Zhang et al., 2020 - Google Patents
Few-shot bearing anomaly detection based on model-agnostic meta-learningZhang et al., 2020
View PDF- Document ID
- 1045500740036225826
- Author
- Zhang S
- Ye F
- Wang B
- Habetler T
- Publication year
- Publication venue
- arXiv preprint arXiv:2007.12851
External Links
Snippet
The rapid development of artificial intelligence and deep learning technology has provided many opportunities to further enhance the safety, stability, and accuracy of industrial Cyber- Physical Systems (CPS). As indispensable components to many mission-critical CPS assets …
- 238000001514 detection method 0 title abstract description 15
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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
- G06N5/025—Extracting rules from data
-
- 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
- G05B23/0243—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 model based detection method, e.g. first-principles knowledge model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- 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
- 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
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0275—Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
- G05B23/0278—Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
-
- 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
- G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
- G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | Few-shot bearing fault diagnosis based on model-agnostic meta-learning | |
Zhang et al. | Few-shot bearing anomaly detection via model-agnostic meta-learning | |
Zhang et al. | Machinery fault diagnosis with imbalanced data using deep generative adversarial networks | |
Cai et al. | Data-driven early fault diagnostic methodology of permanent magnet synchronous motor | |
Yin et al. | Wasserstein Generative Adversarial Network and Convolutional Neural Network (WG‐CNN) for Bearing Fault Diagnosis | |
CN110388315B (en) | Oil transfer pump fault identification method, device and system based on multi-source information fusion | |
CN110059775A (en) | Rotary-type mechanical equipment method for detecting abnormality and device | |
KR102321607B1 (en) | Rotating machine fault detecting apparatus and method | |
Alzghoul et al. | On the Usefulness of Pre-processing Methods in Rotating Machines Faults Classification using Artificial Neural Network | |
Du et al. | Degradation process prediction for rotational machinery based on hybrid intelligent model | |
Tayyab et al. | Intelligent fault diagnosis of rotating machine elements using machine learning through optimal features extraction and selection | |
US20240103950A1 (en) | Method, computing device and computer program for detecting abnormal behavior of process equipment | |
Zhang et al. | Few-shot bearing anomaly detection based on model-agnostic meta-learning | |
Calabrese et al. | An event based machine learning framework for predictive maintenance in industry 4.0 | |
van den Hoogen et al. | An improved wide-kernel cnn for classifying multivariate signals in fault diagnosis | |
Decker et al. | Does your model think like an engineer? explainable ai for bearing fault detection with deep learning | |
Yousefi et al. | Intelligent fault diagnosis of manufacturing processes using extra tree classification algorithm and feature selection strategies | |
AlThobiani | A novel framework for robust bearing fault diagnosis: preprocessing, model selection, and performance evaluation | |
Zheng et al. | Few-shot intelligent fault diagnosis based on an improved meta-relation network | |
Liu et al. | Incremental bearing fault diagnosis method under imbalanced sample conditions | |
Stetco et al. | Wind Turbine operational state prediction: Towards featureless, end-to-end predictive maintenance | |
CN113486926B (en) | An automated terminal equipment anomaly detection system | |
CN117407784A (en) | Intelligent fault diagnosis method and system for rotating machinery for abnormal sensor data | |
Zhang et al. | Fractional derivative kernel recursive generalized maximum correntropy for RUL prediction of rolling bearings | |
Kumari et al. | Remaining useful life prediction using hybrid neural network and genetic algorithm approaches |