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Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot

Institute of Mechatronic Systems, Zurich University of Applied Sciences, 8400 Winterthur, Switzerland
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Academic Editor: Biswanath Samanta
Sensors 2021, 21(21), 6979; https://doi.org/10.3390/s21216979
Received: 6 September 2021 / Revised: 12 October 2021 / Accepted: 19 October 2021 / Published: 21 October 2021
(This article belongs to the Topic New Frontiers in Industry 4.0)
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. View Full-Text
Keywords: predictive maintenance; anomaly detection; autoencoder; gaussian processes; deep learning; data-driven maintenance predictive maintenance; anomaly detection; autoencoder; gaussian processes; deep learning; data-driven maintenance
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MDPI and ACS Style

Fathi, K.; van de Venn, H.W.; Honegger, M. Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot. Sensors 2021, 21, 6979. https://doi.org/10.3390/s21216979

AMA Style

Fathi K, van de Venn HW, Honegger M. Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot. Sensors. 2021; 21(21):6979. https://doi.org/10.3390/s21216979

Chicago/Turabian Style

Fathi, Kiavash, Hans W. van de Venn, and Marcel Honegger. 2021. "Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot" Sensors 21, no. 21: 6979. https://doi.org/10.3390/s21216979

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