{"id":35176,"date":"2026-04-13T14:10:57","date_gmt":"2026-04-13T14:10:57","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/retrofitting-water-pumps-with-health-monitoring-and-machine-learning-at-the-edge\/"},"modified":"2026-06-08T13:18:11","modified_gmt":"2026-06-08T13:18:11","slug":"retrofitting-water-pumps-with-health-monitoring-and-machine-learning-at-the-edge","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/retrofitting-water-pumps-with-health-monitoring-and-machine-learning-at-the-edge\/","title":{"rendered":"Retrofitting Water Pumps with Health Monitoring and Machine Learning at the Edge"},"content":{"rendered":"<p>Water pumps are critical components of modern infrastructure whose maintenance poses significant logistical and financial challenges. Addressing this issue, we propose a solution by retrofitting running pumps with health monitoring, deploying state-of-the-art machine learning (ML) techniques on edge sensors. Our work explores how basic power usage sensors can assess informative features about pump operation cycles. A combination of ML approaches, which can either be run at the edge or as a remote cloud-based analysis service, allows us to then assess the health status of the water pump over time. Our conceptual approach was practically evaluated in a real-world field test on different pump installations, from which we derive \\texttt{SYPHM}, a synthetic data generator for pump health monitoring. Tested across various experiments, our approach demonstrated high accuracy in detecting anomalous patterns while balancing privacy, scalability, and cost-efficiency. As such, our research presents novel insights for deploying ML for critical infrastructure, enabling affordable predictive maintenance for sustainable water technology.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Water pumps are critical components of modern infrastructure whose maintenance poses significant logistical and financial challenges. Addressing this issue, we propose a solution by retrofitting running pumps with health monitoring, deploying state-of-the-art machine learning (ML) techniques on edge sensors. Our work explores how basic power usage sensors can assess informative features about pump operation cycles. A combination of ML approaches, which can either be run at the edge or as [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[30],"class_list":["post-35176","publication","type-publication","status-publish","hentry","publication-type-article"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/35176","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/35176\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=35176"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=35176"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}