This application discloses an intelligent monitoring method and
system for the status of medical devices based on
the Internet of Things (IoT), relating to the field of
medical device monitoring. First, it utilizes information such as the device's service life and operating conditions to
train a unique aging model for each device, predicting its normal performance parameters at the current stage of its life cycle. Then, by subtracting this predicted
normal aging value from the real-time
monitoring data, a clean residual
signal is obtained. Subsequent
anomaly detection will only target this residual
signal; any significant fluctuations will likely point to an impending abnormal failure. This prediction-subtraction-detection model fundamentally solves the problem of
aliasing between
normal aging and abnormal deviations in data characteristics and adapts to the individual differences of devices, thereby accurately identifying true impending failures and significantly reducing the
false alarm rate.