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Vibration-based Incipient Surge Detection and Diagnosis of the Centrifugal Compressor Using Adaptive Feature Fusion and Sparse Ensemble Learning Approach by Yaochun Hou; Yuxuan Wang; Yiran Pan; Weiting He; Wenjun Huang; Peng Wu; Dazhuan Wu is a Engineering article available to read on EtoBox.

What is Vibration-based Incipient Surge Detection and Diagnosis of the Centrifugal Compressor Using Adaptive Feature Fusion and Sparse Ensemble Learning Approach about?

As a critical high-speed rotating machinery, the centrifugal compressor has been widely used in various modern industries. However, it is subject to a potential damaging phenomenon called surge that can be caused by several factors such as unmatched design, improper operation, inlet and outlet blockage, and so on, which thus may result in catastrophic accidents. This paper concerns the incipient surge detection and diagnosis (ISDD) of the centrifugal compressor based on its bearing vibration signals, leveraging adaptive feature fusion and sparse ensemble learning approach to develop a data-driven-based intelligent diagnostic model. Firstly, vibration signals are decomposed by the empirical mode decomposition (EMD) method, and various features can be extracted from the obtained intrinsic mode functions (IMFs) with rich information of the time series. Next, maximum likelihood estimation (MLE) is utilized to compute the intrinsic dimension of the extracted feature vectors, which are then reduced to the corresponding dimensionality adaptively by kernel principal component analysis (kernel PCA). Coping with the multi-dimensional input and nonlinearly separable classification problem, an

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Author

Yaochun Hou; Yuxuan Wang; Yiran Pan; Weiting He; Wenjun Huang; Peng Wu; Dazhuan Wu

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)