Neural Architecture Search For Fault Diagnosis
This work addresses the difficulty of developing deep learning models for fault diagnosis by automating architecture design, though it is incremental as it applies an existing NAS approach to a new domain.
The paper tackled the challenge of designing neural network architectures for fault diagnosis by proposing a neural architecture search (NAS) method using reinforcement learning, achieving state-of-the-art results on the PHM 2009 gearbox dataset.
Data-driven methods have made great progress in fault diagnosis, especially deep learning method. Deep learning is suitable for processing big data, and has a strong feature extraction ability to realize end-to-end fault diagnosis systems. However, designing neural network architecture requires rich professional knowledge and debugging experience, and a lot of experiments are needed to screen models and hyperparameters, increasing the difficulty of developing deep learning models. Frortunately, neural architecture search (NAS) is developing rapidly, and is becoming one of the next directions for deep learning. In this paper, we proposed a NAS method for fault diagnosis using reinforcement learning. A recurrent neural network is used as an agent to generate network architecture. The accuracy of the generated network on the validation dataset is fed back to the agent as a reward, and the parameters of the agent are updated through the strategy gradient algorithm. We use PHM 2009 Data Challenge gearbox dataset to prove the effectiveness of proposed method, and obtain state-of-the-art results compared with other artificial designed network structures. To author's best knowledge, it's the first time that NAS has been applied in fault diagnosis.