Detecting dominant resonant modes of rolling bearing faults using the niching genetic algorithm

In this paper we propose an improvement of methods for adaptive selection of frequency bands containing transients which indicate the presence of the dominant resonant modes of rolling bearing faults using niching genetic algorithm optimization. The main aim of this approach is to diagnose the condition of the bearings and to be able to recognize faults on various parts of bearings and possible combinations of faults. Because the vibration signals corresponding to faults on bearings are typically transients with a wide frequency range occurring around the excited mechanical resonant modes and drowned in the acquired vibration signals, it is necessary to emphasize these excited transients using a matched bank of filters. The dominant resonant modes of a bearing and the system modes produced from fault source are usually unknown, and so there is a need for robust global search methods able to deal with non-linear problems with multiple optima. Instead of applying an optimization method repeatedly for every optimum, non-dominated extensions of the genetic algorithm can be applied only one time to find and maintain multiple optimal solutions. The efficiency of the proposed approach – niching genetic algorithm with fitness sharing – was evaluated using vibration signals acquired on four tapered roller bearings with defined combinations of seeded faults.