monitoring and fault diagnosis of main bearings in aircraft engines. |
