Abstract：It is known that the detection precision of traditional medical equipment fault detection method is low, while the rate of
leak detection and false drop rate are high. In the present study, a complex medical equipment fault detection method was put forward
based on association mining. The failure data dependence of complex medical devices could be calculated by fuzzy decision method
via obtaining the fault data space coordinates to determine the failure data cluster center. In addition, the failure data of medical
equipment was clustered based on the K-Means clustering algorithm to determine the bayesian score function, and the fault data of
complex equipment was tested via introduction of association mining method by using data OFWSC algorithm. The experimental
comparison results showed that the detection accuracy, efficiency and time were superior to the traditional methods after applying the
improved method of fault detection, which indicates that it has a certain practicality and superiority.
李平，邸玮，熊光星. 基于关联挖掘的复杂医疗设备故障的检测技术研究[J]. 中国医疗设备, 2017, 32(12): 48-51.
LI Ping, DI Wei, XIONG Guangxing. Research on Fault Detection of Complex Medical Equipment Based on Association Mining. China Medical Devices, 2017, 32(12): 48-51.
