Vibration-based incipient surge detection and diagnosis of the centrifugal compressor using adaptive feature fusion and sparse ensemble learning approach

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

doi:10.1016/j.aei.2023.101947

Cited by 13 publications

(1 citation statement)

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“…As a powerful machine learning technique, deep-learning approaches are adopted to learn complex feature representations from large amounts of data and exhibit strong generalization abilities. An adaptive feature fusion learning method [ 8 ] was proposed for stall precursor detection and diagnosis based on vibration signals. The model combined multiple-scale features to reduce the complexity of the ensemble model and improve its generalization performance.…”

Section: Introductionmentioning

confidence: 99%

Dynamic System Stability Modeling Approach with Sparrow-Inspired Meta-Heuristic Optimization Algorithm

Xia,

Zhang,

Wang

2023

Biomimetics

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Aiming at the accurate prediction of the inception of instability in a compressor, a dynamic system stability model is proposed based on a sparrow-inspired meta-heuristic optimization algorithm in this article. To achieve this goal, a spatial mode is employed for flow field feature extraction and modeling object acquisition. The nonlinear characteristic presented in the system is addressed using fuzzy entropy as the identification strategy to provide a basis for instability determination. Using Sparrow Search Algorithm (SSA) optimization, a Radial Basis Function Neural Network (RBFNN) is achieved for the performance prediction of system status. A Logistic SSA solution is first established to seek the optimal parameters of the RBFNN to enhance prediction accuracy and stability. On the basis of the RBFNN-LSSA hybrid model, the stall inception is detected about 35.8 revolutions in advance using fuzzy entropy identification. To further improve the multi-step network model, a Tent SSA is introduced to promote the accuracy and robustness of the model. A wider range of potential solutions within the TSSA are explored by incorporating the Tent mapping function. The TSSA-based optimization method proves a suitable adaptation for complex nonlinear dynamic modeling. And this method demonstrates superior performance, achieving 42 revolutions of advance warning with multi-step prediction. This RBFNN-TSSA model represents a novel and promising approach to the application of system modeling. These findings contribute to enhancing the abnormal warning capability of dynamic systems in compressors.

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Section: Introductionmentioning

confidence: 99%