Weighted feature selection with growing neural networks for the FDD of rolling element bearings

This paper suggests an automated approach for fault detection, diagnosis and identification of roller bearings, which is based on optimized form of growing neural networks. In our recent work, we selected features according to their classification accuracy within supervised learning stage. Since each one of selected features has different effect on classification decision, a weighted feature selection is put forward in this paper to improve the network taxonomy. This is followed by a self adaptive growing neural network that optimizes its architecture by adding or updating hidden nodes to fulfill the training requirements. This pattern recognition procedure used to recognize between signals coming from normal bearings and those generated from different industrial bearing faults. The developed approach is compared with two different types of supervised neural networks. Results demonstrate that the developed diagnostic approach can reliably separate different bearing fault conditions at various rotational speeds.