A novel semi-supervised generative adversarial network based on the actor-critic algorithm for compound fault recognition

Xuan, Jianhua; Shi, Tielin

doi:10.1007/s00521-022-07011-z

Cited by 9 publications

(1 citation statement)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tao et al [ 24 ] generated pseudo-cluster labels for labeled and unlabeled data by adopting density peak clustering strategies. In addition, deep generative models were often utilized to generate new samples for labeled minority fault samples, such as GAN [ 25 , 26 , 27 , 28 ] and VAE [ 29 , 30 ]. Difficulties arise, however, when the quality of generated samples should be ensured to implement the data augmentation-based strategies.…”

Section: Introductionmentioning

confidence: 99%

Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions

Yang,

Chen,

et al. 2023

Sensors

View full text Add to dashboard Cite

The lack of labeled data and variable working conditions brings challenges to the application of intelligent fault diagnosis. Given this, extracting labeled information and learning distribution-invariant representation provides a feasible and promising way. Enlightened by metric learning and semi-supervised architecture, a triplet-guided path-interaction ladder network (Tri-CLAN) is proposed based on the aspects of algorithm structure and feature space. An encoder–decoder structure with path interaction is built to utilize the unlabeled data with fewer parameters, and the network structure is simplified by CNN and an element additive combination activation function. Metric learning is introduced to the feature space of the established algorithm structure, which enables the mining of hard samples from extremely limited labeled data and the learning of working condition-independent representations. The generalization and applicability of Tri-CLAN are proved by experiments, and the contribution of the algorithm structure and the metric learning in the feature space are discussed.

show abstract

Section: Introductionmentioning

confidence: 99%

Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions

Yang,

Chen,

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults

Maestro-Prieto,

Ramírez-Sanz,

Bustillo

et al. 2024

Appl Intell

View full text Add to dashboard Cite

Both wear-induced bearing failure and misalignment of the powertrain between the rotor and the electrical generator are common failure modes in wind-turbine motors. In this study, Semi-Supervised Learning (SSL) is applied to a fault detection and diagnosis solution. Firstly, a dataset is generated containing both normal operating patterns and seven different failure classes of the two aforementioned failure modes that vary in intensity. Several datasets are then generated, maintaining different numbers of labeled instances and unlabeling the others, in order to evaluate the number of labeled instances needed for the desired accuracy level. Subsequently, different types of SSL algorithms and combinations of algorithms are trained and then evaluated with the test data. The results showed that an SSL approach could improve the accuracy of trained classifiers when a small number of labeled instances were used together with many unlabeled instances to train a Co-Training algorithm or combinations of such algorithms. When a few labeled instances (fewer than 10% or 327 instances, in this case) were used together with unlabeled instances, the SSL algorithms outperformed the result obtained with the Supervised Learning (SL) techniques used as a benchmark. When the number of labeled instances was sufficient, the SL algorithm (using only labeled instances) performed better than the SSL algorithms (accuracy levels of 87.04% vs. 86.45%, when labeling 10% of instances). A competitive accuracy of 97.73% was achieved with the SL algorithm processing a subset of 40% of the labeled instances. Graphical abstract Steps and processes for approaching semi-supervised FDD of wind-turbine gearbox misalignment and imbalance faults

show abstract

Underwater image enhancement based on multiscale fusion generative adversarial network

Dai,

Wang,

Wang

et al. 2023

Int. J. Mach. Learn. & Cyber.

View full text Add to dashboard Cite

A novel semi-supervised generative adversarial network based on the actor-critic algorithm for compound fault recognition

Cited by 9 publications

References 36 publications

Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions

Metric Learning-Guided Semi-Supervised Path-Interaction Fault Diagnosis Method for Extremely Limited Labeled Samples under Variable Working Conditions

Semi-supervised diagnosis of wind-turbine gearbox misalignment and imbalance faults

Underwater image enhancement based on multiscale fusion generative adversarial network

Contact Info

Product

Resources

About