An Energy Consumption Auditing Anomaly Detection System of Robotic Manipulators based on a Generative Adversarial Network

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Published Oct 26, 2023
Ge Song Seong Hyeon Hong Tristan Kyzer Yi Wang

Abstract

Unexpected anomalies pose significant risks to the health and security of intelligent manufacturing systems. This paper proposes a generative adversarial network (GAN)-based anomaly detection framework specifically for monitoring robotic manipulator operation using a side-channel energy auditing mechanism. To tackle the limitation arising from the lack of labeled data, the GAN model is trained by a semi-supervised learning approach that identifies anomalies during online operations as outliers. The overfitting is purposely utilized during the model training to enlarge the difference between normal energy consumption patterns used for training and anomalous profiles in real-time testing. In addition, the GAN model is modified to use multiple discriminators to analyze the individual energy profile associated with each joint or motor. The anomaly is detected by evaluating the mean and standard deviation values of anomaly scores' distribution, and both values are continuously updated by Welford's algorithm in real time to take into account the effect of environmental variations during operations. The detection performance on our custom dataset demonstrates the feasibility of the proposed pipeline. Specifically, for physical attacks, the framework can achieve an accuracy of approximately 0.93 for instant-wise detection and 0.84 for event-wise detection.

How to Cite

Song, G., Hong, S. H., Kyzer, T., & Wang, Y. (2023). An Energy Consumption Auditing Anomaly Detection System of Robotic Manipulators based on a Generative Adversarial Network. Annual Conference of the PHM Society, 15(1). https://doi.org/10.36001/phmconf.2023.v15i1.3496
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Keywords

Robotic manipulator, Energy auditing, Generative adversarial network, Anomaly detection, Cyber-Physical attack

References
Contreras-Cruz, M. A., Correa-Tome, F. E., Lopez-Padilla, R., & Ramirez-Paredes, J.-P. (2023). Generative Adversarial Networks for anomaly detection in aerial images. Computers and Electrical Engineering, 106, 108470.

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., . . . Bengio, Y. (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144.

Jiang, Z., Song, G., Qian, Y., & Wang, Y. (2022). A deep learning framework for detecting and localizing abnormal pedestrian behaviors at grade crossings. Neural Computing and Applications, 1-15.

Lian, Y., Geng, Y., & Tian, T. (2023). Anomaly Detection Method for Multivariate Time Series Data of Oil and Gas Stations Based on Digital Twin and MTAD-GAN. Applied Sciences, 13(3), 1891.

Memarzadeh, M., Matthews, B., Templin, T., Sharif Rohani, A., & Weckler, D. (2023). Semi-Supervised Active Learning for Anomaly Detection in Aviation. Journal of Aerospace Information Systems, 20(4), 181-194.

Nguyen, N. V., Hum, A. J., Do, T., & Tran, T. (2023). Semi-supervised machine learning of optical in-situ monitoring data for anomaly detection in laser powder bed fusion. Virtual and Physical Prototyping, 18(1), e2129396.

Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018). Adversarially learned one-class classifier for novelty detection. IEEE conference on computer vision and pattern recognition, (pp. 3379-3388).

Welford, B. (1962). Note on a method for calculating corrected sums of squares and products. Technometrics, 4(3), 419-420.

Yan, H., Liu, Z., Chen, J., Feng, Y., & Wang, J. (2023). Memory-augmented skip-connected autoencoder for unsupervised anomaly detection of rocket engines with multi-source fusion. ISA transactions, 133, 53-65.
Section
Technical Research Papers