RELIABILITY ANALYSIS AND LIFE PREDICTION OF HEMODIALYSIS MACHINE KEY COMPONENTS USING WEIBULL DISTRIBUTION: IMPLICATIONS FOR SPORTS MEDICINE AND PATIENT REHABILITATION

Abstract

Objective: This study aims to analyze the life prediction and reliability assessment of key components in hemodialysis machines using the Weibull distribution model, with potential implications for enhancing patient safety, sports rehabilitation, and clinical management of athletes with kidney disorders. Methods: A retrospective analysis was conducted on hemodialysis machines used in the Equipment Department of Nantong First People's Hospital from 2018 to 2023. Maintenance work orders and repair records were collected, summarizing a total of 1,375 equipment failure incidents. The study classified various machine faults, estimated model parameters using Matlab 2019a’s fsolve function, and calculated the Weibull reliability function and Mean Time Between Failures (MTBF). The chi-square goodness-of-fit test was applied to evaluate the consistency of fault data with the Weibull model, and fixed-point discretization was used for Weibull distribution curve processing. Results: The highest failure rates were observed in the dialysate supply system (29.67%), monitoring alarm system (64.95%), and dialysis machine core components (94.62%). The goodness of fit analysis confirmed that most subsystems, including the blood leakage monitoring system, ultrafiltration system, and air removal system, followed the Weibull distribution (P > 0.05). However, failure data for the conductivity monitoring system (R² = 0.77, P < 0.05) did not conform to the Weibull distribution, indicating distinct reliability characteristics. Key components such as the air monitoring system, venous pressure monitoring module, heparin pump, and blood pump showed inconsistencies with the Weibull model. Conclusion: Applying Weibull distribution-based life prediction for hemodialysis machine key components can provide data-driven strategies for preventive maintenance, enhancing treatment efficiency and safety in clinical settings. These findings hold significant value for optimizing dialysis machine performance in sports rehabilitation and nephrology care, ensuring improved patient outcomes, particularly for athletes and physically active individuals undergoing dialysis. Future research should explore the integration of predictive maintenance models with sports medicine strategies to enhance exercise-based rehabilitation protocols for chronic kidney disease (CKD) patients.