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

Zaitsev et al., 2024 - Google Patents

Predictive Diagnostic and State Classification of the Inertial Navigation System Using ML

Zaitsev et al., 2024

Document ID
5458541340641413578
Author
Zaitsev O
Kruchkov R
Publication year
Publication venue
2024 International Russian Automation Conference (RusAutoCon)

External Links

Snippet

The problem of inertial navigation system (INS) state classification and remaining useful life prediction is considered within the framework of the Machine learning approach; the proposed LSTM and gradient boosting algorithms for its solution is described. The initial …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • 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
    • 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
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • 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
    • 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
    • G06N5/02—Knowledge representation
    • G06N5/022—Knowledge engineering, knowledge acquisition
    • 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
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • 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

Similar Documents

Publication Publication Date Title
Li et al. Data alignments in machinery remaining useful life prediction using deep adversarial neural networks
Khelif et al. RUL prediction based on a new similarity-instance based approach
CN112131212A (en) Anomaly prediction method of time series data for hybrid cloud scenarios based on ensemble learning technology
Darong et al. Fault diagnosis for the motor drive system of urban transit based on improved Hidden Markov Model
Wachla et al. A method of leakage location in water distribution networks using artificial neuro-fuzzy system
Cross et al. Model-based and fuzzy logic approaches to condition monitoring of operational wind turbines
Losi et al. Anomaly detection in gas turbine time series by means of Bayesian hierarchical models
Luan et al. Out-of-distribution detection for deep neural networks with isolation forest and local outlier factor
Mathew et al. Regression kernel for prognostics with support vector machines
Gouriveau et al. Strategies to face imbalanced and unlabelled data in PHM applications.
Jackson et al. Strategy synthesis for partially-known switched stochastic systems
Wang et al. A remaining useful life prediction model based on hybrid long-short sequences for engines
Lu et al. Assessment of data-driven, machine learning techniques for machinery prognostics of offshore assets
Pérez-Pérez et al. Robust fault diagnosis of wind turbines based on MANFIS and zonotopic observers
Li et al. Index similarity assisted particle filter for early failure time prediction with applications to turbofan engines and compressors
Ray et al. Learning graph neural networks for multivariate time series anomaly detection
Velasco-Gallego et al. Mar-RUL: A remaining useful life prediction approach for fault prognostics of marine machinery
Tirovolas et al. Introducing fuzzy cognitive map for predicting engine’s health status
Brosinsky et al. Machine learning and digital twins: monitoring and control for dynamic security in power systems
Dang et al. seq2graph: Discovering dynamic non-linear dependencies from multivariate time series
CN117875421B (en) Deep learning-based photovoltaic array fault diagnosis method and system
Hu et al. Estimate remaining useful life for predictive railways maintenance based on LSTM autoencoder
Ingole et al. Investigation of Different Regression Models For The Predictive Maintenance of Aircraft's Engine
Farahani et al. A novel method for designing transferable soft sensors and its application
Zaitsev et al. Predictive Diagnostic and State Classification of the Inertial Navigation System Using ML