Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach

Khalid Almutairi; Jyoti Sinha; Haobin Wen

doi:10.1007/978-3-031-39619-9_40

Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach

Khalid Almutairi, Jyoti Sinha, Haobin Wen

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

Original language	English
Title of host publication	International Congress and Workshop on Industrial AI and eMaintenance 2023
Editors	Uday Kumar, Ramin Karim, Diego Galar, Ravdeep Kour
Publisher	Springer Nature
Pages	545-556
ISBN (Electronic)	978-3-031-39619-9
ISBN (Print)	978-3-031-39618-2
DOIs	https://doi.org/10.1007/978-3-031-39619-9_40
Publication status	Published - Jan 2024

Publication series

Name	Lecture Notes in Mechanical Engineering
ISSN (Electronic)	2195-4364

Access to Document

10.1007/978-3-031-39619-9_40

Cite this

Almutairi, K., Sinha, J., & Wen, H. (2024). Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach. In U. Kumar, R. Karim, D. Galar, & R. Kour (Eds.), International Congress and Workshop on Industrial AI and eMaintenance 2023 (pp. 545-556). (Lecture Notes in Mechanical Engineering ). Springer Nature. https://doi.org/10.1007/978-3-031-39619-9_40

Almutairi, Khalid ; Sinha, Jyoti ; Wen, Haobin. / Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach. International Congress and Workshop on Industrial AI and eMaintenance 2023. editor / Uday Kumar ; Ramin Karim ; Diego Galar ; Ravdeep Kour. Springer Nature, 2024. pp. 545-556 (Lecture Notes in Mechanical Engineering ).

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Almutairi, K, Sinha, J & Wen, H 2024, Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach. in U Kumar, R Karim, D Galar & R Kour (eds), International Congress and Workshop on Industrial AI and eMaintenance 2023. Lecture Notes in Mechanical Engineering , Springer Nature, pp. 545-556. https://doi.org/10.1007/978-3-031-39619-9_40

Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach. / Almutairi, Khalid; Sinha, Jyoti; Wen, Haobin.
International Congress and Workshop on Industrial AI and eMaintenance 2023. ed. / Uday Kumar; Ramin Karim; Diego Galar; Ravdeep Kour. Springer Nature, 2024. p. 545-556 (Lecture Notes in Mechanical Engineering ).

Research output: Chapter in Book/Conference proceeding › Chapter › peer-review

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T1 - Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach

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AU - Sinha, Jyoti

AU - Wen, Haobin

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A2 - Karim, Ramin

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Almutairi K, Sinha J, Wen H. Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach. In Kumar U, Karim R, Galar D, Kour R, editors, International Congress and Workshop on Industrial AI and eMaintenance 2023. Springer Nature. 2024. p. 545-556. (Lecture Notes in Mechanical Engineering ). doi: 10.1007/978-3-031-39619-9_40

Rotor and Bearing Fault Classification of Rotating Machinery Using Extracted Features from Experimental Vibration Data and Machine Learning Approach

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