Principal Component Analysis Based Dynamic Fuzzy Neural Network for Internal Corrosion Rate Prediction of Gas Pipelines

Aiming at the characteristics of the nonlinear changes in the internal corrosion rate in gas pipelines, and artificial neural networks easily fall into a local optimum. This paper proposes a model that combines a principal component analysis (PCA) algorithm and a dynamic fuzzy neural network (D-FNN) to address the problems above. The principal component analysis algorithm is used for dimensional reduction and feature extraction, and a dynamic fuzzy neural network model is utilized to perform the prediction. The study implementing the PCA-D-FNN is further accomplished with the corrosion data from a real pipeline, and the results are compared among the artificial neural networks, fuzzy neural networks, and D-FNN models. The results verify the effectiveness of the model and algorithm for inner corrosion rate prediction.

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Face Recognition Using Principal Component Analysis-Fuzzy Linear Discriminant Analysis and Dynamic Fuzzy Neural Network

Lecture Notes in Electrical Engineering - Future Wireless Networks and Information Systems ◽

10.1007/978-3-642-27323-0_73 ◽

2012 ◽

pp. 577-586 ◽

Cited By ~ 1

Author(s):

Sai Wang ◽

Huiwen Deng

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Face Recognition ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Fuzzy Neural Network ◽

Principal Component ◽

Component Analysis ◽

Linear Discriminant ◽

Fuzzy Neural

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Fuzzy Classification of Gear Fault Using Principal Component Analysis-Based Fuzzy Neural Network

2020 International Symposium on Flexible Automation ◽

10.1115/isfa2020-9632 ◽

2020 ◽

Author(s):

Kai Zhou ◽

J. Tang

Keyword(s):

Neural Network ◽

Principal Component Analysis ◽

Fuzzy Neural Network ◽

Principal Component ◽

Component Analysis ◽

Fuzzy Classification ◽

Vibration Data ◽

Gear Fault ◽

Fuzzy Neural

Abstract Condition assessment of machinery components such as gears is important to maintain their normal operations and thus can bring benefit to their life circle management. Data-driven approaches haven been a promising way for such gear condition monitoring and fault diagnosis. In practical situation, gears generally have a variety of fault types, some of which exhibit continuous severities of fault. Vibration data collected oftentimes are limited to reflect all possible fault types. Therefore, there is practical need to utilize the data with a few discrete fault severities in training and then infer fault severities for the general scenario. To achieve this, we develop a fuzzy neural network (FNN) model to classify the continuous severities of gear faults based on the experimental measurement. Principal component analysis (PCA) is integrated with the FNN model to capture the main features of the time-series vibration signals with dimensional reduction for the sake of computational efficiency. Systematic case studies are carried out to validate the effectiveness of proposed methodology.

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