Abstract: In order to solve the problems of poor predictability and low accuracy in the current fault diagnosis methods of mine ventilator, a predictive fault diagnosis method of mine ventilator based on digital twin and probabilistic neural network(PNN)is proposed.Unity3D, 3dsMax and SciFEA are used to build the digital twin model of ventilator to simulate the structural characteristics, physical properties and operation rules of the real ventilator, and the method uses PREspective to communicate with the PLC of the ventilator in real time to map the operation status of the ventilator to the digital twin model in real time.Based on the digital twin model of the ventilator, combined with expert knowledge, machine learning and historical data, the study constructs a predictive fault diagnosis model of the ventilator.The model continuously learns and updates the model parameters by analyzing the relationship between the real-time data and the operation status of the ventilator.The improved whale optimization algorithm(IWOA)is used to obtain the optimal value of the smoothing factor through the biological behaviors of surrounding prey, preying and searching prey, and assigns the optimal value to the PNN.The optimized PNN is applied to perform predictive fault diagnosis of the ventilator, and the result of the predictive fault model of the ventilator is compared with the actual situation to judge whether the results match the actual situation.If the diagnosis is wrong, the predictive fault diagnosis model needs to be corrected until the fault judgment is accurate.The experimental results show that compared with the PNN fault diagnosis accuracy optimized by the genetic algorithm(GA), particle swarm optimization algorithm(PSO)and whale optimization algorithm(WOA), the fault diagnosis accuracy of PNN optimized by IWOA reaches 97.5%, indicating that the predictive fault diagnosis method of mine ventilator based on digital twin and PNN can meet the requirements of real-time and accuracy of ventilator fault diagnosis.