Weighted domain separation based open set fault diagnosis
被引:6
|
作者:
Zhang, Xingwu
论文数: 0引用数: 0
h-index: 0
机构:
Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R ChinaXi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China
Zhang, Xingwu
[1
]
论文数: 引用数:
h-index:
机构:
Zhao, Yu
[1
]
Yu, Xiaolei
论文数: 0引用数: 0
h-index: 0
机构:
Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R ChinaXi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China
Yu, Xiaolei
[1
]
Ma, Rui
论文数: 0引用数: 0
h-index: 0
机构:
Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R ChinaXi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China
Ma, Rui
[1
]
Wang, Chenxi
论文数: 0引用数: 0
h-index: 0
机构:
Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R ChinaXi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China
Wang, Chenxi
[1
]
Chen, Xuefeng
论文数: 0引用数: 0
h-index: 0
机构:
Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R ChinaXi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China
Chen, Xuefeng
[1
]
机构:
[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Shaanxi, Peoples R China
Domain adaptation (DA);
Open set fault diagnosis (OSFD);
Weighted domain separation network (WDSN);
D O I:
10.1016/j.ress.2023.109518
中图分类号:
T [工业技术];
学科分类号:
08 ;
摘要:
Cross domain fault diagnosis based on deep learning is of great significance for improving the reliability and safety of mechanical equipment. Generally, it assumes that the label sets of training data (source domain) and test data (target domain) are consistent. However, the test data usually contain unknown classes that are unseen in the training data due to unpredictable fault modes in real industrial scenarios. Therefore, the open set fault diagnosis (OSFD) where the training label set is a part of the test label set appeared. However, most previous studies directly aligned the source domain and target domain without considering the private features of each domain and required prior knowledge to set the threshold for unknown class detection. Thus, a weighted domain separation network (WDSN) is proposed. First, the unknown samples are detected by establishing the boundary between known class and unknown class by a binary classifier without setting a threshold. Then, the private features of each domain are separated to obtain the shared domain, thereby avoiding interference of unknown classes and noise during feature alignment. Results on two datasets demonstrate that the proposed method outperforms state-of-the-art methods and has more prospects for ensuring the reliability of mechanical equipment.
机构:
College of Weaponry Engineering, Naval University of Engineering, Wuhan,430000, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan,430000, China
She, Bo
Liang, Weige
论文数: 0引用数: 0
h-index: 0
机构:
College of Weaponry Engineering, Naval University of Engineering, Wuhan,430000, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan,430000, China
Liang, Weige
Qin, Fenqi
论文数: 0引用数: 0
h-index: 0
机构:
Research Institute of China Shipbuilding, Zhengzhou,450000, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan,430000, China
Qin, Fenqi
Dong, Haidi
论文数: 0引用数: 0
h-index: 0
机构:
College of Weaponry Engineering, Naval University of Engineering, Wuhan,430000, ChinaCollege of Weaponry Engineering, Naval University of Engineering, Wuhan,430000, China
Dong, Haidi
[J].
Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument,
2023,
44
(07):
: 325
-
334
机构:
Southeast Univ, Natl Engn Res Ctr Power Generat Control & Safety, Sch Energy & Environm, Nanjing 210096, Peoples R ChinaSoutheast Univ, Natl Engn Res Ctr Power Generat Control & Safety, Sch Energy & Environm, Nanjing 210096, Peoples R China
Liu, Yang
Deng, Aidong
论文数: 0引用数: 0
h-index: 0
机构:
Southeast Univ, Natl Engn Res Ctr Power Generat Control & Safety, Sch Energy & Environm, Nanjing 210096, Peoples R ChinaSoutheast Univ, Natl Engn Res Ctr Power Generat Control & Safety, Sch Energy & Environm, Nanjing 210096, Peoples R China
Deng, Aidong
Deng, Minqiang
论文数: 0引用数: 0
h-index: 0
机构:
Southeast Univ, Natl Engn Res Ctr Power Generat Control & Safety, Sch Energy & Environm, Nanjing 210096, Peoples R ChinaSoutheast Univ, Natl Engn Res Ctr Power Generat Control & Safety, Sch Energy & Environm, Nanjing 210096, Peoples R China
Deng, Minqiang
Shi, Yaowei
论文数: 0引用数: 0
h-index: 0
机构:
Nanjing Univ Posts & Telecommun, Sch Internet Things, Nanjing 210009, Peoples R ChinaSoutheast Univ, Natl Engn Res Ctr Power Generat Control & Safety, Sch Energy & Environm, Nanjing 210096, Peoples R China
Shi, Yaowei
Li, Jing
论文数: 0引用数: 0
h-index: 0
机构:
Nanjing Audit Univ, Sch Informat Engn, Nanjing 211815, Peoples R ChinaSoutheast Univ, Natl Engn Res Ctr Power Generat Control & Safety, Sch Energy & Environm, Nanjing 210096, Peoples R China