Failure prediction for hemodialysis units using machine learning and hall effect sensors

Abstract

Hemodialysis Units (HDUs) are critical for providing essential dialysis treatments to patients with renal failure. However, failures in HDUs, particularly water pump failures, can disrupt patient care and compromise safety. This study focused on predicting water pump failures in HDUs using machine learning algorithms and Hall Effect sensors. By monitoring the magnetic field strength of water pump motors and leveraging machine learning, the research successfully estimated the Remaining Useful Life (RUL) of water pumps. The findings of this study demonstrated the effectiveness of the proposed approach in enhancing maintenance and reliability, thereby improving patient safety and care. The use of Hall Effect sensors provided non-invasive monitoring capabilities, ensuring easy integration into existing HDU infrastructure while minimizing disruptions. The cost-effectiveness of the sensors allowed for the widespread deployment of multiple sensors across different pumps, enhancing monitoring capabilities and reducing maintenance costs. The discussion surrounding this research highlighted the advantages of AI and machine learning in predictive maintenance for medical equipment. The ability to accurately estimate the RUL of water pumps enabled early intervention, reducing the risks associated with failures and minimizing treatment disruptions. The data-driven insights provided by machine learning algorithms facilitated informed decision-making and optimized maintenance planning, resulting in enhanced operational efficiency and improved patient outcomes. In conclusion, this study presented a proactive maintenance approach for water pump failure prediction in HDUs, leveraging machine learning algorithms and Hall Effect sensors. The findings demonstrated the successful estimation of the RUL of water pumps, enabling early intervention and minimizing risks. The implementation of AI and machine learning techniques, along with Hall Effect sensors, offered valuable insights for maintenance planning, ensuring reliable HDU system operation. By adopting such approaches, healthcare facilities can optimize their operations, enhance patient safety, and deliver high-quality care in HDUs.