[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123678-en":3,"doc-seo-123678-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123678,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment - A Machine Learning Approach","Industrial fulfillment centers face persistent equipment health challenges because diagnostics and prognostics often depend on labor-intensive human involvement. Failure duration directly drives operational costs, making accurate identification of long- versus short-term failures essential. This research develops a machine learning framework using historical failure and fault data together with AI sensor signals. Eight classification algorithms and a stacked ensemble are compared across hyperparameter settings, and gradient boosting achieves best performance, enabling automated detection that improves maintenance planning and risk mitigation for fulfillment operations.","Hindawi  \nJournal of Engineering  \nVolume 2023, Article ID 8557487, 10 pages [https://doi.org/10.1155/2023/8557487](https://doi.org/10.1155/2023/8557487)  \nResearch Article  \nThe Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment: A Machine Learning Approach  \nAbed Mutemi  and Fernando Bacao   \nNOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide,  \nLisboa 1070-312, Portugal  \nCorrespondence should be addressed to Abed Mutemi; [d20200455@novaims.unl.pt](d20200455@novaims.unl.pt)  \nReceived 15 October 2022; Revised 2 March 2023; Accepted 24 March 2023; Published 15 April 2023  \nAcademic Editor: Chong Leong Gan  \nCopyright © 2023 Abed Mutemi and Fernando Bacao. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.  \nDue to the difculties inherent in diagnostics and prognostics, maintaining machine health remains a substantial issue in industrial production. Current approaches rely substantially on human engagement, making them costly and unsustainable, especially in high-volume industrial complexes like fulfllment centers. Te length of time that fulfllment center equipment failures last is particularly important because it afects operational costs dramatically. A machine learning approach for identifying long and short equipment failures is presented using historical equipment failure and fault data. Under a variety of hyperparameter confgurations, we test and compare the outcomes of eight diferent machine learning classifcation algorithms, seven individual classifers, and a stacked ensemble. Te gradient boosting classifer (GBC) produces state-of-the-art results in this setting, with precision of 0.76, recall of 0.82, and false positive rate (FPR) of 0.002. Tis model has since been applied successfully to automate the detection of long-and short-term defects, which has improved equipment maintenance schedules and personnel allocation towards fulfllment operations. Since its launch, this system has contributed to saving over $500 million in fulfllment expenses. It has also resulted in a better understanding of the faws that cause long-term failures, which is now being used to build more sophisticated failure prediction and risk-mitigation systems for fulfllment equipment.  \n1. Introduction  \nArtifcial intelligence advancements have resulted in smart devices that are now widely used in a variety of industries. Tese smart technologies, which range from robots to cameras to medical equipment to low-cost smart sensors, could help companies and industries achieve higher efciency and efectiveness. AI is now being implemented outside of the data center, in various devices and machines, with processors designed to capture and process data at lightning rates while using minimal power and computing resources. Because of the growth of AIpowered smart gadgets that can detect and react to sights, sounds, and other patterns, pervasive intelligence is now being integrated into a wide range of practical applications. Machines are increasingly attaining high levels of performance through learning from their experiences, adjusting to changing settings, and forecasting events. While certain industries, such as aviation,  \nhave embraced these advancements, others are still catching up. Scaling fulfllment operations, for example, is a concern as the ecommerce business grows rapidly around the world. Te overreliance on human input in decision-making is a majorstumbling block to scale. Te ability of humans to make rapid and efcient decisions is hampered by “information overload,”which includes too many tools to monitor and too many pages of best practices documentation to examine as input for maintenance decision support. Furthermore, the sheer magnitude of the equipment and structure that make up the fulfllment center ","cbCaiuaH8wiFYplI","https://ap.wps.com/l/cbCaiuaH8wiFYplI","pdf",1510396,1,10,"English","en",105,"# Introduction\n## Problem context: equipment health and failure duration\n## Fulfillment sortation systems and fault identification\n## Research goal and proposed machine learning framework","[{\"question\":\"Why is distinguishing long- and short-duration equipment failures important in fulfillment centers?\",\"answer\":\"Failure length strongly affects operational costs and maintenance planning, so faster and more accurate differentiation supports cost reduction and reliable fulfillment flows.\"},{\"question\":\"What data sources does the proposed machine learning approach use?\",\"answer\":\"It combines historical equipment failure and fault records from condition-monitoring systems with AI-powered sensor data such as vibration, current, traffic, weight, acoustic signals, temperature, and motion speeds.\"},{\"question\":\"Which machine learning model performed best in the reported experiments?\",\"answer\":\"Under various hyperparameter configurations, the gradient boosting classifier produced state-of-the-art results, achieving precision 0.76, recall 0.82, and a false positive rate of 0.002.\"}]","The Discriminants of Long and Short Duration Failures in Fulfillment Sortation Equipment - 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