Ochella et al., 2022 - Google Patents
Artificial intelligence in prognostics and health management of engineering systemsOchella et al., 2022
View PDF- Document ID
- 5697928442265265340
- Author
- Ochella S
- Shafiee M
- Dinmohammadi F
- Publication year
- Publication venue
- Engineering Applications of Artificial Intelligence
External Links
Snippet
Prognostics and health management (PHM) has become a crucial aspect of the management of engineering systems and structures, where sensor hardware and decision support tools are deployed to detect anomalies, diagnose faults and predict remaining …
- 238000000034 method 0 abstract description 76
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
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
- G06Q10/0635—Risk analysis
-
- 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
- G06N3/08—Learning methods
-
- 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
-
- 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
-
- 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
- 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
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F15/00—Digital computers in general; Data processing equipment in general
- G06F15/18—Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Ochella et al. | Artificial intelligence in prognostics and health management of engineering systems | |
Zio | Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice | |
Xu et al. | Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges | |
Rigamonti et al. | Ensemble of optimized echo state networks for remaining useful life prediction | |
Saxena et al. | Metrics for offline evaluation of prognostic performance | |
Nguyen et al. | Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems | |
Wang et al. | A Bayesian inference-based approach for performance prognostics towards uncertainty quantification and its applications on the marine diesel engine | |
Al-Dulaimi et al. | NBLSTM: Noisy and hybrid convolutional neural network and BLSTM-Based deep architecture for remaining useful life estimation | |
Azar et al. | Semi-supervised clustering-based method for fault diagnosis and prognosis: A case study | |
Keshun et al. | Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning | |
Xiong et al. | Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions | |
Hu et al. | System reliability prediction model based on evidential reasoning algorithm with nonlinear optimization | |
Boujamza et al. | Attention-based LSTM for remaining useful life estimation of aircraft engines | |
Schwartz et al. | An unsupervised approach for health index building and for similarity-based remaining useful life estimation | |
Colo et al. | Intelligent approach for the industrialization of deep learning solutions applied to fault detection | |
Li et al. | Intelligent reliability and maintainability of energy infrastructure assets | |
Wang et al. | Three‐stage feature selection approach for deep learning‐based RUL prediction methods | |
Xiahou et al. | Bayesian dual-input-channel LSTM-based prognostics: Toward uncertainty quantification under varying future operations | |
Li et al. | A survey of deep learning-driven architecture for predictive maintenance | |
Velasco-Gallego et al. | Mar-RUL: A remaining useful life prediction approach for fault prognostics of marine machinery | |
Hu et al. | Estimate remaining useful life for predictive railways maintenance based on LSTM autoencoder | |
Gupta et al. | A critical review on system architecture, techniques, trends and challenges in intelligent predictive maintenance | |
Huang et al. | Prognostics and health management for predictive maintenance: A review | |
Azyus et al. | Prediction of remaining useful life using the CNN-GRU network: A study on maintenance management | |
Rajaoarisoa et al. | Hybrid and co-learning approach for anomalies prediction and explanation of wind turbine systems |