Meta-cognitive Regression Neural Network for function approximation: Application to Remaining Useful Life estimation

Abstract

In this paper, we present a novel approach for Remaining Useful Life (RUL) estimation problem in prognostics using a proposed `sequential learning Meta-cognitive Regression Neural Network (McRNN) algorithm for function approximation'. The McRNN has two components, namely, a cognitive component and a meta-cognitive components. The cognitive component is an evolving single hidden layer Radial Basis Function (RBF) network with Gaussian activation functions. The meta-cognitive component present in McRNN helps to cognitive component in selecting proper samples to learn based on its current knowledge and evolve architecture automatically. The McRNN employs extended Kalman Filter (EKF) to find optimal network parameters in training. First, the performance of the proposed sequential learning McRNN algorithm has been evaluated using a set of benchmark function approximation problems and is compared with existing sequential learning algorithms. The performance results on these problems show the better performance of McRNN algorithm over the other algorithms. Next, the proposed McRNN algorithm has been applied to RUL estimation problem based on sensor data. For simulation studies, we have used Prognostics Health Management (PHM) 2008 Data Challenge data set and compared with the existing approaches based on state-of-the-art regression algorithms. The experimental results show that our proposed McRNN algorithm based approach can accurately estimate RUL of the system.