The advances of intelligent fault diagnosis in recent years show that deep learning has strong capability of automatic feature extraction and accurate identification for fault signals. Nevertheless, data scarcity and varying working conditions can degrade the performance of the model. More recently, a tool has been proposed to address the above challenges simultaneously. Meta-learning, also known as learning to learn, uses a small sample to quickly adapt to a new task. It has great application potential in few-shot and cross-domain fault diagnosis, and thus has become a promising tool. However, there is a lack of a survey to conclude existing work and look into the future. This paper comprehensively investigates deep meta-learning in fault diagnosis from three views: (i) what to use, (ii) how to use, and (iii) how to develop, i.e. algorithms, applications, and prospects. Algorithms are illustrated by optimization-, metric-, and model-based methods, the applications are concluded in few-shot cross-domain fault diagnosis, and open challenges, as well as prospects, are given to motivate the future work. Additionally, we demonstrate the performance of three approaches on two few-shot cross-domain tasks. Typical meta-learning methods are implemented and available at https://github.com/fyancy/MetaFD .

Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects

Kh 2 po 4 型結晶の複屈折と構造との關係

A survey of transfer learning for machinery diagnostics and prognostics

As a promising tool for intelligent diagnosis of rotating machinery with unlabeled data, transfer learning (TL) has attracted considerable attentions from academia and industry. However, mechanical data in real-case have obviously unlabeled and imbalanced characteristics, which are not simultaneously concerned enough by existing intelligent TL fault diagnosis methods. Specially, the highly imbalanced mechanical data lead to the skewed marginal distribution and classifier, which makes a huge challenge for the TL-based intelligent fault diagnosis method. Self-updating knowledge via tradeoff of believing, doubting and rectifying is a main learning process of human being, from which the previous experience is of great importance to enhance the learning ability. Inspired by the learning strategy of human being, a self-learning transferable neural network (STNN) is proposed for the intelligent machinery fault diagnosis with unlabeled and imbalanced data in this study. Three novel loss terms are constructed into STNN for realizing the self-believing, doubting and rectifying in the prediction of health conditions. First one is the self-believing loss term, which uses the conditional distribution adaptation to align the learned cross-domain features proactively. Second one is the self-doubting loss term that provides the ability of freeing from the false experience for STNN. Third one is the self-rectifying loss term, in which the information entropy is employed to regulate the learning process of STNN. Two experimental cases of rotating machinery show that the effectiveness and superiority of the proposed STNN in enhancing the intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data.

Self-learning transferable neural network for intelligent fault diagnosis of rotating machinery with unlabeled and imbalanced data

Efficient federated convolutional neural network with information fusion for rolling bearing fault diagnosis

Intelligent fault diagnosis of rolling element bearings gains increasing attention in recent years due to the promising development of artificial intelligent technology. Many intelligent diagnosis methods work well requiring massive historical data of the diagnosed object. However, it is hard to get sufficient fault data in advance in real diagnosis scenario and the diagnosis model constructed on such small dataset suffers from serious overfitting and losing the ability of generalization, which is described as small sample problem in this paper. Focus on the small sample problem, this paper proposes a new intelligent fault diagnosis framework based on dynamic model and transfer learning for rolling element bearings race faults. In the proposed framework, dynamic model of bearing is utilized to generate massive and various simulation data, then the diagnosis knowledge learned from simulation data is leveraged to real scenario based on convolutional neural network (CNN) and parameter transfer strategies. The effectiveness of the proposed method is verified and discussed based on three fault diagnosis cases in detail. The results show that based on the simulation data and parameter transfer strategies in CNN, the proposed method can learn more transferable features and reduce the feature distribution discrepancy, contributing to enhancing the fault identification performance significantly.

A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem.

Yunjia Dong

Papers

A new dynamic model and transfer learning based intelligent fault diagnosis framework for rolling element bearings race faults: Solving the small sample problem.