عنوان انگلیسی مقاله:
Soft extreme learning machine for fault detection of aircraft engine
ترجمه فارسی عنوان مقاله:
یادگیری ماشین افراطی نرم برای تشخیص خطا موتور هواپیما
Sciencedirect - Elsevier - Aerospace Science and Technology, 91 (2019) 70–81: 10:1016/j:ast:2019:05:021
Yong-PingZhaoa,∗, GongHuanga, Qian-KunHua, Jian-FengTana, Jian-JunWangb, ZheYangb
When extreme learning machine (ELM) is used to cope with classification problems, the ±1is commonly used to construct the label vector. Since ELM adopts the square loss function, this means that it tends to force the margins of all the training samples exactly equaling one from the perspective of margin learning theory, which is unreasonable to some extent. To overcome this hard margin flaw, in this paper a soft extreme learning machine (SELM) is proposed, which flexibly sets a soft target margin for each training sample. Through solving a series of regularized ELMs (RELMs), SELM can be computed efficiently. Based on SELM, an improved SELM (ISELM) is proposed to deal with imbalanced classification problems, which can keep the same computational efficiency as SELM via solving a series of weighted RELMs. From the experimental results on benchmark data sets, the effectiveness and feasibility of SELM and ISELM are confirmed. More importantly, when they are applied to fault detection of aircraft engine, they are promising to be developed as the candidate techniques for it, and ISELM is especially in favor.
Keywords: Fault detection | Aircraft engine | Extreme learning machine | Imbalanced classification | Machine learning