Research on multi-leaf collimator fault prediction model of Varian Novalis Tx medical linear accelerator based on BP Neural Network realized by R language / 中华放射肿瘤学杂志

Objective To construct and investigate the multi-leaf collimator (MLC) fault prediction model of Varian NovalisTx medical linear accelerator based on BP neural network.Methods The MLC fault data applied in clinical trial for 18 months were collected and analyzed.The total use time of accelerator,the quantity of patients per month,average daily working hours of accelerator,volume of RapidArc plans and time interval between accelerator maintenance were used as the input factors and the prediction of MLC fault frequency was considered as the output result.The BP neural network model of MLC fault prediction was realized by AMORE package of R language and the simulation results were validated.Results The model contained 3 layers of network to realize the input-output switch.There were 5 nodes in the input layer,13 nodes in the hide layer and 1 node in the output layer,respectively.The transfer function from the input layer to the hide layer selected the tansig function and purelin function was used from the hide layer to the output layer.The maximum time of training was pre-set as 150 in the designed model.Actually,111 times of training were performed.The pre-set error was 3％ and the actual error was 2.7％,which indicated good convergence.The simulation results of MLC fault applied in clinical trial for 18 months were similar to the actual data.Conclusions The BP neural network model realized by R language of MLC fault prediction can describe the mapping relationship between fault factors and fault frequency,which provides references for the understanding of accelerator fault and management of spare parts inventory.

Research on prediction method of medical equipment sudden fault / 中国医学装备

Objective:To do research on prediction method of medical equipment sudden fault for the requirement of medical security at sea which is away from base.Methods: Sudden fault data of the medical equipment is a random variable which often manifest as the time of sudden fault happening. The possible distribution type of fault data is hypothesized according to the engineering experience. In order to ensure the sudden fault density function of medical equipment, parameter estimation and distribution fit test are carried out for the fault data.Results: The sudden fault prediction model of medical equipment is established to get the future sudden fault probability of medical equipment based on the distribution function of fault data.Conclusion: The results of case analysis validate the rationality of sudden fault prediction model.