A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM
Joint Authors
Yu, Lu
Qu, Jianling
Gao, Feng
Tian, Yanping
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-01-06
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
Faced with severe operating conditions, rolling bearings tend to be one of the most vulnerable components in mechanical systems.
Due to the requirements of economic efficiency and reliability, effective fault diagnosis methods for rolling bearings have long been a hot research topic of rotary machinery fields.
However, traditional methods such as support vector machine (SVM) and backpropagation neural network (BP-NN) which are composed of shallow structures trap into a dilemma when further improving their accuracies.
Aiming to overcome shortcomings of shallow structures, a novel hierarchical algorithm based on stacked LSTM (long short-term memory) is proposed in this text.
Without any preprocessing operation or manual feature extraction, the proposed method constructs a framework of end-to-end fault diagnosis system for rolling bearings.
Beneficial from the memorize-forget mechanism of LSTM, features inherent in raw temporal signals are extracted hierarchically and automatically by stacking LSTM.
A series of experiments demonstrate that the proposed model can not only achieve up to 99% accuracy but also outperform some state-of-the-art intelligent fault diagnosis methods.
American Psychological Association (APA)
Yu, Lu& Qu, Jianling& Gao, Feng& Tian, Yanping. 2019. A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM. Shock and Vibration،Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1211087
Modern Language Association (MLA)
Yu, Lu…[et al.]. A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM. Shock and Vibration No. 2019 (2019), pp.1-10.
https://search.emarefa.net/detail/BIM-1211087
American Medical Association (AMA)
Yu, Lu& Qu, Jianling& Gao, Feng& Tian, Yanping. A Novel Hierarchical Algorithm for Bearing Fault Diagnosis Based on Stacked LSTM. Shock and Vibration. 2019. Vol. 2019, no. 2019, pp.1-10.
https://search.emarefa.net/detail/BIM-1211087
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1211087