Author name: zhygear

The figure is the confusion matrix corresponding to the method in this paper, through which the identification of each type

In order to verify the advantages and effectiveness of intelligent fault diagnosis of gearbox bearing based on Sanc and 1d-cnn,

The parameters of neural network mainly include: learning rate, cost function, activation function, number of learning rounds, batch size, network

In order to verify the effectiveness of the proposed method, the experimental study is carried out on the zjs50 comprehensive

Combined with the signal separation ability of Sanc and the intelligent feature extraction and fault identification ability of 1d-cnn, this

Combined with the signal separation ability of Sanc and the intelligent feature extraction and fault identification ability of 1d-cnn, this

The data in normal working state is selected as the training set of the model, with a total of 12800

LSTM principle LSTM model is a special form of recurrent neural network (RNN), which can mine the relationship between the

Data preprocessing The data comes from the unit operation data recorded by SCADA system of a wind farm in Northwest

As an important part of wind turbine, gearbox is mainly composed of gear, rolling bearing and other parts. The common

In order to verify the superiority of this method in engineering application, relevant tests are carried out on the closed

When multiple faults coexist, different fault features need to be decomposed into different intrinsic mode functions. Therefore, EEMD is needed

As the most commonly used mechanical transmission components, gearbox faults are very common, including gear faults, bearing outer ring, inner

Gearbox has the advantages of compact structure and high transmission efficiency, which is the most widely used transmission component at

Due to the advantages of small volume, large transmission ratio, strong bearing capacity and high efficiency, planetary gearbox is widely