Fault diagnosis of compressed vibration signal based on 1-dimensional CNN with optimized parameters.

In view of the characteristics of multi-parameter for the 1-dimensional convolutional neural network (CNN), a parameter optimization method combining orthogonal test and pastille swarm optimization algorithm is proposed and applied to the fault diagnosis of compressed vibration signals. Compressive sensing theory breaks through the limitation of Nyquist sampling theorem and provides an effective way for the collection and transmission of a large number of vibration signals. Firstly, a CNN fault diagnosis model based on compressed signal is established by using the "end-to-end" feature of CNN. The orthogonal test is used to make a rough evaluation of the parameter range, and the best scheme is selected. For each parameter in the scheme, the multi-objective particle swarm optimization algorithm is used to refine it, and the optimal value of each parameter is obtained. The measured signal of gear box and bearing signal from Case Western Reserve University are selected as the research objects. The experimental results show that after optimization, the output characteristic classification of non-inferior parties is obvious, and the CNN diagnosis rate is significantly improved. The results also prove the feasibility of direct fault diagnosis for compressed signals. [ABSTRACT FROM AUTHOR]