scholarly journals Application of Reinforcement Learning Algorithm Model in Gas Path Fault Intelligent Diagnosis of Gas Turbine

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yulong Luo
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Learning Algorithm ◽  
Working Environment ◽  
Intelligent Diagnosis ◽  
Diagnosis Method ◽  
Gas Path Fault ◽  
Reinforcement Learning Algorithm

Gas turbine is widely used because of its advantages of fast start and stop, no pollution, and high thermal efficiency. However, the working environment of high temperature, high pressure, and high speed makes the gas turbine prone to failure. The traditional gas path fault intelligent diagnosis scheme of the gas turbine has the problems of poor control effect and low scheduling accuracy. Experiment studies the application of neural network and reinforcement learning algorithm in gas path fault intelligent diagnosis of the gas turbine. The accurate control of fault diagnosis planning is realized from gas path fault diagnosis, daily maintenance, service condition monitoring, power utilization rate, and other aspects of the gas turbine. The reinforcement learning model can realize the intelligent diagnosis and record of gas path fault of the gas turbine, to achieve diversified analysis and intelligent diagnosis scheme. Through neural network algorithm and deep learning technology, the whole process monitoring of the gas turbine is realized, and the failure rate of the gas turbine in the working process is reduced. The experimental results show that, compared with the thermal fault diagnosis method and the fault diagnosis method of the electric percussion drill, using thermal imaging, the gas turbine gas path fault intelligent diagnosis model based on the reinforcement learning algorithm can complete the data information in the process of real-time data transmission. The quantified conversion and processing of the system has the advantages of higher control accuracy and faster response speed, which can effectively improve the diagnostic efficiency and accuracy.

scholarly journals Research on Gas-Path Fault-Diagnosis Method of Marine Gas Turbine Based on Exergy Loss and Probabilistic Neural Network

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4701 ◽  
Author(s):  
Yunpeng Cao ◽  
Xinran Lv ◽  
Guodong Han ◽  
Junqi Luan ◽  
Shuying Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Probabilistic Neural Network ◽  
Second Law Of Thermodynamics ◽  
Fault Detection And Isolation ◽  
Exergy Loss ◽  
Diagnosis Method ◽  
Gas Path Fault ◽  
Faulty Component

In order to improve the accuracy of gas-path fault detection and isolation for a marine three-shaft gas turbine, a gas-path fault diagnosis method based on exergy loss and a probabilistic neural network (PNN) is proposed. On the basis of the second law of thermodynamics, the exergy flow among the subsystems and the external environment is analyzed, and the exergy model of a marine gas turbine is established. The exergy loss of a marine gas turbine under the healthy condition and typical gas-path faulty condition is analyzed, and the relative change of exergy loss is used as the input of the PNN to detect the gas-path malfunction and locate the faulty component. The simulation case study was conducted based on a three-shaft marine gas turbine with typical gas-path faults. Several results show that the proposed diagnosis method can accurately detect the fault and locate the malfunction component.

TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play

1994 ◽  
Vol 6 (2) ◽  
pp. 215-219 ◽  
Author(s):  
Gerald Tesauro
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Intermediate Level ◽  
Zero Knowledge ◽  
Initial Strategy ◽  
Master Level ◽  
Reinforcement Learning Algorithm

TD-Gammon is a neural network that is able to teach itself to play backgammon solely by playing against itself and learning from the results, based on the TD(λ) reinforcement learning algorithm (Sutton 1988). Despite starting from random initial weights (and hence random initial strategy), TD-Gammon achieves a surprisingly strong level of play. With zero knowledge built in at the start of learning (i.e., given only a “raw” description of the board state), the network learns to play at a strong intermediate level. Furthermore, when a set of hand-crafted features is added to the network's input representation, the result is a truly staggering level of performance: the latest version of TD-Gammon is now estimated to play at a strong master level that is extremely close to the world's best human players.

Fault Diagnosis of Cascaded Converter Based on Wavelet Packet RBF Neural Network

2013 ◽  
Vol 273 ◽  
pp. 300-304
Author(s):  
Xin Wang ◽  
Juan Xu ◽  
Guo Dong Zhang ◽  
Rui Min Qi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Rbf Neural Network ◽  
Wavelet Packet ◽  
Open Circuit ◽  
Learning Performance ◽  
Cascaded Converter ◽  
Diagnosis Method ◽  
Faults Diagnosis

To study the power component open circuit faults diagnosis method of the cascaded converter. Aiming at the insufficiency of the BP learning algorithm in the machinery fault diagnosis, such as the low learning convergence speed, the easily appearing local minimum, the instability learning performance caused by the initial value, to proposed a new method applied to the cascaded converter based on radial basis function (RBF) neural network. Experiments show that the method based on wavelet packet analysis and RBF neural network has better learning and fault identification capability, and it can meet the online real-time fault diagnosis of the cascaded converter.

scholarly journals Research on Intelligent Diagnosis Method for Large-Scale Ship Engine Fault in Non-Deterministic Environment

2017 ◽  
Vol 24 (s3) ◽  
pp. 200-206 ◽  
Author(s):  
Donghua Feng ◽  
Yahong Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Use Value ◽  
Intelligent Diagnosis ◽  
High Diagnostic Accuracy ◽  
Diagnosis Method ◽  
Main Fault ◽  
Diagnosis Model ◽  
Fault Mode

Abstract Aiming at the problem of inaccurate and time-consuming of the fault diagnosis method for large-scale ship engine, an intelligent diagnosis method for large-scale ship engine fault in non-deterministic environment based on neural network is proposed. First, the possible fault of the engine was analyzed, and the downtime fault of large-scale ship engine and the main fault mode were identified. On this basis, the fault diagnosis model for large-scale ship engine based on neural network is established, and the intelligent diagnosis of engine fault is completed. The experiment proved that the proposed method has high diagnostic accuracy, engine fault diagnosis takes only about 3s, with a higher use value.

Analog Circuit Fault Diagnosis Based on Levenberg-Marquardt Learning Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 979-982
Author(s):  
Huang Guo ◽  
Bao Ru Han ◽  
Guo Fang Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Learning Algorithm ◽  
Convergence Speed ◽  
Bp Network ◽  
Descent Algorithm ◽  
Newton Algorithm ◽  
Levenberg Marquardt ◽  
Diagnosis Method

This paper presents a fault diagnosis method of BP neural network based on Levenberg-Marquardt learning algorithm. First, the use of principal component analysis to reduce the dimension of the fault sample reduced BP neural network input variables. Then use the Levenberg-Marquardt learning algorithm to adjust the network weights. Levenberg-Marquardt learning algorithm is combination of the Gauss - Newton algorithm and steepest descent algorithm. It has Gauss - Newton algorithm of local convergence and gradient descent algorithm of the global characteristic. So it has higher convergence speed, reduces the training time, to a certain extent, overcomes the problem of traditional BP network convergence speed slow and easy to fall into local minimum point. Simulation results demonstrate the correctness and accuracy of this fault diagnosis method.

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