{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:40:31Z","timestamp":1759970431830,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T00:00:00Z","timestamp":1737417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In this study, anomalies in a fan system were classified using a real measurement setup to simulate mechanical anomalies such as blade detachment or debris accumulation. Data were collected under normal operating conditions and with an added unbalancing mass. Additionally, sensor anomalies were introduced by manipulating accelerometer readings and examining three types: spike, stuck, and dropout. To classify the anomalies, four neural network models\u2014variations in Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) were tested. These models incorporated a Continuous Wavelet Transform (CWT) layer. A novel approach for implementing the CWT layer in both LSTM and CNN architectures was proposed, along with a dual-branch input structure featuring two CWT layers using different mother wavelets. The dual-branch configuration with different mother wavelets yielded better accuracy for the simpler LSTM network. Accuracy comparisons were conducted for the 10 best-performing models based on validation set predictions, revealing improved classification performance. The study concluded with a summary of prediction accuracy for both the validation and test sets of data, along with the calculation of average accuracy, demonstrating the effectiveness of the proposed dual-branch neural network structure in classifying anomalies in fan systems.<\/jats:p>","DOI":"10.3390\/info16020071","type":"journal-article","created":{"date-parts":[[2025,1,21]],"date-time":"2025-01-21T05:47:42Z","timestamp":1737438462000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Anomalies Classification in Fan Systems Using Dual-Branch Neural Networks with Continuous Wavelet Transform Layers: An Experimental Study"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-9099-4138","authenticated-orcid":false,"given":"Cezary","family":"Pa\u0142czy\u0144ski","sequence":"first","affiliation":[{"name":"Department of Automation, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Lodz University of Technology, 1\/15 Stefanowski Street, 90-537 Lodz, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3310-0951","authenticated-orcid":false,"given":"Pawe\u0142","family":"Olejnik","sequence":"additional","affiliation":[{"name":"Department of Automation, Biomechanics and Mechatronics, Faculty of Mechanical Engineering, Lodz University of Technology, 1\/15 Stefanowski Street, 90-537 Lodz, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kesharwani, A., and Shukla, P. 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