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Intelligent rice disease recognition methods based on deep neural networks can predict the degree of disease on the basis of, for example, the number of disease spots on an image, so that preventive measures can be taken. Currently, intelligent recognition methods for rice diseases suffer from the disadvantages of poor versatility and low accuracy. This paper uses eight common image classification networks to classify and identify four rice diseases. ResNet50 was selected as the feature extraction network and an enhanced feature fusion and target adaptive network (EFFTAN), referred to as EFFTAN, is proposed. The EFFTAN was used to detect four rice spot diseases in the rice leaf disease image samples dataset; the mean average precision of the final detection was 95.3%, and effective detection was also achieved for the dense spot features. [ABSTRACT FROM AUTHOR]

Bearing is a key component in rotary machines. Their failures may cause the abrupt shutdown of these machines, which would result in substantial economic losses. Therefore, the prediction of the remaining useful life (RUL) of bearings is regarded as one of the critical approaches to avoid failure of bearings and their systems. In this article, an ensemble data-driven approach is proposed to predict the RUL of bearings. It uses feature extraction, an attention mechanism, and uncertainty analysis. First, the features embedded in the bearings’ vibration signals are extracted. Second, a stacked gated recurrent unit (GRU) is constructed to predict the bearing RUL. A novel attention mechanism based on dynamic time warping (DTW) is developed to improve the performance of information extraction, and a Bayesian approach is employed to analyze the prediction uncertainty. Finally, the proposed approach is validated using two benchmark-bearing data sets. The results show that the proposed approach can predict the bearing RUL effectively, and the prediction uncertainty can also be evaluated. [ABSTRACT FROM AUTHOR]

Deep learning (DL)-based methods for remaining useful life (RUL) prediction have received increasing research attention due to excellent feature extraction abilities. Most DL methods rely on abundant labeled samples for supervised training. However, because of the adoption of the over-maintenance strategy of equipment, the monitored data for the degradation of equipment usually consists of few labeled samples and a large amount of unlabeled samples, which limits the performance of DL methods. To take advantage of the value of unlabeled samples, this paper proposed a contrastive learning framework for RUL prediction. First, an unlabeled sample augmentation is developed firstly to extend the sample set. Then, an unlabeled sample learning (USL) architecture is proposed to learn the information of degradation from unlabeled samples to promote general DL models' performance on RUL prediction. Based on the proposed framework, USL-convolutional neural network and USL-long short-term memory network are used to validate its performance based on datasets of turbofan engine and bearing. Results show that the performance of RUL prediction based on the proposed framework can be enhanced by unlabeled samples and verify the good scalability and generalization ability of the proposed framework. • Proposed framework boosts RUL prediction with unlabeled samples despite few labeled. • Proposed framework is designed for general deep learning methods like CNN and RNN. • Two case analyses validated the effectiveness and generalization ability. [ABSTRACT FROM AUTHOR]