| In new energy vehicles,the motor system is a key component of the vehicle power system,and the failure of the drive motor may cause serious consequences.In view of the current problem that motor overhaul in new energy vehicles mainly relies on human overhaul,and it is difficult and unbalanced to obtain normal and fault data of motors,a fault prediction method combining digital twin technology and the theory of critical phase change of complex systems is proposed;in view of the problem that industrial time series data contains noise and is difficult to use,a fault diagnosis method based on GRU-convolutional noise reduction selfencoder model is proposed for motor systems.The problem of fault prediction and diagnosis of new energy vehicle motor systems with a small number of samples is solved,filling a gap in the research on intelligent operation and maintenance of new energy vehicle motor systems.Firstly,a simplified digital twin architecture is designed based on the specific needs of this research.The physical mechanism of the motor system of the new energy vehicle is analysed during operation,a mechanistic twin model of the normal motor system is established,and then the failure occurrence mechanism is specifically investigated,a mechanistic twin model of the faulty motor system is established,and the digital twin data is obtained for backup in subsequent studies.Secondly,the fault prediction study of the motor system of new energy vehicles is carried out by combining the theory of the critical phase change of complex systems.The early warning features are extracted from the random fluctuation signals of the motor operation by means of a digital twin model,and the consistency between the system fault and the critical phase transition of the complex system is checked by various tests.Finally,a fault diagnosis method based on GRU-convolutional noise reduction self-encoder is proposed to solve the problem that all kinds of fault data of the motor system of new energy vehicles belong to industrial time series data,and the usual fault diagnosis methods cannot effectively learn its time series characteristics,and the collected data will be mixed with certain noise,etc.A fault diagnosis method based on GRU Convolution Denoising Auto-Encoder is proposed to realize the fault diagnosis of various kinds of motor faults,which is convenient for overhaul and maintenance.Validation is carried out with digital twin data and real motor data collected in multiple groups of projects.The results show that the proposed fault prediction and diagnosis method can be achieved fault prediction and fault diagnosis by time series data with fewer samples on both types of data.It possesses a high diagnostic accuracy rate.It opens up new ideas for the study of a general method for fault prediction and diagnosis of new energy vehicle motors. |
