System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes
In the manufacturing industry, robots are constantly operated at high speed, which degrades their performance by the degradation of internal components, eventually reaching failure. To address this issue, a framework for system-level fault diagnosis is proposed, which consists of extracting useful f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/18/10243 |
| _version_ | 1797581477588762624 |
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| author | Inu Lee Hyung Jun Park Jae-Won Jang Chang-Woo Kim Joo-Ho Choi |
| author_facet | Inu Lee Hyung Jun Park Jae-Won Jang Chang-Woo Kim Joo-Ho Choi |
| author_sort | Inu Lee |
| collection | DOAJ |
| description | In the manufacturing industry, robots are constantly operated at high speed, which degrades their performance by the degradation of internal components, eventually reaching failure. To address this issue, a framework for system-level fault diagnosis is proposed, which consists of extracting useful features from the motor control signal acquired during the operation, diagnosing the current health of each component using the features, and estimating the associated degradation in the robot system’s performance. Finally, a maintenance strategy is determined by evaluating how well the system performance is restored by the replacement of each component. The framework is demonstrated using the example of a wafer transfer robot in the semiconductor industry, in which the robot is operated under faults with various severities for two critical components: the harmonic drive and the timing belt. Features are extracted for the motor signal using wavelet packet decomposition, followed by feature selection by considering the trendability and separability of the fault severity. An artificial neural network model and Gaussian process regression are employed for the diagnosis of the components’ health and the system’s performance, respectively. |
| first_indexed | 2024-03-10T23:05:04Z |
| format | Article |
| id | doaj.art-fe49b8ecc3dd41159a906b1ef14e420d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T23:05:04Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-fe49b8ecc3dd41159a906b1ef14e420d2023-11-19T09:25:00ZengMDPI AGApplied Sciences2076-34172023-09-0113181024310.3390/app131810243System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure ModesInu Lee0Hyung Jun Park1Jae-Won Jang2Chang-Woo Kim3Joo-Ho Choi4Department of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang-si 10540, Republic of KoreaDepartment of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang-si 10540, Republic of KoreaDepartment of Smart Air Mobility, Korea Aerospace University, Goyang-si 10540, Republic of KoreaCymechs, Hwaseong-si 18487, Republic of KoreaSchool of Aerospace & Mechanical Engineering, Korea Aerospace University, Goyang-si 10540, Republic of KoreaIn the manufacturing industry, robots are constantly operated at high speed, which degrades their performance by the degradation of internal components, eventually reaching failure. To address this issue, a framework for system-level fault diagnosis is proposed, which consists of extracting useful features from the motor control signal acquired during the operation, diagnosing the current health of each component using the features, and estimating the associated degradation in the robot system’s performance. Finally, a maintenance strategy is determined by evaluating how well the system performance is restored by the replacement of each component. The framework is demonstrated using the example of a wafer transfer robot in the semiconductor industry, in which the robot is operated under faults with various severities for two critical components: the harmonic drive and the timing belt. Features are extracted for the motor signal using wavelet packet decomposition, followed by feature selection by considering the trendability and separability of the fault severity. An artificial neural network model and Gaussian process regression are employed for the diagnosis of the components’ health and the system’s performance, respectively.https://www.mdpi.com/2076-3417/13/18/10243system-level fault diagnosisindustrial robotfault severitymulti-component failure modeswavelet packet decomposition |
| spellingShingle | Inu Lee Hyung Jun Park Jae-Won Jang Chang-Woo Kim Joo-Ho Choi System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes Applied Sciences system-level fault diagnosis industrial robot fault severity multi-component failure modes wavelet packet decomposition |
| title | System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes |
| title_full | System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes |
| title_fullStr | System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes |
| title_full_unstemmed | System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes |
| title_short | System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes |
| title_sort | system level fault diagnosis for an industrial wafer transfer robot with multi component failure modes |
| topic | system-level fault diagnosis industrial robot fault severity multi-component failure modes wavelet packet decomposition |
| url | https://www.mdpi.com/2076-3417/13/18/10243 |
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