/pdf/a-perspective-survey-on-deep-transfer-learning-for-fault-m5e3nrkv.pdf

A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

A novel method based on meta-learning for bearing fault diagnosis with small sample learning under different working conditions

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

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. 

In the engineering practice, lacking of data especially labeled data typically hinders the wide application of deep learning in mechanical fault diagnosis. However, collecting and labeling data is often expensive and time-consuming. To address this problem, a kind of semi-supervised meta-learning networks (SSMN) with squeeze-and-excitation attention is proposed for few-shot fault diagnosis in this paper. SSMN consists of a parameterized encoder, a non-parameterized prototype refinement process and a distance function. Based on attention mechanism, the encoder is able to extract distinct features to generate prototypes and enhance the identification accuracy. With semi-supervised few-shot learning, SSMN utilizes unlabeled data to refine original prototypes for better fault recognition. A combinatorial learning optimizer is designed to optimize SSMN efficiently. The effectiveness of the proposed method is demonstrated through three bearing vibration datasets and the results indicate the outstanding adaptability in different situations. Comparison with other approaches is also made under the same setup and the experimental results prove the superiority of the proposed method for few-shot fault diagnosis.

Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.

With wide applications of intelligent methods in mechanical fault diagnosis, satisfactory results have been achieved. However, complicated and diverse practical working conditions would significantly reduce the performance of the diagnostic model that works well in the laboratory, i.e. domain shift occurs. To address the problem, this paper proposed a novel similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification. The proposed domain-adversarial similarity-based meta-learning network (DASMN) consists of three modules: a feature encoder, a classifier and a domain discriminator. First, the encoder and the classifier implement the similarity-based meta-learning algorithm, in while the good generalization ability for unseen tasks is obtained. Then, adversarial domain adaptation is conducted by minimizing and maximizing the domain-discriminative error adversarially, which takes unlabeled source data and target data as inputs. The effectiveness of DASMN is evaluated by multiple cross-domain cases using three bearing vibration datasets and is compared with five well-established methods. Experimental results demonstrate the availability and outstanding generalization ability of the proposed method for cross-domain fault identification.

Similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification

Triplet metric driven multi-head GNN augmented with decoupling adversarial learning for intelligent fault diagnosis of machines under varying working condition

Yong Feng

Papers

Semi-supervised meta-learning networks with squeeze-and-excitation attention for few-shot fault diagnosis.

Similarity-based meta-learning network with adversarial domain adaptation for cross-domain fault identification

Triplet metric driven multi-head GNN augmented with decoupling adversarial learning for intelligent fault diagnosis of machines under varying working condition