Nothing Special   »   [go: up one dir, main page]

Zhang et al., 2020 - Google Patents

Few-shot bearing anomaly detection based on model-agnostic meta-learning

Zhang 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 …
Continue reading at www.researchgate.net (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • G06N5/025Extracting rules from data
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218Electric 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
    • G05B23/0243Electric 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0275Fault isolation and identification, e.g. classify fault; estimate cause or root of failure
    • G05B23/0278Qualitative, e.g. if-then rules; Fuzzy logic; Lookup tables; Symptomatic search; FMEA
    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00Testing or monitoring of control systems or parts thereof
    • G05B23/02Electric testing or monitoring
    • G05B23/0205Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283Predictive 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]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6267Classification techniques
    • G06K9/6279Classification techniques relating to the number of classes
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic 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
Cai et al. Data-driven early fault diagnostic methodology of permanent magnet synchronous motor
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
Alzghoul et al. On the Usefulness of Pre-processing Methods in Rotating‎ Machines Faults Classification using Artificial Neural Network
CN111241673A (en) Health state prediction method for industrial equipment in noisy environment
KR102321607B1 (en) Rotating machine fault detecting apparatus and method
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
Alfarizi et al. An extreme gradient boosting aided fault diagnosis approach: A case study of fuse test bench
CN114037478A (en) Advertisement abnormal flow detection method and system, electronic equipment and readable storage medium
CN115453356A (en) Power equipment running state monitoring and analyzing method, system, terminal and medium
van den Hoogen et al. An improved wide-kernel cnn for classifying multivariate signals in fault diagnosis
Yousefi et al. Intelligent fault diagnosis of manufacturing processes using extra tree classification algorithm and feature selection strategies
Decker et al. Does your model think like an engineer? explainable ai for bearing fault detection with deep learning
Stetco et al. Wind Turbine operational state prediction: Towards featureless, end-to-end predictive maintenance
Liu et al. Incremental bearing fault diagnosis method under imbalanced sample conditions
Zheng et al. Few-shot intelligent fault diagnosis based on an improved meta-relation network
AlThobiani A Novel Framework for Robust Bearing Fault Diagnosis: Preprocessing, Model Selection, and Performance Evaluation
Guerroum et al. Machine learning for the predictive maintenance of a Jaw Crusher in the mining industry
Nguyen Feature Engineering and Health Indicator Construction for Fault Detection and Diagnostic
Hameed et al. Fault detection in single-stage helical planetary gearbox using support vector machine (svm) and artificial neural network (ann) with statistical features
Tai et al. A Physics-based Model-data-driven Method for Spindle Health Diagnosis, Part III: Model Training and Fault Detection
Zhang et al. Fractional derivative kernel recursive generalized maximum correntropy for RUL prediction of rolling bearings