Abstract
Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.
Highlights
Aircraft engine is a mission-critical mechanical system [1] with complicated structure and poor operating conditions [2], and its stable and reliable operation is of great importance to the flight safety [3]
Gaussian kernel function is selected, and the regular parameters and kernel parameters of Kernel extreme learning machine (KELM) are selected by the fast leave-one-out (FLOO) method [39], while the adjustment parameters of distributed kernel extreme learning machines (DKELMs) and support vector machine (SVM) are selected by the experimental method
The study of failure diagnostics is crucial for improving the safety and economic performance of aircraft engine, and KELM has attracted extensive attention in the field of failure diagnostics
Summary
Aircraft engine is a mission-critical mechanical system [1] with complicated structure and poor operating conditions [2], and its stable and reliable operation is of great importance to the flight safety [3]. Diagnostics is to detect the fault and carry out the identification and isolation [10], and the main purpose of prognostics is to predict the aircraft engine performance after the degradation and failure [11]. The failure mechanism of the aircraft engine is usually extremely complex, and it is hard to establish a volumetric dynamic or thermodynamics model that can predict the health state and is applicable to the whole working condition range [17] with high reliability. In order to verify the validity of distributed kernel extreme learning machines (DKELMs) algorithm, performance comparison among the proposed algorithm, the original KELM algorithm and SVM algorithm are carried out in both regression and classification benchmark dataset.
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