Localized multi-kernel diagnosis model for avionics based on affinity propagation clustering

In consideration of the low diagnosis accuracy for avionics functional module fault, a new offline localized clustering multi-kernel extreme learning machine (LCMKELM) diagnosis model is proposed in this paper by combining the capabilities of multi-resolution interpretation and local feature self-adaptive representation from localized multi-kernel learning (LMKL) with the characteristic of high-performance operation from extreme learning machine (ELM). In order to avoid overfitting issue, affinity propagation (AP) clustering is used to make full use of the underlying localities in the training data and effectively reduce the computational complexity. Considering that the updating of localized kernel weights in dual optimization form of kernel ELM (KELM)is a difficult quadratic nonconvex problem, gating function M₁ and M₂ are respectively constructed to approximate localized weights by analyzing the clustering characteristics in input space and feature space. The proposed method is applied to actual fault diagnosis task of rotary transformer excitation generating circuit, and the experimental results show that the proposed method has the lower false alarm rate and missing alarm rate in comparison with four state-of-the-art multi-kernel learning algorithms, and meanwhile the diagnosis accuracy is averagely increased by 3.80% when M₁ gating model is used, and increased by 5.98% when M₂ gating model is used. Moreover, compared with canonical LMKL algorithms, the proposed method obtains similar training time cost, but it has less testing time cost.