Haro, Luis Miguel Moreno, Oliveira-Filho, Adaiton, Agard, Bruno et Tahan, Antoine.
2025.
« Failure detection in sensors via variational autoencoders and image-based feature representation ».
Sensors, vol. 25, nº 7.
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Résumé
This paper presents a novel approach for detecting sensor failures using imagebased feature representation and the Convolutional Variational Autoencoder (CVAE) model. Existing methods are limited when analyzing multiple failure modes simultaneously or adapting to diverse sensor data. This limitation may compromise decision-making and system performance, hence the need for more flexible and resilient models. The proposed approach transforms sensor data into image-based feature representations of statistics such as mean, variance, kurtosis, entropy, skewness, and correlation. The CVAE is trained on such image representations, and the corresponding reconstruction error leads to a Health Index (HI) for detecting multiple sensor failures. Moreover, the CVAE latent space is used to define a complementary HI and a convenient visualization tool, enhancing the interpretability of the proposed approach. The evaluation of the proposed detection approach with data comprising diverse configurations of faulty sensors showed encouraging results. The proposed approach is illustrated in an industrial case study emerging from the aeronautical domain, with data from a complex electromechanical system comprising nearly 80 sensor measurements at a 1 Hz sampling rate. The results demonstrate the potential of the proposed method in detecting multiple sensor failures.
| Type de document: | Article publié dans une revue, révisé par les pairs |
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| Professeur: | Professeur Tahan, Antoine |
| Affiliation: | Génie mécanique |
| Date de dépôt: | 30 avr. 2025 16:10 |
| Dernière modification: | 02 mai 2025 19:13 |
| URI: | https://espace2.etsmtl.ca/id/eprint/30855 |
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