Abstract: Disclosed are an identifying method and an identifying system for a centrifuge rotor. The identifying method for a centrifuge rotor is based on a centrifuge rotor identifying system which includes a code disk with a plurality of pairs of magnetic poles, a Hall sensor and a controlling device. The method includes the following steps: according to a predetermined code rule, detecting the duration of each level signal generated from the Hall sensor; according to the total number of windows of the code disk and the duration of each level signal, determining the preliminary code; according to the predetermined code rule, determining the code starter; and based on the code starter, ordering the preliminary code to the rotor final code.

Abstract: Systems and methods for generating a beam model for radiotherapy treatment planning are discussed. An exemplary system includes a memory to store a trained deep learning model, and a processor circuit to generate a beam model. The deep learning model can be trained to establish a relationship between machine scanning data and values of beam model parameters, and validated for accuracy. The processor circuit can receive machine scanning data indicative of a configuration or an operation status of the radiation therapy device, apply the machine scanning data to the trained deep learning model to determine values for the beam model parameters, and generate a beam model based on the determined values of the plurality of beam model parameters. The beam model may be provided to a user, or a treatment planning system.

Abstract: Systems and methods for detecting and diagnosing faults in a radiotherapy system, such as a fault related to a dynamic leaf guide (DLG), are discussed. An exemplary predictive maintenance system includes a processor configured to receive machine data indicative of configuration and operation of a DLG in a target radiotherapy machine, apply a trained deep learning model to the received machine data, and detect and diagnose a DLG fault. The predictive maintenance system can train the deep learning model using data sequences constructed from the received machine data of the one more normal DLGs and the one or more faulty DLGs. Diagnosis of the DLG fault in the target radiotherapy machine includes classifying the DLG faults into different fault types or different fault severities.