Oil condition evaluation for cutting reducer of roadheader based on FDD model
Reliable operation of the cutting reducer of the roadheader is closely related to the lubricating oil state.In order to reasonably evaluate the oil state,an oil state evaluation method based on fuzzy deep learning model(FDD)was proposed according to the viscosity,moisture and particle number.Firstly,the oil state was divided into four grades according to a single index,and the fuzzy evaluation was carried out according to the fuzzy comprehensive evaluation method;Secondly,the index data was normalized as the input of the deep neural network,and then the ReLU activation function was used to activate the network to obtain an overfitting neural network;Then,the degree of network fitting was reduced using the Dropout layer feature,and the hyper parameters in the model was optimized using the genetic algorithm;Finally,the model was trained with the simulation data,and the the model was validated with the actual data.The results show that the average prediction accuracy of the proposed method is 97%,and the data loss is 0.0018,which solves the difficulty in characterizing oil state due to inconsistent multi-index information and the difficulty in training neural network with less data.
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