Prognostics and Health Management of the Robotic Servo-Motor under Variable Operating Conditions

Hyewon Lee, Izaz Raouf, Jinwoo Song, Heung Soo Kim () and Soobum Lee
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Hyewon Lee: Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Izaz Raouf: Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Jinwoo Song: Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Heung Soo Kim: Department of Mechanical, Robotics and Energy Engineering, Dongguk University Seoul, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, Republic of Korea
Soobum Lee: Department of Mechanical Engineering, University of Maryland Baltimore County, Baltimore, MD 21250, USA

Mathematics, 2023, vol. 11, issue 2, 1-17

Abstract: 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.

Keywords: artificial neural network; fault detection; feature extraction; motor current signature analysis; servo motor (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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