  The performance of a deep neural network (DNN) for fault diagnosis is very
much dependent on the network architecture. Also, the diagnostic performance is
reduced if the model trained on a laboratory case machine is used on a test
dataset from an industrial machine running under variable operating conditions.
Thus, there are two challenges for the intelligent fault diagnosis of
industrial machines: (i) selection of suitable DNN architecture and (ii) domain
adaptation for the change in operating conditions. Therefore, we propose an
evolutionary Net2Net transformation (EvoN2N) that finds the best suitable DNN
architecture for the given dataset. Non-dominated sorting genetic algorithm II
has been used to optimize the depth and width of the DNN architecture. Also, we
have introduced a hybrid crossover technique for optimization of the depth and
width of the deep neural network encoded in a chromosome. We have formulated a
knowledge transfer-based fitness evaluation scheme for faster evolution. The
proposed framework can obtain the best model for intelligent fault diagnosis
without the need for a long-time-taking search process. We have used the Case
Western Reserve University dataset, Paderborn university dataset, and gearbox
fault detection dataset to demonstrate the effectiveness of the proposed
framework for the selection of the best suitable architecture capable of
excellent diagnostic performance, classification accuracy almost up to 100%
