The work aims to solve the problem of on-line monitoring and fault diagnosis of aero-engine rolling bearing un-der actual working conditions.Firstly,the effective value was selected as the time domain characteristic parameter,the charac-teristic energy was proposed as the frequency domain characteristic parameter,and the number of oil metal debris was used as the fusion vibration and oil debris information.The above parameters were fused based on fuzzy inference theory.By selecting membership functions and defining fuzzy inference rules,the vibration signals and oil metal debris information were fused to diagnose bearing faults.The spalling extension test of the aero-engine main bearing was carried out,the vibration and oil debris detection system was installed,the vibration and oil debris information of the bearing in the whole process of spalling was col-lected synchronously,and the measured data were analyzed by the proposed method.With the fault expansion,the effective value of vibration signal parameters was an overall upward trend.The frequency domain characteristic energy decreased and fluctuated with the increase of fault spread to a certain extent,which was sensitive to early fault diagnosis of the bearing.Oil debris was the important information for bearing fault diagnosis,and its change trend was monotonically increasing.Oil debris information changed significantly in the later stage of bearing fault,which was sensitive to bearing fault diagnosis.The vibration and oil debris information fusion method based on fuzzy reasoning theory can comprehensively analyze the fault characteristics of different signals and effectively distinguish the running state of bearings.
rolling bearingvibration signaloil metal debrisdecision fusioncondition monitoringfuzzy reasoningaero-engine
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