Combined Flywheel Fault Diagnosis Method Based on Improved LSTM and Fault Tree
Under consideration of the difficulty in obtaining an accurate model of the flywheel and the limitation of computing power,a combined fault diagnosis method based on improved LSTM and fault tree is proposed.Firstly,the traditional grey wolf optimizer algorithm(GWO)is improved by population initial-ization,distance control parameters and a wolf position updates to achieve better convergence performance.Then,during the network training process,the improved GWO is introduced to optimize the hyper-parameter space,so the low efficiency of hyper-parameter selection caused by traditional manual adjustment method or grid search method is overcome;Further,due to considering the engineering practicality of fault tree analy-sis and the autonomy of neural network,a fault diagnosis framework that combines with those two ways is designed;Finally,a flywheel fault tree model is established and simulation experiments are conducted,which demonstrate the excellent convergence of the improved GWO and the effectiveness of the combined diag-nosis algorithm for flywheel fault detection and recognition.
Fault diagnosisReaction wheelGrey wolf optimizerLSTMFault tree
万方数据
维普
