Devakumar et al., 2023 - Google Patents
Critical measurement parameters estimation in liquid rocket engine using LSTM-based soft sensorDevakumar et al., 2023
- Document ID
- 5371451370699798851
- Author
- Devakumar M
- Uma G
- Umapathy M
- et al.
- Publication year
- Publication venue
- Flow Measurement and Instrumentation
External Links
Snippet
Abstract Liquid Rocket engines (LREs) need to be hot tested on the ground, in order to make them flight-worthy. Ground hot tests are critical, expensive, and involve considerable human efforts in acquiring the critical measurement parameters. Measurement sensors are prone to …
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
- 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
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- 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
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
- G06N3/063—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means
- G06N3/0635—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons using electronic means using analogue means
-
- 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
- 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
- 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
-
- 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
- G06F17/10—Complex mathematical operations
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Lu et al. | Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm | |
Gu et al. | Online adaptive least squares support vector machine and its application in utility boiler combustion optimization systems | |
Sharifi et al. | Nonlinear sensor fault diagnosis using mixture of probabilistic PCA models | |
Tayarani-Bathaie et al. | Dynamic neural network-based fault diagnosis of gas turbine engines | |
Xu et al. | Improved hybrid modeling method with input and output self-tuning for gas turbine engine | |
Zhou et al. | A model for real-time failure prognosis based on hidden Markov model and belief rule base | |
Ping et al. | Degradation feature extraction using multi-source monitoring data via logarithmic normal distribution based variational auto-encoder | |
Tavakolpour-Saleh et al. | Parametric and nonparametric system identification of an experimental turbojet engine | |
Bartolini et al. | Application of artificial neural networks to micro gas turbines | |
Yuan et al. | A SIA-LSTM based virtual metrology for quality variables in irregular sampled time sequence of industrial processes | |
Devakumar et al. | Critical measurement parameters estimation in liquid rocket engine using LSTM-based soft sensor | |
Kobayashi et al. | Integration of on-line and off-line diagnostic algorithms for aircraft engine health management | |
CN107045575A (en) | Aero-engine performance model modelling approach based on self-adjusting Wiener model | |
Li et al. | Learning transfer feature representations for gas path fault diagnosis across gas turbine fleet | |
Soualhi et al. | PHM SURVEY: Implementation of prognostic methods for monitoring industrial systems | |
Liu et al. | Long–short-term memory encoder–decoder with regularized hidden dynamics for fault detection in industrial processes | |
Xie et al. | Layered online data reconciliation strategy with multiple modes for industrial processes | |
Mohammadi et al. | Soft sensor design and fault detection using Bayesian network and probabilistic principal component analysis | |
Pan et al. | VAE-based interpretable latent variable model for process monitoring | |
Hong et al. | Remaining useful life prediction using time-frequency feature and multiple recurrent neural networks | |
Zhu et al. | Mixture semisupervised Bayesian principal component regression for soft sensor modeling | |
Zhang et al. | A new adaptive Mamdani-type fuzzy modeling strategy for industrial gas turbines | |
Shahbaz et al. | Design of hybrid fault-tolerant control system for air-fuel ratio control of internal combustion engines using artificial neural network and sliding mode control against sensor faults | |
Wisyaldin et al. | Using LSTM network to predict circulating water pump bearing condition on coal fired power plant | |
Zhang et al. | A fault-tolerant acceleration control strategy for turbofan engine based on multi-layer perceptron with exponential Gumbel loss |