Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment
Anomaly detection is critical for enhancing operational efficiency, safety, and maintenance in industrial applications, particularly in the era of Industry 4.0 and IoT. While traditional anomaly detection approaches face limitations such as scalability issues, high false alarm rates, and reliance on...
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Elsevier
2025-03-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025003627 |
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| author | Praveen Vijai Bagavathi Sivakumar P |
| author_facet | Praveen Vijai Bagavathi Sivakumar P |
| author_sort | Praveen Vijai |
| collection | DOAJ |
| description | Anomaly detection is critical for enhancing operational efficiency, safety, and maintenance in industrial applications, particularly in the era of Industry 4.0 and IoT. While traditional anomaly detection approaches face limitations such as scalability issues, high false alarm rates, and reliance on skilled expertise, this study proposes a novel approach using a BiLSTM-Variational Autoencoder (BiLSTM-VAE) model with a dynamic loss function. The proposed model addresses key challenges, including data imbalance, interpretability issues, and computational complexity. By leveraging the bidirectional capability of BiLSTM in the encoder and decoder, the model captures comprehensive temporal dependencies, enabling more effective anomaly detection. The innovative dynamic loss function integrates a tempering index mechanism with tuneable parameters (α and γ), which assigns higher weights to underrepresented classes and down-weights easily classified samples. This improves reconstruction and enhances detection accuracy, particularly for minority class anomalies. Experimental evaluations on the SKAB and TEP datasets demonstrate the superiority of the proposed framework. The model achieved an accuracy of 98% and an F1 score of 96% for binary classification on the SKAB dataset and a multiclass classification accuracy of 92% with an F1 score of 85% on the TEP dataset. These results significantly outperform state-of-the-art models, including traditional VAE, LSTM, and transformer-based approaches. The proposed BiLSTM-VAE model not only demonstrates robust anomaly detection capabilities across diverse datasets but also effectively handles data imbalance and reduces false positives, making it a scalable and reliable solution for industrial anomaly detection in the context of Industry 4.0 and IoT environments. |
| format | Article |
| id | doaj-art-4b741d5fb8f0459faed3bfed13a869a4 |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-4b741d5fb8f0459faed3bfed13a869a42025-02-12T05:31:44ZengElsevierResults in Engineering2590-12302025-03-0125104277Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environmentPraveen Vijai0Bagavathi Sivakumar P1Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaCorresponding author.; Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, IndiaAnomaly detection is critical for enhancing operational efficiency, safety, and maintenance in industrial applications, particularly in the era of Industry 4.0 and IoT. While traditional anomaly detection approaches face limitations such as scalability issues, high false alarm rates, and reliance on skilled expertise, this study proposes a novel approach using a BiLSTM-Variational Autoencoder (BiLSTM-VAE) model with a dynamic loss function. The proposed model addresses key challenges, including data imbalance, interpretability issues, and computational complexity. By leveraging the bidirectional capability of BiLSTM in the encoder and decoder, the model captures comprehensive temporal dependencies, enabling more effective anomaly detection. The innovative dynamic loss function integrates a tempering index mechanism with tuneable parameters (α and γ), which assigns higher weights to underrepresented classes and down-weights easily classified samples. This improves reconstruction and enhances detection accuracy, particularly for minority class anomalies. Experimental evaluations on the SKAB and TEP datasets demonstrate the superiority of the proposed framework. The model achieved an accuracy of 98% and an F1 score of 96% for binary classification on the SKAB dataset and a multiclass classification accuracy of 92% with an F1 score of 85% on the TEP dataset. These results significantly outperform state-of-the-art models, including traditional VAE, LSTM, and transformer-based approaches. The proposed BiLSTM-VAE model not only demonstrates robust anomaly detection capabilities across diverse datasets but also effectively handles data imbalance and reduces false positives, making it a scalable and reliable solution for industrial anomaly detection in the context of Industry 4.0 and IoT environments.http://www.sciencedirect.com/science/article/pii/S2590123025003627Anomaly detectionDeep learningVariational autoencoderBidirectional long short term memory modelDynamic loss function |
| spellingShingle | Praveen Vijai Bagavathi Sivakumar P Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment Results in Engineering Anomaly detection Deep learning Variational autoencoder Bidirectional long short term memory model Dynamic loss function |
| title | Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment |
| title_full | Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment |
| title_fullStr | Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment |
| title_full_unstemmed | Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment |
| title_short | Anomaly detection solutions: The dynamic loss approach in VAE for manufacturing and IoT environment |
| title_sort | anomaly detection solutions the dynamic loss approach in vae for manufacturing and iot environment |
| topic | Anomaly detection Deep learning Variational autoencoder Bidirectional long short term memory model Dynamic loss function |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025003627 |
| work_keys_str_mv | AT praveenvijai anomalydetectionsolutionsthedynamiclossapproachinvaeformanufacturingandiotenvironment AT bagavathisivakumarp anomalydetectionsolutionsthedynamiclossapproachinvaeformanufacturingandiotenvironment |