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

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 sig-nals 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 ensemble learning approach with L1-regularization term is adopted for incipient surge identification of the centrifugal compressor. A novel and hyperparameter-relaxed hybrid algorithm is employed for the sparse ensemble learning procedure, which guarantees transcendental accuracy and preeminent generalization capability while ensuring high robustness and sparsity of the intelligent model. Finally, in addition to diagnostic tasks of the centrifugal compressor datasets under different working conditions, the ISDD performance of the proposed method in imbalanced sample scenarios, varying working conditions and additional noise interference circumstances is comprehensively investigated, and the overall success indicates the robustness and superiority of the proposed method compared with other existing approaches.