Please use this identifier to cite or link to this item:
|Publication type:||Article in scientific journal|
|Type of review:||Peer review (publication)|
|Title:||Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot|
van de Venn, Hans Wernher
|Publisher / Ed. Institution:||MDPI|
|Subjects:||Anomaly detection; Autoencoder; Data-driven maintenance; Deep learning; Gaussian processes; Predictive maintenance; Normal distribution; Probability; Robotics|
|Subject (DDC):||006: Special computer methods |
621.3: Electrical, communications, control engineering
|Abstract:||Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 DoF delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data are available. Due to the sequential nature of the data, nonlinearity of the system, and correlations between parameter time-series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method can calculate RUL using Gaussian process (GP), as a degradation model, given HI values as its input.|
|Fulltext version:||Published version|
|License (according to publishing contract):||CC BY 4.0: Attribution 4.0 International|
|Departement:||School of Engineering|
|Organisational Unit:||Institute of Mechatronic Systems (IMS)|
|Appears in collections:||Publikationen School of Engineering|
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|2021_Fathi-etal_Predictive-maintenance_Sensors.pdf||3.08 MB||Adobe PDF|
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Fathi, K., van de Venn, H. W., & Honegger, M. (2021). Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot. Sensors, 21(21), 6979. https://doi.org/10.3390/s21216979
Fathi, K., van de Venn, H.W. and Honegger, M. (2021) ‘Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot’, Sensors, 21(21), p. 6979. Available at: https://doi.org/10.3390/s21216979.
K. Fathi, H. W. van de Venn, and M. Honegger, “Predictive maintenance : an autoencoder anomaly-based approach for a 3 DoF delta robot,” Sensors, vol. 21, no. 21, p. 6979, Oct. 2021, doi: 10.3390/s21216979.
Fathi, Kiavash, et al. “Predictive Maintenance : An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot.” Sensors, vol. 21, no. 21, Oct. 2021, p. 6979, https://doi.org/10.3390/s21216979.
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