Vanem et al., 2018 - Google Patents
Cluster-based anomaly detection in condition monitoring of a marine engine systemVanem et al., 2018
View PDF- Document ID
- 11129662537653491394
- Author
- Vanem E
- Brandsæter A
- Publication year
- Publication venue
- 2018 Prognostics and System Health Management Conference (PHM-Chongqing)
External Links
Snippet
Sensor data can be used to detect changes in the performance of a system in near real-time which may be indicative of a system fault. However, there is a need for efficient and robust algorithms to detect such changes in the data streams. In this paper, sensor data from a …
- 238000001514 detection method 0 title abstract description 67
Classifications
-
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
-
- 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
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6251—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on a criterion of topology preservation, e.g. multidimensional scaling, self-organising maps
-
- 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
- G05B23/0224—Process history based detection method, e.g. whereby history implies the availability of large amounts of data
- G05B23/024—Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks
-
- 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
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- 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
- G05B23/0254—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 based on a quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks
-
- 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 |
---|---|---|
Shen et al. | Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines | |
Vanem et al. | Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine | |
Chao et al. | Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models | |
Li et al. | Data-driven bearing fault identification using improved hidden Markov model and self-organizing map | |
CN115034248A (en) | Automatic diagnostic method, system and storage medium for equipment | |
Brandsæter et al. | Efficient on-line anomaly detection for ship systems in operation | |
CN108062586B (en) | Ship host machine associated component state monitoring method and system based on decline contribution degree | |
CN111241673A (en) | Health state prediction method for industrial equipment in noisy environment | |
GB2614026A (en) | System and method for training an autoencoder to detect anomalous system behaviour | |
Zemouri et al. | Recent research and applications in variational autoencoders for industrial prognosis and health management: A survey | |
Vanem et al. | Cluster-based anomaly detection in condition monitoring of a marine engine system | |
US20240362463A1 (en) | System and method for training an autoencoder to detect anomalous system behavior | |
Chen et al. | Simultaneous fault type and severity identification using a two-branch domain adaptation network | |
CN115791174A (en) | Rolling bearing abnormity diagnosis method and system, electronic equipment and storage medium | |
Loukopoulos et al. | Dealing with missing data as it pertains of e-maintenance | |
Frisk et al. | Residual selection for consistency based diagnosis using machine learning models | |
Moddemann et al. | Discret2Di--Deep Learning based Discretization for Model-based Diagnosis | |
EP4152210A1 (en) | System and method for training an autoencoder to detect anomalous system behaviour | |
Wang et al. | Fault detection for the class imbalance problem in semiconductor manufacturing processes | |
CN114818116B (en) | Aircraft engine failure mode identification and life prediction method based on joint learning | |
Arias Chao | Combining deep learning and physics-based performance models for diagnostics and prognostics | |
Wei et al. | Multi-sensor monitoring based on-line diesel engine anomaly detection with baseline deviation | |
Won et al. | Prediction of remaining useful lifetime of membrane using machine learning | |
Wang et al. | Health indicator forecasting for improving remaining useful life estimation | |
Fezai et al. | Online statistical hypothesis test for leak detection in water distribution networks |