A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Shao, H., Jiang, H., Zhao, H., & Wang, F. Mechanical Systems and Signal Processing, 95:187–204, October, 2017. ![A novel deep autoencoder feature learning method for rotating machinery fault diagnosis [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex The operation conditions of the rotating machinery are always complex and variable, which makes it difficult to automatically and effectively capture the useful fault features from the measured vibration signals, and it is a great challenge for rotating machinery fault diagnosis. In this paper, a novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault. Firstly, the maximum correntropy is adopted to design the new deep autoencoder loss function for the enhancement of feature learning from the measured vibration signals. Secondly, artificial fish swarm algorithm is used to optimize the key parameters of the deep autoencoder to adapt to the signal features. The proposed method is applied to the fault diagnosis of gearbox and electrical locomotive roller bearing. The results confirm that the proposed method is more effective and robust than other methods.
@article{shao_novel_2017,
title = {A novel deep autoencoder feature learning method for rotating machinery fault diagnosis},
volume = {95},
issn = {0888-3270},
url = {https://www.sciencedirect.com/science/article/pii/S0888327017301607},
doi = {10.1016/j.ymssp.2017.03.034},
abstract = {The operation conditions of the rotating machinery are always complex and variable, which makes it difficult to automatically and effectively capture the useful fault features from the measured vibration signals, and it is a great challenge for rotating machinery fault diagnosis. In this paper, a novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault. Firstly, the maximum correntropy is adopted to design the new deep autoencoder loss function for the enhancement of feature learning from the measured vibration signals. Secondly, artificial fish swarm algorithm is used to optimize the key parameters of the deep autoencoder to adapt to the signal features. The proposed method is applied to the fault diagnosis of gearbox and electrical locomotive roller bearing. The results confirm that the proposed method is more effective and robust than other methods.},
language = {en},
urldate = {2022-05-02},
journal = {Mechanical Systems and Signal Processing},
author = {Shao, Haidong and Jiang, Hongkai and Zhao, Huiwei and Wang, Fuan},
month = oct,
year = {2017},
keywords = {Artificial fish swarm algorithm, Deep autoencoder, Fault diagnosis, Feature learning, Maximum correntropy},
pages = {187--204},
}
Downloads: 0
{"_id":"8tCKsPLDT85AdqDTF","bibbaseid":"shao-jiang-zhao-wang-anoveldeepautoencoderfeaturelearningmethodforrotatingmachineryfaultdiagnosis-2017","author_short":["Shao, H.","Jiang, H.","Zhao, H.","Wang, F."],"bibdata":{"bibtype":"article","type":"article","title":"A novel deep autoencoder feature learning method for rotating machinery fault diagnosis","volume":"95","issn":"0888-3270","url":"https://www.sciencedirect.com/science/article/pii/S0888327017301607","doi":"10.1016/j.ymssp.2017.03.034","abstract":"The operation conditions of the rotating machinery are always complex and variable, which makes it difficult to automatically and effectively capture the useful fault features from the measured vibration signals, and it is a great challenge for rotating machinery fault diagnosis. In this paper, a novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault. Firstly, the maximum correntropy is adopted to design the new deep autoencoder loss function for the enhancement of feature learning from the measured vibration signals. Secondly, artificial fish swarm algorithm is used to optimize the key parameters of the deep autoencoder to adapt to the signal features. The proposed method is applied to the fault diagnosis of gearbox and electrical locomotive roller bearing. The results confirm that the proposed method is more effective and robust than other methods.","language":"en","urldate":"2022-05-02","journal":"Mechanical Systems and Signal Processing","author":[{"propositions":[],"lastnames":["Shao"],"firstnames":["Haidong"],"suffixes":[]},{"propositions":[],"lastnames":["Jiang"],"firstnames":["Hongkai"],"suffixes":[]},{"propositions":[],"lastnames":["Zhao"],"firstnames":["Huiwei"],"suffixes":[]},{"propositions":[],"lastnames":["Wang"],"firstnames":["Fuan"],"suffixes":[]}],"month":"October","year":"2017","keywords":"Artificial fish swarm algorithm, Deep autoencoder, Fault diagnosis, Feature learning, Maximum correntropy","pages":"187–204","bibtex":"@article{shao_novel_2017,\n\ttitle = {A novel deep autoencoder feature learning method for rotating machinery fault diagnosis},\n\tvolume = {95},\n\tissn = {0888-3270},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0888327017301607},\n\tdoi = {10.1016/j.ymssp.2017.03.034},\n\tabstract = {The operation conditions of the rotating machinery are always complex and variable, which makes it difficult to automatically and effectively capture the useful fault features from the measured vibration signals, and it is a great challenge for rotating machinery fault diagnosis. In this paper, a novel deep autoencoder feature learning method is developed to diagnose rotating machinery fault. Firstly, the maximum correntropy is adopted to design the new deep autoencoder loss function for the enhancement of feature learning from the measured vibration signals. Secondly, artificial fish swarm algorithm is used to optimize the key parameters of the deep autoencoder to adapt to the signal features. The proposed method is applied to the fault diagnosis of gearbox and electrical locomotive roller bearing. The results confirm that the proposed method is more effective and robust than other methods.},\n\tlanguage = {en},\n\turldate = {2022-05-02},\n\tjournal = {Mechanical Systems and Signal Processing},\n\tauthor = {Shao, Haidong and Jiang, Hongkai and Zhao, Huiwei and Wang, Fuan},\n\tmonth = oct,\n\tyear = {2017},\n\tkeywords = {Artificial fish swarm algorithm, Deep autoencoder, Fault diagnosis, Feature learning, Maximum correntropy},\n\tpages = {187--204},\n}\n\n\n\n","author_short":["Shao, H.","Jiang, H.","Zhao, H.","Wang, F."],"key":"shao_novel_2017","id":"shao_novel_2017","bibbaseid":"shao-jiang-zhao-wang-anoveldeepautoencoderfeaturelearningmethodforrotatingmachineryfaultdiagnosis-2017","role":"author","urls":{"Paper":"https://www.sciencedirect.com/science/article/pii/S0888327017301607"},"keyword":["Artificial fish swarm algorithm","Deep autoencoder","Fault diagnosis","Feature learning","Maximum correntropy"],"metadata":{"authorlinks":{}},"html":""},"bibtype":"article","biburl":"https://bibbase.org/zotero/mh_lenguyen","dataSources":["iwKepCrWBps7ojhDx"],"keywords":["artificial fish swarm algorithm","deep autoencoder","fault diagnosis","feature learning","maximum correntropy"],"search_terms":["novel","deep","autoencoder","feature","learning","method","rotating","machinery","fault","diagnosis","shao","jiang","zhao","wang"],"title":"A novel deep autoencoder feature learning method for rotating machinery fault diagnosis","year":2017}