{"id":6785,"date":"2026-06-10T08:25:11","date_gmt":"2026-06-10T08:25:11","guid":{"rendered":"https:\/\/en.kecbu.uobaghdad.edu.iq\/?p=6785"},"modified":"2026-06-10T08:47:15","modified_gmt":"2026-06-10T08:47:15","slug":"al-khwarizmi-college-of-engineering-held-a-master-defense-on-distributed-split-learning-architecture-based-on-industrial-internet-of-things-for-predictive-maintenance","status":"publish","type":"post","link":"https:\/\/en.kecbu.uobaghdad.edu.iq\/?p=6785","title":{"rendered":"Al-Khwarizmi College of Engineering held a master defense on Distributed Split Learning Architecture Based on Industrial Internet of Things for Predictive Maintenance."},"content":{"rendered":"<div class=\"wpb-content-wrapper\"><p>[vc_row][vc_column][vc_column_text css=&#8221;&#8221;]<\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 17px;\">On Wednesday, June, 3<sup>rd<\/sup> ,2026 the department of Mechatronics engineering at Al-Khwarizmi College of engineering held a master thesis defense titled \u201cDistributed Split Learning Architecture Based on Industrial Internet of Things for Predictive Maintenance)),<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 17px;\">\u00a0for the master candidate (Hanen Hussein Adel) at the thesis defense hall, where a degree of passing with distinction was awarded for her research findings.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 17px;\">The committee is composed of the following members:<\/span><\/p>\n<ul style=\"text-align: justify;\">\n<li><span style=\"font-size: 17px;\">Professor Dr. Ameer Hussein Murad, Chair<\/span><\/li>\n<li><span style=\"font-size: 17px;\">Professor Dr. Waleed Khalid Khairi, Examiner<\/span><\/li>\n<li><span style=\"font-size: 17px;\">Assistant Professor Dr. Azzah Abdel Razzaq Abdelkarim, Examiner<\/span><\/li>\n<li><span style=\"font-size: 17px;\">Professor Dr. Osama Fadhil Abdullatif, 1<sup>st<\/sup> Supervisor<\/span><\/li>\n<li><span style=\"font-size: 17px;\">Assistant Professor Dr. Ali Hussein Hamad, 2<sup>nd<\/sup> Supervisor<\/span><\/li>\n<\/ul>\n<p style=\"text-align: justify;\"><span style=\"font-size: 17px;\">This thesis aimed to develop advanced procedure for predictive maintenance using data in industrial internet of things, since it represents a key enabler for improving equipment reliability and operational efficiency in Industrial Internet of Things (IoT) environments. However, centralized prediction-based artificial intelligence (AI) requires raw data to be uploaded to the server, raising privacy, scalability, and communication concerns. Also, a deep learning model on IoT nodes requires substantial computation.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 17px;\">This thesis proposes a distributed PdM framework based on split learning (SL) and split federated learning (SFL). It compares the performance with that of a centralized baseline deep neural network (DNN) and SCINet models.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 17px;\">The experimental setup consists of three AC motors equipped with vibration, current, and temperature sensors, connected to Raspberry Pi devices via the I2C protocol to enable efficient, synchronized data acquisition. The collected dataset includes normal operating conditions and multiple simulated fault types, such as high vibration, overcurrent, overheating, overload, and stop-rotation, to emulate real industrial scenarios. Data preprocessing techniques such as normalization and labeling were applied before model training.<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 17px;\">Experimental results showed that SCINet achieved 99.35% with the lowest test loss while SL model achieved a testing accuracy of 99.46%, as well as the proposed SL model demonstrated significant generalization and best performance. SFL model, on the other hand, offered distributed scalability and improved privacy\u00a0despite\u00a0a minor decrease in the\u00a0accuracy, achieving testing accuracy (97.89%).<\/span><\/p>\n<p style=\"text-align: justify;\"><span style=\"font-size: 17px;\">\u00a0Overall, the results confirmed that SL provides near-optimal predictive performance, while SFL offers a practical, privacy-preserving solution for distributed predictive maintenance in industrial environments.<\/span><\/p>\n<p>[\/vc_column_text][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/2&#8243;][vc_single_image image=&#8221;6787&#8243; img_size=&#8221;full&#8221; css=&#8221;&#8221;][\/vc_column][vc_column width=&#8221;1\/2&#8243;][vc_single_image image=&#8221;6788&#8243; img_size=&#8221;full&#8221; css=&#8221;&#8221;][\/vc_column][\/vc_row][vc_row][vc_column width=&#8221;1\/2&#8243;][vc_single_image image=&#8221;6789&#8243; img_size=&#8221;full&#8221; css=&#8221;&#8221;][\/vc_column][vc_column width=&#8221;1\/2&#8243;][vc_single_image image=&#8221;6788&#8243; img_size=&#8221;full&#8221; css=&#8221;&#8221;][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>[vc_row][vc_column][vc_column_text css=&#8221;&#8221;] On Wednesday, June, 3rd ,2026 the department of Mechatronics engineering at Al-Khwarizmi College of &#8230; <a class=\"cz_readmore\" href=\"https:\/\/en.kecbu.uobaghdad.edu.iq\/?p=6785\"><i class=\"fa czico-132-arrows-8\" aria-hidden=\"true\"><\/i><span>more<\/span><\/a><\/p>\n","protected":false},"author":27,"featured_media":6809,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_lmt_disableupdate":"no","_lmt_disable":"","footnotes":""},"categories":[8,1],"tags":[],"class_list":["post-6785","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-masters-discussions","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - 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