A Power Data Anomaly Detection Model Based on Deep Learning with Adaptive Feature Fusion
Source abstract
Abstract
With the popularisation of intelligent power, power devices have different shapes, numbers and specifications. This means that the power data has distributional variability, the model learning process cannot achieve sufficien... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Smart Grid and Power Systems
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (34)
Show source-provided references
1. Multi-perspective social recommendation method with graph representation learning10.1016/j.neucom.2021.10.050 ↗
2. Orientation cues-aware facial relationship representation for head pose estimation via transformer10.1109/TIP.2023.3331309 ↗
3. A review of planetary and epicyclic gear dynamics and vibrations research10.1115/1.4027812 ↗
4. Application of spectral kurtosis for detection of a tooth crack in the planetary gear of a wind turbine
5. Wavelet analysis with time-synchronous averaging of planetary gearbox vibration data for fault detection and diagnostics
6. Vibration-based diagnostics of gearboxes under variable speed and load conditions10.1007/s11012-016-0583-z ↗
7. X. S. Lou, “Fault detection and diagnosis for rolling element bearing,” Ph.D. dissertation, Case Western Reserve Univ., USA, 2000.
8. P. Chen, “Bearing condition monitoring and fault diagnosis,” Ph.D. dissertation, The Univ. of Calgary, Canadian, 2000.
9. P. Xu, “Neural network based wheel bearing fault detection and diagnosis using wavelets,” Ph.D. dissertation, Texas A and M Univ., 2002.
10. Fault diagnosis based on dependent feature vector and probability neural network for rolling element bearings
11. Fault diagnosis of rolling bearings using agenetic algorithm optimized neural network10.1016/j.measurement.2014.08.041 ↗
12. Data-driven fault diagnosis method based on the conversion of erosion operation signals into images and convolutional neural network10.1016/j.psep.2021.03.016 ↗
13. A knowledge-guided and data-driven method for building HVAC systems fault diagnosis10.1016/j.buildenv.2021.107850 ↗
14. Fault diagnosis of rotating machincry based on recurrent neural networks10.1016/j.measurement.2020.108774 ↗
15. Bearing fault diagnosis based on statistical locally linear embedding10.3390/s150716225 ↗
16. Multiple-domain manifold for feature extraction in machinery fault diagnosis10.1016/j.measurement.2015.07.042 ↗
17. Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis
18. Independent component analysis: Algorithms and applications10.1016/S0893-6080(00)00026-5 ↗
19. Vibration signal classification by wavelet packet energy flow manifold learning10.1016/j.jsv.2012.11.006 ↗
20. An algorithm for finding intrinsic dimensionality of data10.1109/T-C.1971.223208 ↗
21. A global geometric framework for nonlinear dimensionality reduction10.1126/science.290.5500.2319 ↗
22. Laplacian Eigen maps for dimensionality reduction and data representation10.1162/089976603321780317 ↗
23. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model10.1016/j.ymssp.2015.11.022 ↗
24. Intelligent diagnosis method for rotating machinery using dictionary learning and singular value decomposition10.3390/s17040689 ↗
25. TransIFC: Invariant cues-aware feature concentration learning for efficient fine-grained bird image classification
26. LDCNet: Limb direction cues-aware network for flexible human pose estimation in industrial behavioral biometrics systems
27. EHPE: Skeleton cues-based gaussian coordinate encoding for efficient human pose estimation
28. Intelligent fault diagnosis of rolling bearing using hierarchical convolutional network based health state classification10.1016/j.aei.2017.02.005 ↗
29. A deep learning approach to anomaly sequence detection for high-resolution monitoring of power systems10.1109/TPWRS.2022.3168529 ↗
30. Rolling bearing fault detection using continuous deep belief network with locally linear embedding10.1016/j.compind.2018.01.005 ↗
31. A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders10.1016/j.ymssp.2017.09.026 ↗
32. On improving hotspot detection through synthetic pattern-based database enhancement10.1109/TCAD.2021.3049285 ↗
33. Federated learning in mobile edge networks: A comprehensive survey
34. Deep learning based recommender system: A survey and new perspectives10.1145/3158369 ↗