Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions
A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable pa...
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| Formato: | Artigo |
| Idioma: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
| Subjects: | |
| Acceso en liña: | https://www.mdpi.com/2227-7390/11/2/398 |
| _version_ | 1827623401658777600 |
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| author | Hyewon Lee Izaz Raouf Jinwoo Song Heung Soo Kim Soobum Lee |
| author_facet | Hyewon Lee Izaz Raouf Jinwoo Song Heung Soo Kim Soobum Lee |
| author_sort | Hyewon Lee |
| collection | DOAJ |
| description | A robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model’s performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment. |
| first_indexed | 2024-03-09T11:46:13Z |
| format | Article |
| id | doaj.art-1a24d92c91594e86a032dce1a06f2c3e |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T11:46:13Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-1a24d92c91594e86a032dce1a06f2c3e2023-11-30T23:21:38ZengMDPI AGMathematics2227-73902023-01-0111239810.3390/math11020398Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating ConditionsHyewon Lee0Izaz Raouf1Jinwoo Song2Heung Soo Kim3Soobum Lee4Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of KoreaDepartment of Mechanical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USAA robot is essential in many industrial and manufacturing facilities due to its efficiency, accuracy, and durability. However, continuous use of the robotic system can result in various component failures. The servo motor is one of the critical components, and its bearing is one of the vulnerable parts, hence failure analysis is required. Some previous prognostics and health management (PHM) methods are very limited in considering the realistic operating conditions of industrial robots based on various operating speeds, loading conditions, and motions, because they consider constant speed data with unloading conditions. This paper implements a PHM for the servo motor of a robotic arm based on variable operating conditions. Principal component analysis-based dimensionality reduction and correlation analysis-based feature selection are compared. Two machine learning algorithms have been used to detect fault features under various operating conditions. This method is proposed as a robust fault-detection model for industrial robots under various operating conditions. Features from different domains not only improved the generalization of the model’s performance but also improved the computational efficiency of massive data by reducing the total number of features. The results showed more than 90% accuracy under various operating conditions. As a result, the proposed method shows the possibility of robust failure diagnosis under various operating conditions similar to the actual industrial environment.https://www.mdpi.com/2227-7390/11/2/398artificial neural networkfault detectionfeature extractionmotor current signature analysisservo motor |
| spellingShingle | Hyewon Lee Izaz Raouf Jinwoo Song Heung Soo Kim Soobum Lee Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions Mathematics artificial neural network fault detection feature extraction motor current signature analysis servo motor |
| title | Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions |
| title_full | Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions |
| title_fullStr | Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions |
| title_full_unstemmed | Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions |
| title_short | Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions |
| title_sort | prognostics and health management of the robotic servo motor under variable operating conditions |
| topic | artificial neural network fault detection feature extraction motor current signature analysis servo motor |
| url | https://www.mdpi.com/2227-7390/11/2/398 |
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