{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:44:53Z","timestamp":1767707093265,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T00:00:00Z","timestamp":1725926400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Spanish Government and FEDER funds (AEI\/FEDER, UE)","award":["PID2021-124502OB-C4","BOC 18-10-2021"],"award-info":[{"award-number":["PID2021-124502OB-C4","BOC 18-10-2021"]}]},{"name":"Universidad de Cantabria","award":["PID2021-124502OB-C4","BOC 18-10-2021"],"award-info":[{"award-number":["PID2021-124502OB-C4","BOC 18-10-2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidate the decision-making processes of these black box models and evaluate their behaviour. By analysing the inner workings of the models, we aim at providing insights into the factors influencing failure predictions. Through comprehensive experimentation and analysis on sensor data collected from a water pump, this study contributes to the understanding of deep learning methodologies for anomaly detection and failure prediction and underscores the importance of model interpretability in critical applications such as prognostics and health management. Additionally, we specify the architecture for deploying these models in a real environment using the RAI4.0 metamodel, meant for designing, configuring and automatically deploying distributed stream-based industrial applications. Our findings will offer valuable guidance for practitioners seeking to deploy deep learning techniques effectively in predictive maintenance systems, facilitating informed decision-making and enhancing reliability and efficiency in industrial operations.<\/jats:p>","DOI":"10.3390\/info15090557","type":"journal-article","created":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T04:35:24Z","timestamp":1725942924000},"page":"557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Design, Building and Deployment of Smart Applications for Anomaly Detection and Failure Prediction in Industrial Use Cases"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3163-0473","authenticated-orcid":false,"given":"Ricardo","family":"Dint\u00e9n","sequence":"first","affiliation":[{"name":"ISTR Group, Facultad de Ciencias, Universidad de Cantabria, Avenida de los Castros s\/n, 39005 Santander, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0475-8834","authenticated-orcid":false,"given":"Marta","family":"Zorrilla","sequence":"additional","affiliation":[{"name":"ISTR Group, Facultad de Ciencias, Universidad de Cantabria, Avenida de los Castros s\/n, 39005 Santander, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s43681-021-00132-6","article-title":"Artificial intelligence and real-time predictive maintenance in industry 4.0: A bibliometric analysis","volume":"2","author":"Keleko","year":"2022","journal-title":"AI Ethics"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ucar, A., Karakose, M., and K\u0131r\u0131m\u00e7a, N. 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