Motor fault diagnosis is a fundamental aspect of ensuring the reliability of industrial equipment. However, industrial scenarios exhibit an inherent data scarcity problem, which imposes significant restrictions on the practical application of traditional deep learning-based intelligent fault diagnosis (IFD) methods. Typically, only a small volume of labeled data along with limited informative unlabeled data are available from industrial motors. Effectively utilizing informative unlabeled samples in the context of few-shot fault diagnosis poses a substantial challenge. In this article, a prototype refinement method for semi-supervised few-shot fault diagnosis based on deep reinforcement learning (DRL) is proposed. First, we propose to formalize a Markov decision process (MDP) of an iterative semi-supervised meta-learning strategy involving the selection of informative unlabeled samples and the refinement of category prototypes. Subsequently, we develop a mirror prototypical network (ProtoNet) structure for interaction with a DRL agent, which learns to adaptively select valuable samples to supervise the diagnosis process. Moreover, a state space involving feature embedding and category information is designed, and a comprehensive reward taking into account selection confidence, effectiveness, and representative is proposed. Extensive experiments on several motor experimental datasets verify the method's effectiveness in few-shot diagnosis of unseen faults and new working conditions.

Machinery remaining useful life (RUL) prediction is an important task in condition-based maintenance. Data-driven methods have been widely studied and applied, however, almost all the researches learn degradation trends regardless of different fault conditions, which can lead to different degradation patterns. This article proposes a novel fault information assisted RUL prediction method based on a convolutional long short-term memory (LSTM) ensemble network, where fault conditions are obtained via fault knowledge transfer. Divergence minimization and domain adversarial adaptation are combined to transfer fault knowledge from a fault dataset to the run-to-failure data in a weakly supervised manner. With the predicted fault information, the RUL prediction network can learn various degradation patterns under different faults separately using a structure of multiple LSTMs. Then an ensemble strategy based onsoft fault conditionsis designed to get final RUL prediction results. Experiment on bearing datasets verifies the effectiveness of our proposed method.