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Liu S, Zhou S, Li B, Niu Z, Abdullah M, Wang R. Servo torque fault diagnosis implementation for heavy-legged robots using insufficient information. ISA TRANSACTIONS 2024; 147:439-452. [PMID: 38350797 DOI: 10.1016/j.isatra.2024.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 12/02/2023] [Accepted: 02/07/2024] [Indexed: 02/15/2024]
Abstract
The reliability of sensors and servos is paramount in diagnosing the Heavy-Legged Robot (HLR). Servo faults stemming from mechanical wear, environmental disturbances, or electrical issues pose significant challenges to traditional diagnostic methods, which rely heavily on delicate sensors. This study introduces a framework that solely relies on joint position and permanent magnet synchronous motor (PMSM) information to mitigate dependency on fragile sensors for servo-fault diagnosis. An essential contribution involves refining a model that directly connects PMSM currents to HLR motion. Moreover, to address scenarios where actual servo outputs and HLR cylinder velocities are unavailable, an improved sliding mode observer (ISMO) is proposed. Additionally, a Fourier expansion model characterizes the relationship between operation time and fault-free disturbance in the HLR. Subsequently, the dual-line particle filter (DPF) algorithm is employed to predict fault-free disturbance. The outputs of DPF serve as a feedforward to the ISMO, enabling the real-time servo torque fault diagnosis. The accuracy and validity of this technical framework are verified through various simulations in MATLAB/SIMSCAPE and real-world experiments.
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Affiliation(s)
- Shaoxun Liu
- Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dongchuan RD. Minhang District, Shanghai, 200240, China.
| | - Shiyu Zhou
- Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dongchuan RD. Minhang District, Shanghai, 200240, China.
| | - Boyuan Li
- Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dongchuan RD. Minhang District, Shanghai, 200240, China.
| | - Zhihua Niu
- Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dongchuan RD. Minhang District, Shanghai, 200240, China.
| | - Mohamed Abdullah
- Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dongchuan RD. Minhang District, Shanghai, 200240, China.
| | - Rongrong Wang
- Shanghai Jiao Tong University, School of Mechanical Engineering, 800 Dongchuan RD. Minhang District, Shanghai, 200240, China.
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Zhao J, Zhang K, Hou M, Zhang H, Bai Y, Huang Y, Li J. Actuator fault detection for masonry robot manipulator arm with the interval observer. J FIELD ROBOT 2022. [DOI: 10.1002/rob.22114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Jinbao Zhao
- The School of Mechanical Engineering Shenyang Jianzhu University Shenyang China
| | - Ke Zhang
- The School of Mechanical Engineering Shenyang Jianzhu University Shenyang China
| | - Maxiao Hou
- The School of Mechanical Engineering Shenyang Jianzhu University Shenyang China
- The School of Mechanical Engineering Xian Jiaotong University Xian China
| | - Hao Zhang
- The School of Mechanical Engineering Shenyang Jianzhu University Shenyang China
- Langfang Kaibo Construction Machinery Technology Corp. Ltd. Hebei China
| | - Yunfei Bai
- The School of Mechanical Engineering Shenyang Jianzhu University Shenyang China
- Langfang Kaibo Construction Machinery Technology Corp. Ltd. Hebei China
| | - Yanzheng Huang
- The School of Mechanical Engineering Shenyang Jianzhu University Shenyang China
- The Company of China Construction Seventh Engineering Division Corp. Ltd. Zhengzhou China
| | - Jianan Li
- The School of Mechanical Engineering Shenyang Jianzhu University Shenyang China
- The Company of China Construction Seventh Engineering Division Corp. Ltd. Zhengzhou China
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Czubenko M, Kowalczuk Z. A Simple Neural Network for Collision Detection of Collaborative Robots. SENSORS 2021; 21:s21124235. [PMID: 34205579 PMCID: PMC8234637 DOI: 10.3390/s21124235] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 06/14/2021] [Accepted: 06/17/2021] [Indexed: 02/02/2023]
Abstract
Due to the epidemic threat, more and more companies decide to automate their production lines. Given the lack of adequate security or space, in most cases, such companies cannot use classic production robots. The solution to this problem is the use of collaborative robots (cobots). However, the required equipment (force sensors) or alternative methods of detecting a threat to humans are usually quite expensive. The article presents the practical aspect of collision detection with the use of a simple neural architecture. A virtual force and torque sensor, implemented as a neural network, may be useful in a team of collaborative robots. Four different approaches are compared in this article: auto-regressive (AR), recurrent neural network (RNN), convolutional long short-term memory (CNN-LSTM) and mixed convolutional LSTM network (MC-LSTM). These architectures are analyzed at different levels of input regression (motor current, position, speed, control velocity). This sensor was tested on the original CURA6 robot prototype (Cooperative Universal Robotic Assistant 6) by Intema. The test results indicate that the MC-LSTM architecture is the most effective with the regression level set at 12 samples (at 24 Hz). The mean absolute prediction error obtained by the MC-LSTM architecture was approximately 22 Nm. The conducted external test (72 different signals with collisions) shows that the presented architecture can be used as a collision detector. The MC-LSTM collision detection f1 score with the optimal threshold was 0.85. A well-developed virtual sensor based on such a network can be used to detect various types of collisions of cobot or other mobile or stationary systems operating on the basis of human-machine interaction.
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Affiliation(s)
- Michał Czubenko
- Department of Robotics and Decision Systems, Faculty of Electronics Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland;
- Intema Sp. z o. o., Siennicka 25a, 80-758 Gdańsk, Poland
- Correspondence:
| | - Zdzisław Kowalczuk
- Department of Robotics and Decision Systems, Faculty of Electronics Telecommunications and Informatics, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland;
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Exploiting Generative Adversarial Networks as an Oversampling Method for Fault Diagnosis of an Industrial Robotic Manipulator. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10217712] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Data-driven machine learning techniques play an important role in fault diagnosis, safety, and maintenance of the industrial robotic manipulator. However, these methods require data that, more often that not, are hard to obtain, especially data collected from fault condition states and, without enough and appropriated (balanced) data, no acceptable performance should be expected. Generative adversarial networks (GAN) are receiving a significant interest, especially in the image analysis field due to their outstanding generative capabilities. This paper investigates whether or not GAN can be used as an oversampling tool to compensate for an unbalanced data set in an industrial manipulator fault diagnosis task. A comprehensive empirical analysis is performed taking into account six different scenarios for mitigating the unbalanced data, including classical under and oversampling (SMOTE) methods. In all of these, a wavelet packet transform is used for feature generation while a random forest is used for fault classification. Aspects such as loss functions, learning curves, random input distributions, data shuffling, and initial conditions were also considered. A non-parametric statistical test of hypotheses reveals that all GAN based fault-diagnosis outperforms both under and oversampling classical methods while, within GAN based methods, an average accuracy difference as high as 1.68% can be achieved.
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5
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Birjandi SAB, Haddadin S. Model-Adaptive High-Speed Collision Detection for Serial-Chain Robot Manipulators. IEEE Robot Autom Lett 2020. [DOI: 10.1109/lra.2020.3015187] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Nguyen NP, Huynh TT, Do XP, Xuan Mung N, Hong SK. Robust Fault Estimation Using the Intermediate Observer: Application to the Quadcopter. SENSORS 2020; 20:s20174917. [PMID: 32878080 PMCID: PMC7506654 DOI: 10.3390/s20174917] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 06/04/2020] [Accepted: 06/07/2020] [Indexed: 12/03/2022]
Abstract
In this paper, an actuator fault estimation technique is proposed for quadcopters under uncertainties. In previous studies, matching conditions were required for the observer design, but they were found to be complex for solving linear matrix inequalities (LMIs). To overcome these limitations, in this study, an improved intermediate estimator algorithm was applied to the quadcopter model, which can be used to estimate actuator faults and system states. The system stability was validated using Lyapunov theory. It was shown that system errors are uniformly ultimately bounded. To increase the accuracy of the proposed fault estimation algorithm, a magnitude order balance method was applied. Experiments were verified with four scenarios to show the effectiveness of the proposed algorithm. Two first scenarios were compared to show the effectiveness of the magnitude order balance method. The remaining scenarios were described to test the reliability of the presented method in the presence of multiple actuator faults. Different from previous studies on observer-based fault estimation, this proposal not only can estimate the fault magnitude of the roll, pitch, yaw, and thrust channel, but also can estimate the loss of control effectiveness of each actuator under uncertainties.
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Affiliation(s)
- Ngoc Phi Nguyen
- Department of Aerospace Engineering, Sejong University, Seoul 143-747 (05006), Korea; (N.P.N.); (N.X.M.)
| | - Tuan Tu Huynh
- Department of Electrical Engineering, Yuan Ze University, No. 135, Yuandong Road, Zhongli, Taoyuan 320, Taiwan;
- Department of Electrical Electronic and Mechanical Engineering, Lac Hong University, No. 10, Huynh Van Nghe Road, Bien Hoa, Dong Nai 810000, Vietnam
| | - Xuan Phu Do
- MediRobotics Laboratory, Department of Machatronics and Sensor Systems Technology, Vietnamese-German University, Binh Duong 820000, Vietnam;
| | - Nguyen Xuan Mung
- Department of Aerospace Engineering, Sejong University, Seoul 143-747 (05006), Korea; (N.P.N.); (N.X.M.)
| | - Sung Kyung Hong
- Department of Aerospace Engineering, Sejong University, Seoul 143-747 (05006), Korea; (N.P.N.); (N.X.M.)
- Correspondence:
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Miao P, Wu D, Shen Y, Zhang Z. Discrete-time neural network with two classes of bias noises for solving time-variant matrix inversion and application to robot tracking. Neural Comput Appl 2019. [DOI: 10.1007/s00521-018-03986-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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