Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Saibo Xing"'
Publikováno v:
IEEE Transactions on Industrial Electronics. 69:1968-1976
Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. There
Publikováno v:
IEEE Transactions on Industrial Electronics. 68:2617-2625
As a deep learning model, a deep belief network (DBN) consists of multiple restricted Boltzmann machines (RBMs). Based on DBN, many intelligent fault diagnosis methods are proposed. However, these methods seldom considered the appearance of new worki
Publikováno v:
IEEE Transactions on Industrial Electronics. 66:7316-7325
The success of intelligent fault diagnosis of machines relies on the following two conditions: 1) labeled data with fault information are available; and 2) the training and testing data are drawn from the same probability distribution. However, for s
Publikováno v:
Mechanical Systems and Signal Processing. 122:692-706
Intelligent fault diagnosis of rolling element bearings has made some achievements based on the availability of massive labeled data. However, the available data from bearings used in real-case machines (BRMs) are insufficient to train a reliable int
Publikováno v:
Mechanical Systems and Signal Processing. 110:349-367
Deep learning has attracted attentions in intelligent fault diagnosis of machinery because it allows a deep network to accomplish the tasks of feature learning and fault classification automatically. Among deep learning models, convolutional neural n
Publikováno v:
Mechanical Systems and Signal Processing. 162:108036
It has always been an issue of significance to diagnose compound faults of machines. Existing intelligent diagnosis methods have to be trained by sufficient data of each compound fault. However, both labeled and unlabeled data of mechanical compound
Publikováno v:
Neurocomputing. 272:619-628
In traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken for the manual design of fault features, which makes these methods less automatic. Among deep learning techniques, autoencoders may be a potential tool f
Publikováno v:
IOP Conference Series: Materials Science and Engineering. 1043:052026
Since fault diagnosis has entered the big data era, deep learning has been more and more widely studied to diagnose faults of rolling element bearings. Generally, existing methods require labeled data for training before they can be used to recognize
Publikováno v:
IEEE Transactions on Industrial Electronics. 63:3137-3147
Intelligent fault diagnosis is a promising tool to deal with mechanical big data due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In traditional intelligent diagnosis methods, howeve
Publikováno v:
2018 International Conference on Sensing,Diagnostics, Prognostics, and Control (SDPC).
It is difficult to train a reliable intelligent fault diagnosis model for machines used in real cases (MURC) because there are not sufficient labeled data. However, we can easily simulate various faults in a laboratory, and the data from machines use