scholarly journals Online fault detection of induction motors using independent component analysis and fuzzy neural network

2009 ◽  
Author(s):  
Zhao-Xia Wang ◽  
C.S. Chang ◽  
X. German ◽  
W.W. Tan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Independent Component Analysis ◽  
Fuzzy Neural Network ◽  
Induction Motors ◽  
Component Analysis ◽  
Independent Component ◽  
Fuzzy Neural

Soft sensor modelling of propylene conversion based on a Takagi-Sugeno fuzzy neural network optimized with independent component analysis and mutual information

2018 ◽  
Vol 41 (3) ◽  
pp. 737-748 ◽  
Author(s):  
Shuting Liu ◽  
Xianwen Gao ◽  
Wenhai Qi ◽  
Shumei Zhang
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Independent Component Analysis ◽  
Fuzzy Neural Network ◽  
Component Analysis ◽  
Soft Sensor ◽  
Independent Component ◽  
Fuzzy Neural ◽  
Propylene Conversion ◽  
Sensor Modelling

Propylene conversion is important to economic efficiency in the production of acrylic acid. Hence, the online measurement of propylene conversion is becoming more and more important. The current measurement method is mainly uses an offline meteorological chromatography analyser, which is difficult to measure accurately in real time. A soft sensor modelling method of propylene conversion based on Takagi-Sugeno (T-S) fuzzy neural network optimized by independent component analysis and mutual information is proposed in this paper. Firstly, fast independent component analysis-based denoising strategy is developed to remove the noise in the measurement of variables influenced by propylene conversion. Then, a mutual information-based variable selection method is proposed to select the key variables from multitudinous variables to reduce the influence of weak correlation. Finally, a T-S fuzzy neural network algorithm is employed to forecast the propylene conversion in the process of propylene oxidation. Simulation results show that the proposed soft sensor modelling method has better prediction accuracy and generalization ability. The method of this paper is obvious and effective.

scholarly journals Improved Process Monitoring Scheme Using Multi-Scale Independent Component Analysis

2021 ◽  
Author(s):  
K Ramakrishna Kini ◽  
Muddu Madakyaru
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Noisy Environment ◽  
Process Data ◽  
Multi Scale ◽  
Efficient Information ◽  
Non Gaussian

AbstractThe task of fault detection is crucial in modern chemical industries for improved product quality and process safety. In this regard, data-driven fault detection (FD) strategy based on independent component analysis (ICA) has gained attention since it improves monitoring by capturing non-gaussian features in the process data. However, presence of measurement noise in the process data degrades performance of the FD strategy since the noise masks important information. To enhance the monitoring under noisy environment, wavelet-based multi-scale filtering is integrated with the ICA model to yield a novel multi-scale Independent component analysis (MSICA) FD strategy. One of the challenges in multi-scale ICA modeling is to choose the optimum decomposition depth. A novel scheme based on ICA model parameter estimation at each depth is proposed in this paper to achieve this. The effectiveness of the proposed MSICA-based FD strategy is illustrated through three case studies, namely: dynamic multi-variate process, quadruple tank process and distillation column process. In each case study, the performance of the MSICA FD strategy is assessed for different noise levels by comparing it with the conventional FD strategies. The results indicate that the proposed MSICA FD strategy can enhance performance for higher levels of noise in the data since multi-scale wavelet-based filtering is able to de-noise and capture efficient information from noisy process data.

scholarly journals Faults diagnosis in stator windings of high speed solid rotor induction motors using fuzzy neural network

2021 ◽  
Vol 12 (1) ◽  
pp. 597
Author(s):  
Ahmed Thamer Radhi ◽  
Wael Hussein Zayer
Keyword(s):  
Neural Network ◽  
Equivalent Circuit ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Induction Motors ◽  
Stator Winding ◽  
Three Phase ◽  
Fuzzy Neural ◽  
Diagnosis Method ◽  
Faults Diagnosis

The paper deals with faults diagnosis method proposed to detect the inter-turn and turn to earth short circuit in stator winding of three-phase high-speed solid rotor induction motors. This method based on negative sequence current of motor and fuzzy neural network algorithm. On the basis of analysis of 2-D electromagnet field in the solid rotor the rotor impedance has been derived to develop the solid rotor induction motor equivalent circuit. The motor equivalent circuit is simulated by MATLAB software to study and record the data for training and testing the proposed diagnosis method. The numerical results of proposed approach are evaluated using simulation of a three-phase high-speed solid-rotor induction motor of two-pole, 140 Hz. The results of simulation shows that the proposed diagnosis method is fast and efficient for detecting inter-turn and turn to earth faults in stator winding of high-speed solid-rotor induction motors with different faults conditions

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