Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes a...
Rolling bearings are critical components in industrial machinery, and their failures can lead to equipment downtime or safety hazards, making accurate fault diagnosis vital. While data-driven intelligent methods perform well with sufficient labeled d...
With the development of science and technology, hyperspectral image (HSI) classification has been studied in depth by researchers as one of the important means of human cognition in living environments and the exploration of surface information. Neve...
Deep neural networks have been successfully applied in domain adaptation which uses the labeled data of source domain to supplement useful information for target domain. Deep Adaptation Network (DAN) is one of these efficient frameworks, it utilizes...
Accurate remaining useful life (RUL) prediction of rolling bearings plays a critical role in predictive maintenance. However, existing methods face challenges in extracting and fusing multi-source spatiotemporal features, addressing distribution diff...
Currently, huge quantities of remote sensing images (RSIs) are becoming available. Nevertheless, the scarcity of labeled samples hinders the semantic understanding of RSIs. Fortunately, many ground-level image datasets with detailed semantic annotati...
The diagnosis of rolling bearing faults has become an increasingly popular research topic in recent years. However, many studies have been conducted based on sufficient training data. In the real industrial scene, there are some problems in bearing f...
Data-driven bearing-fault diagnosis methods have become a research hotspot recently. These methods have to meet two premises: (1) the distributions of the data to be tested and the training data are the same; (2) there are a large number of high-qual...
Aiming to tackle the challenge of feature transfer in cross-domain fault diagnosis for rolling bearings, an enhanced domain adaptation-based intelligent fault diagnosis method is proposed. This method systematically combines multi-layer multi-core MM...
In real-world scenarios, the rotational speed of bearings is variable. Due to changes in operating conditions, the feature distribution of bearing vibration data becomes inconsistent, which leads to the inability to directly apply the training model...