Zhang et al., 2024 - Google Patents
Deep adaptive sparse residual networks: A lifelong learning framework for rotating machinery fault diagnosis with domain incrementsZhang et al., 2024
- Document ID
- 16839034084265432288
- Author
- Zhang Y
- Shen C
- Shi J
- Li C
- Lin X
- Zhu Z
- Wang D
- Publication year
- Publication venue
- Knowledge-Based Systems
External Links
Snippet
Rotating machinery operates continuously for long periods of time under varying conditions in actual industrial environments. The number of fault samples increases with equipment operating time, whereas differences in data distribution are inevitable because of varying …
- 238000003745 diagnosis 0 title abstract description 66
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Pang et al. | Spatio-temporal fusion neural network for multi-class fault diagnosis of wind turbines based on SCADA data | |
Xu et al. | Online fault diagnosis method based on transfer convolutional neural networks | |
Zhang et al. | A fault diagnosis method for wind turbines gearbox based on adaptive loss weighted meta-ResNet under noisy labels | |
Huang et al. | Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion | |
Haidong et al. | Intelligent fault diagnosis of rolling bearing using deep wavelet auto-encoder with extreme learning machine | |
Li et al. | Self-attention ConvLSTM and its application in RUL prediction of rolling bearings | |
Zhang et al. | A new bearing fault diagnosis method based on modified convolutional neural networks | |
Zhang et al. | Data-driven methods for predictive maintenance of industrial equipment: A survey | |
Jamil et al. | A deep boosted transfer learning method for wind turbine gearbox fault detection | |
Zhao et al. | Intelligent fault diagnosis of multichannel motor–rotor system based on multimanifold deep extreme learning machine | |
Lu et al. | Deep model based domain adaptation for fault diagnosis | |
Cao et al. | Fault diagnosis of wind turbine gearbox based on deep bi-directional long short-term memory under time-varying non-stationary operating conditions | |
Lei et al. | Prior knowledge-embedded meta-transfer learning for few-shot fault diagnosis under variable operating conditions | |
Li et al. | Joint attention feature transfer network for gearbox fault diagnosis with imbalanced data | |
Lu et al. | A deep adversarial learning prognostics model for remaining useful life prediction of rolling bearing | |
Zhang et al. | Deep adaptive sparse residual networks: A lifelong learning framework for rotating machinery fault diagnosis with domain increments | |
Shen et al. | Recognition of rolling bearing fault patterns and sizes based on two-layer support vector regression machines | |
Kumar et al. | The Importance of Feature Processing in Deep‐Learning‐Based Condition Monitoring of Motors | |
Bi et al. | A comprehensive survey on applications of AI technologies to failure analysis of industrial systems | |
Li et al. | Digital twin-assisted dual transfer: A novel information-model adaptation method for rolling bearing fault diagnosis | |
Kibrete et al. | Applications of artificial intelligence for fault diagnosis of rotating machines: A review | |
Yang et al. | LSTA-Net framework: pioneering intelligent diagnostics for insulating bearings under real-world complex operational conditions and its interpretability | |
Liu et al. | Semi-supervised deep learning recognition method for the new classes of faults in wind turbine system | |
Chen et al. | Fault Detection for Turbine Engine Disk Based on Adaptive Weighted One‐Class Support Vector Machine | |
Yu et al. | Feature Extraction Based on Self-Supervised Learning for Remaining Useful Life Prediction |