scholarly journals An approach of classification and parameters estimation, using neural network, for lubricant degradation diagnosis

2018 ◽  
Vol Volume XXVII (XVII), 2018/2 (2) ◽  
Author(s):  
Gavril Grebenişan ◽  
Nazzal Salem ◽  
Sanda Bogdan
Keyword(s):  
Neural Network ◽  
Parameters Estimation ◽  
Lubricant Degradation ◽  
Degradation Diagnosis

scholarly journals An approach of classification and parameters estimation, using neural network, for lubricant degradation diagnosis

2018 ◽  
Vol 184 ◽  
pp. 03009
Author(s):  
Gavril Grebenişan ◽  
Nazzal Salem ◽  
Sanda Bogdan
Keyword(s):  
Machine Tools ◽  
Machine Intelligence ◽  
Parameters Estimation ◽  
Support Vector ◽  
Lubricant Degradation ◽  
National Program ◽  
Data Set ◽  
Validation And Verification ◽  
Industrial Systems ◽  
Degradation Diagnosis

This paper addresses a delicate problem, namely the diagnosis of the state of the oils in the industrial systems, namely the machine tools. Based on measurements (the data set contains over five million records), within a Machine Intelligence for Diagnosis Automation (MIDA) project funded by the National Program PN II, ERA MANUNET: NR 13081221 / 13.08.2013, several applications of MATLAB toolbars are being developed in the field of artificial intelligence, specifically using the Support Vector Machine algorithms and neural networks. The tests were carried out on several distinct situations, followed by validation and verification tests on the devices designed and developed within the project (MIDA, Monitoil).

scholarly journals Exponential Reaching Law and Sensorless DTC IM Control with Neural Network Online Parameters Estimation based on MRAS

2018 ◽  
Vol 7 (2) ◽  
pp. 77
Author(s):  
Legrioui Said ◽  
Rezgui Salah Eddine ◽  
Benalla Hocine
Keyword(s):  
Neural Network ◽  
Time Constant ◽  
Sliding Mode ◽  
Direct Torque Control ◽  
Parameters Estimation ◽  
Torque Control ◽  
System Control ◽  
Reaching Law ◽  
Exponential Reaching Law ◽  
Stator Resistance

The most important problem in the control of induction machine (IM) is the change of its parameters, especially the stator resistance and rotor-time constant. The objective of<em> </em>this paper is to implement a new strategy in sensorless direct torque control (DTC) of an IM drive. The rotor flux based model reference adaptive system (MRAS) is used<em> </em>to estimate conjointly<em> </em>the rotor<em> </em>speed, the stator resistance and the inverse rotor time constant, the process of the estimation is performed on-line by a new MRAS-based artificial neural network (ANN) technique. Furthermore, the drive is complemented with a new exponential reaching law (ERL), based on the sliding mode control (SMC) to significantly improve the performances of the system control compared to the conventional SMC which is known to be susceptible to the annoying chattering phenomenon. An experimental investigation was carried out via the Matlab/Simulink with real time interface (RTI) and dSPACE (DS1104) board where the behavior of the proposed method was tested at different points of IM operation.

scholarly journals Equivalent Circuit Parameters Estimation for PEM Fuel Cell Using RBF Neural Network and Enhanced Particle Swarm Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Wen-Yeau Chang
Keyword(s):  
Neural Network ◽  
Equivalent Circuit ◽  
Rbf Neural Network ◽  
Estimation Method ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Equivalent Circuit Parameters ◽  
Current Loading ◽  
Parameters Measurement ◽  
Enhanced Particle Swarm Optimization

This paper proposes an equivalent circuit parameters measurement and estimation method for proton exchange membrane fuel cell (PEMFC). The parameters measurement method is based on current loading technique; in current loading test a no load PEMFC is suddenly turned on to obtain the waveform of the transient terminal voltage. After the equivalent circuit parameters were measured, a hybrid method that combines a radial basis function (RBF) neural network and enhanced particle swarm optimization (EPSO) algorithm is further employed for the equivalent circuit parameters estimation. The RBF neural network is adopted such that the estimation problem can be effectively processed when the considered data have different features and ranges. In the hybrid method, EPSO algorithm is used to tune the connection weights, the centers, and the widths of RBF neural network. Together with the current loading technique, the proposed hybrid estimation method can effectively estimate the equivalent circuit parameters of PEMFC. To verify the proposed approach, experiments were conducted to demonstrate the equivalent circuit parameters estimation of PEMFC. A practical PEMFC stack was purposely created to produce the common current loading activities of PEMFC for the experiments. The practical results of the proposed method were studied in accordance with the conditions for different loading conditions.

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