Reliability Analysis of Packing Auger of Desilting Robot Based on Deep Neural Networks
The packing auger of an underwater desilting robot often collides with hard objects in the sludge during operation.The static stress check cannot reflect the transient dynamic response in the collision process.At the same time,the traditional reliability analysis method is often inefficient and inaccurate in solving the problem of multi-source and uncertain transient dynamic response.A reliability analysis method for the collision between the winch and hard objects is proposed based on numerical simulation,deep neural networks(DNN)and the Monte Carlo(MC)method to overcome the shortcomings of the static stress check.Firstly,the uncertain variables affecting the impact force are determined based on theoretical derivation,and the final input of DNN is obtained with sensitivity analysis.Secondly,the Latin hypercube sampling(LHS)technique is used to sample according to the input distribution of each DNN,and the finite element model of the collision between the winch and hard objects corresponding to the sampling data is established by using the finite element software ANSYS/LS-DYNA to extract the output of DNN.Finally,the damage criteria of the packing auger are determined through experiments,and its reliability and failure probability are predicted by combining deep neural networks with the Monte Carlo method(DNN-MC).The results show that the accuracy of this method is much higher than the required engineering accuracy.Compared with the traditional reliability analysis method,this method has higher efficiency and better robustness to ensure high accuracy.
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