Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation
This paper presents a novel approach for detecting sensor failures using image-based 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 limitatio...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/7/2175 |
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| author | Luis Miguel Moreno Haro Adaiton Oliveira-Filho Bruno Agard Antoine Tahan |
| author_facet | Luis Miguel Moreno Haro Adaiton Oliveira-Filho Bruno Agard Antoine Tahan |
| author_sort | Luis Miguel Moreno Haro |
| collection | DOAJ |
| description | This paper presents a novel approach for detecting sensor failures using image-based 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. |
| format | Article |
| id | doaj-art-d64b445a603b43d1b4f20f65b6ff7c15 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d64b445a603b43d1b4f20f65b6ff7c152025-08-20T03:08:56ZengMDPI AGSensors1424-82202025-03-01257217510.3390/s25072175Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature RepresentationLuis Miguel Moreno Haro0Adaiton Oliveira-Filho1Bruno Agard2Antoine Tahan3Laboratoire en Intelligence des Données, Department of Mathematical and Industrial Engineering, Polytechnique Montréal, Montréal, QC H3T 0A3, CanadaDepartment of Mechanical Engineering, École de Technologie Supérieure, Montréal, QC H3C 1K3, CanadaLaboratoire en Intelligence des Données, Department of Mathematical and Industrial Engineering, Polytechnique Montréal, Montréal, QC H3T 0A3, CanadaDepartment of Mechanical Engineering, École de Technologie Supérieure, Montréal, QC H3C 1K3, CanadaThis paper presents a novel approach for detecting sensor failures using image-based 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.https://www.mdpi.com/1424-8220/25/7/2175sensor failure detectionhealth indexvariational autoencoderfeature representationaeronautical sensors |
| spellingShingle | Luis Miguel Moreno Haro Adaiton Oliveira-Filho Bruno Agard Antoine Tahan Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation Sensors sensor failure detection health index variational autoencoder feature representation aeronautical sensors |
| title | Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation |
| title_full | Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation |
| title_fullStr | Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation |
| title_full_unstemmed | Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation |
| title_short | Failure Detection in Sensors via Variational Autoencoders and Image-Based Feature Representation |
| title_sort | failure detection in sensors via variational autoencoders and image based feature representation |
| topic | sensor failure detection health index variational autoencoder feature representation aeronautical sensors |
| url | https://www.mdpi.com/1424-8220/25/7/2175 |
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