Gas Path Fault Diagnosis of Aeroengine Based on Soft Square Pinball Loss ELM

• Main Authors: Yuyuan Cao, Bowen Zhang, Huawei Wang, Yu Bai
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Extreme learning machine; fault diagnosis; aircraft engine; machine learning
• Online Access: https://ieeexplore.ieee.org/document/9143161/

The ELM constructed based on the least squares loss function and ±1 label has poor generalization in the classification of data containing noise. The introduction of square pinball loss function can improve the robustness of ELM. However, the algorithm based on the squared loss function and the ±1 label imposes a margin of 1 for all training samples. At the same time, due to the unbounded nature of the loss function, the generalization of the algorithm in the classification problem is reduced. This paper proposes a soft threshold square pinball loss (SSP-Loss) function. This function can set more flexible thresholds for training samples while maintaining the robustness of the square pinball loss function. The soft-threshold square pinball loss function can approximate the bounded loss function in stages to further improve the classification performance of the algorithm. The performance of ELM based on the soft pinball loss function on several benchmark data sets proves the effectiveness of our proposed algorithm. More importantly, the excellent robustness and classification performance of the algorithm is very suitable for aeroengine gas path fault diagnosis, and is expected to become its candidate technology.