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所属单位：能源与动力学院

发表刊物：2017 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-HARBIN)

关键字：fault diagnosis sparse filtering softmax regression rotating speed fluctuations

摘要：Effective fault diagnosis method has long been a hot topic in the field of prognosis and health management of rotary machinery. This paper investigates an effective deep learning method known as sparse filtering, which is used to extract features from fault signal directly. And then, the supervised learning method softmax regression is applied to classify the fault types. The training samples are the vibration signals under a certain rotational speed and the test samples are in different rotational speeds. The key parameters of the model are optimized and analyzed through orthogonal experiments and single factor experiment. The diagnosis results show that the sparse filtering model has strong robustness for rotating machinery fault diagnosis in the case of rotating speed fluctuations.

ISSN号：2166-5656

是否译文：否

发表时间：2017-01-01

合写作者：An, Zenghui,Wang, Jinrui,Qian, Weiwei

通讯作者：李舜酩