@inproceedings { , title = {Deep convolutional neural network with 2D spectral energy maps for fault diagnosis of gearboxes under variable speed.}, abstract = {For industrial safety, correct classification of gearbox fault conditions is necessary. One of the most crucial tasks in data-driven fault diagnosis is determining the best set of features by analyzing the statistical parameters of the signals. However, under variable speed conditions, these statistical parameters are incapable of uncovering the dynamic characteristics of different fault conditions of gearboxes. Later, several deep learning algorithms are used to improve the performance of the feature selection process, but domain knowledge expertise is still necessary. In this paper, a combination domain knowledge analysis and a deep neural network is proposed. By using the input acoustic emission (AE) signal, a two-dimensional spectrum energy map (2D AE-SEM) is created to form an identical fault pattern for various speed conditions of gearboxes. Then, a deep convolutional neural network (DCNN) is proposed to investigate the detailed structure of the 2D input for final fault classification. This 2D AE-SEM offers a graphical depiction of acoustic emission spectral characteristics. Our proposed system offers vigorous and dynamic classification performance through the proposed DCNN with a high diagnostic fault classification accuracy of 96.37\% in all considered scenarios.}, conference = {3rd Mediterranean conference on pattern recognition and artificial intelligence (MedPRAI 2019)}, doi = {10.1007/978-3-030-37548-5\_9}, isbn = {9783030375478}, note = {INFO COMPLETE (Record added by contact, updated 8/11/2022 LM) PERMISSION GRANTED (version = AAM; embargo = 24 months; licence = Pub's own; POLICY = https://www.springernature.com/gp/open-research/policies/book-policies 8/11/2022 LM) DOCUMENT READY (AAM rec'd from contact 10/11/2022 LM) ADDITIONAL INFO - Contact: Md Junayed Hasan Set Statement - (This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-030-37548-5\_9. Use of this Accepted Version is subject to the publisher's Accepted Manuscript terms of use [https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms].)}, pages = {106-117}, publicationstatus = {Published}, publisher = {Springer}, url = {https://rgu-repository.worktribe.com/output/1664728}, keyword = {Gearbox safety, Fault diagnosis, Convolutional neural network}, year = {2020}, author = {Hasan, Md Junayed and Kim, Jongmyon} editor = {Djeddi, Chawki and Jamil, Akhtar and Siddiqi, Imran} }