@article{liu_artificial_2018,
	title = {Artificial intelligence for fault diagnosis of rotating machinery: {A} review},
	volume = {108},
	issn = {0888-3270},
	shorttitle = {Artificial intelligence for fault diagnosis of rotating machinery},
	url = {https://www.sciencedirect.com/science/article/pii/S0888327018300748},
	doi = {10.1016/j.ymssp.2018.02.016},
	abstract = {Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.},
	language = {en},
	urldate = {2021-11-02},
	journal = {Mechanical Systems and Signal Processing},
	author = {Liu, Ruonan and Yang, Boyuan and Zio, Enrico and Chen, Xuefeng},
	month = aug,
	year = {2018},
	keywords = {-Nearest neighbour, Artificial intelligence, Artificial neural network, Deep learning, Fault diagnosis, Naive Bayes, Rotating machinery, Support vector machine},
	pages = {33--47},
}

{"_id":"67aSWbfEt8gHNcPAu","bibbaseid":"liu-yang-zio-chen-artificialintelligenceforfaultdiagnosisofrotatingmachineryareview-2018","author_short":["Liu, R.","Yang, B.","Zio, E.","Chen, X."],"bibdata":{"bibtype":"article","type":"article","title":"Artificial intelligence for fault diagnosis of rotating machinery: A review","volume":"108","issn":"0888-3270","shorttitle":"Artificial intelligence for fault diagnosis of rotating machinery","url":"https://www.sciencedirect.com/science/article/pii/S0888327018300748","doi":"10.1016/j.ymssp.2018.02.016","abstract":"Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. Finally, the advantages, limitations, practical implications of different AI algorithms, as well as some new research trends, are discussed.","language":"en","urldate":"2021-11-02","journal":"Mechanical Systems and Signal Processing","author":[{"propositions":[],"lastnames":["Liu"],"firstnames":["Ruonan"],"suffixes":[]},{"propositions":[],"lastnames":["Yang"],"firstnames":["Boyuan"],"suffixes":[]},{"propositions":[],"lastnames":["Zio"],"firstnames":["Enrico"],"suffixes":[]},{"propositions":[],"lastnames":["Chen"],"firstnames":["Xuefeng"],"suffixes":[]}],"month":"August","year":"2018","keywords":"-Nearest neighbour, Artificial intelligence, Artificial neural network, Deep learning, Fault diagnosis, Naive Bayes, Rotating machinery, Support vector machine","pages":"33–47","bibtex":"@article{liu_artificial_2018,\n\ttitle = {Artificial intelligence for fault diagnosis of rotating machinery: {A} review},\n\tvolume = {108},\n\tissn = {0888-3270},\n\tshorttitle = {Artificial intelligence for fault diagnosis of rotating machinery},\n\turl = {https://www.sciencedirect.com/science/article/pii/S0888327018300748},\n\tdoi = {10.1016/j.ymssp.2018.02.016},\n\tabstract = {Fault diagnosis of rotating machinery plays a significant role for the reliability and safety of modern industrial systems. As an emerging field in industrial applications and an effective solution for fault recognition, artificial intelligence (AI) techniques have been receiving increasing attention from academia and industry. However, great challenges are met by the AI methods under the different real operating conditions. This paper attempts to present a comprehensive review of AI algorithms in rotating machinery fault diagnosis, from both the views of theory background and industrial applications. A brief introduction of different AI algorithms is presented first, including the following methods: k-nearest neighbour, naive Bayes, support vector machine, artificial neural network and deep learning. Then, a broad literature survey of these AI algorithms in industrial applications is given. 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