One-shot neural architecture search for fault diagnosis using vibration signals

Abstract Machine learning method has been widely applied in industrial fault diagnosis, especially the deep learning method. In the field of industrial fault diagnosis, deep learning is mostly used to extract features of vibration signals to achieve end-to-end fault diagnosis systems. Due to the complexity and variety of actual industrial datasets, some deep learning models are designed to be complicated. However, designing neural network architectures requires rich professional knowledge, experience, and a large number of experiments, increasing the difficulty of developing deep learning models. Fortunately, Neural Architecture Search (NAS), a branch of Automated Machine Learning (AutoML), is developing rapidly. Given a search space, NAS can search for networks that perform better than manually designed. In this paper, a one-shot NAS method for fault diagnosis is proposed. The one-shot model is a supernet that contains all candidate networks in a given search space. The supernet is trained to evaluate the actual performance of candidate networks by measuring the difference between its output probability and the true labels. According to the prediction of supernet, the networks with excellent performance can be searched, using some common search methods such as random search or evolutionary algorithm. Finally, the searched network is trained by reusing the weights of the supernet. To evaluate the proposed method, two search spaces are designed, ResNet and Inception search spaces, to search on PHM 2009 Data Challenge gearbox dataset. The state-of-the-art results are obtained, and accuracies of searched ResNet-A and Inception-A are 84.11% and 83.81%, which are 3.29% and 10.88% higher than Reinforcement Learning based NAS.