ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Dulce María Ibarra Montes
Universidad de Guadalajara
0009-0002-2939-3066
Francisco González Barriga
Universidad de Guadalajara
0009-0005-0340-4419
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Jesús Hernández Barragan
Universidad de Guadalajara
0000-0001-7518-1668
Gabriel Martínez Soltero
Universidad de Guadalajara
0000-0003-3538-8132
Acerca de
Fault detection is a topic of great relevance due to its importance in the control of various types of systems. In particular, the most common faults usually occur in actuators or sensors. This paper proposes the use of autoencoders for the detection of faults in the joint sensors of a robotic manipulator during trajectory tracking tasks. The proposed methodology addresses the identification of faults caused by disconnections, noise and blockage in the sensors of all the joints of the manipulator. In addition, a differential evolution optimization algorithm was implemented to select the optimal threshold that determines whether or not a fault occurs, which increases the accuracy of the system. The applicability of this approach is demonstrated by detecting faults in the sensors of a 5-degree-of-freedom (DOF) manipulator. The results obtained show that autoencoders are a powerful and versatile tool for fault detection in robotic manipulators, at least in most of their joints.
Referencias
Abboush, M., Knieke, C., Rausch, A.: Machine learning-assisted test records anal- ysis during the real-time validation of automotive software systems based on hil simulation (2025)
Aggarwal, C.C., Aggarwal, C.C.: An introduction to outlier analysis. Springer (2017)
Ai, X., Liu, Y., Shan, L., Gao, J., Zhang, W.: A robust diagnosis method specifically for similar faults in nuclear power plant multi-systems based on data segmentation and stacked convolutional autoencoders. Annals of Nuclear Energy 213, 111178 (2025)
Chen, T., Liu, X., Xia, B., Wang, W., Lai, Y.: Unsupervised anomaly detection of industrial robots using sliding-window convolutional variational autoencoder. IEEE Access 8, 47072–47081 (2020)
Chen, Z., Li, W.: Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Transactions on instrumentation and measurement 66(7), 1693–1702 (2017)
Deng, W., Shang, S., Cai, X., Zhao, H., Song, Y., Xu, J.: An improved differential evolution algorithm and its application in optimization problem. Soft Computing 25, 5277–5298 (2021)
Han, P., Ellefsen, A.L., Li, G., Holmeset, F.T., Zhang, H.: Fault detection with lstm-based variational autoencoder for maritime components. IEEE Sensors Jour- nal 21(19), 21903–21912 (2021)
He, Y., Du, L., Chen, W., Chen, F., Xu, Y.: Missing signal reconstruction and aileron fault detection via generative adversarial learning. Neurocomputing 620, 129205 (2025)
Isermann, R.: Fault-diagnosis applications: model-based condition monitoring: ac- tuators, drives, machinery, plants, sensors, and fault-tolerant systems. Springer Science & Business Media (2011)
Jana, D., Patil, J., Herkal, S., Nagarajaiah, S., Duenas-Osorio, L.: Cnn and convo- lutional autoencoder (cae) based real-time sensor fault detection, localization, and correction. Mechanical Systems and Signal Processing 169, 108723 (2022)
Jiang, G., Xie, P., He, H., Yan, J.: Wind turbine fault detection using a denoising autoencoder with temporal information. IEEE/Asme transactions on mechatronics 23(1), 89–100 (2017)
Jiang, X., Zhang, Z., Yuan, H., He, J., Tong, Y.: Dae-bilstm model for accurate diagnosis of bearing faults in escalator principal drive systems. Processes 13(1), 202 (2025)
Kang, G., Gao, S., Yu, L., Zhang, D.: Deep architecture for high-speed railway insulator surface defect detection: Denoising autoencoder with multitask learning. IEEE Transactions on Instrumentation and Measurement 68(8), 2679–2690 (2018)
Khalid, S., Azad, M.M., Kim, H.S.: A generalized autonomous power plant fault detection model using deep feature extraction and ensemble machine learning. Mathematics 13(3), 342 (2025)
Kim, J., Ko, J., Choi, H., Kim, H.: Printed circuit board defect detection using deep learning via a skip-connected convolutional autoencoder. Sensors 21(15), 4968 (2021)
Kramer, M.A.: Nonlinear principal component analysis using autoassociative neu- ral networks. AIChE journal 37(2), 233–243 (1991)
Mallak, A., Fathi, M.: Sensor and component fault detection and diagnosis for hy- draulic machinery integrating lstm autoencoder detector and diagnostic classifiers. Sensors 21(2), 433 (2021)
Park, P., Marco, P.D., Shin, H., Bang, J.: Fault detection and diagnosis using combined autoencoder and long short-term memory network. Sensors 19(21), 4612 (2019)
Siciliano, B., Khatib, O., Kröger, T.: Springer handbook of robotics, vol. 200. Springer (2008)
Simon, D.: Evolutionary optimization algorithms. John Wiley & Sons (2013)
Spong, M.W., Hutchinson, S., Vidyasagar, M.: Robot modeling and control. John Wiley & Sons (2020)
Tsai, D.M., Jen, P.H.: Autoencoder-based anomaly detection for surface defect inspection. Advanced Engineering Informatics 48, 101272 (2021)
Wu, X., Jiang, G., Wang, X., Xie, P., Li, X.: A multi-level-denoising autoencoder approach for wind turbine fault detection. Ieee Access 7, 59376–59387 (2019)