首页|p Finding the optimal multilayer network structure through reinforcement learning in fault diagnosis
p Finding the optimal multilayer network structure through reinforcement learning in fault diagnosis
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NSTL
Elsevier
Deep learning (DL) is an important method in industrial fault diagnosis. However, DL's network structure needs to be designed with experience. To simplify the design of network structures, we propose the neural architecture search network with Pareto efficiency reward and insert replay buffer (NAS-PERIRB) algorithm. In this paper, the early stopping and insert replay buffer (IRB) are used to improving the training efficiency of the samples. In addition, we design the Pareto efficiency reward function to optimize the goals and design a network search space to perform effective searches. What is more, we evaluate the NAS-PERIRB under two datasets. Results show that the two datasets have reached 99% accuracy in various situations, which means the NAS-PERIRB can achieve the purpose of designing the network structure independently.
NSTL
Elsevier
