Residual Life Prediction Method of Belt Conveyors Based on MDT Learning
In the coal mining processes,the operating environments of the belt conveyors were harsh and the working conditions were complex.These resulted in a limited amount of sensor monito-ring data and a large amount of noise interference,which seriously limited the accuracy of the residual life prediction.Aiming at this problem,a MDT learning residual life prediction method was proposed.To predict the residual life of key component roller bearings accurately,multiple working condition data of belt conveyors accumulated in coal flow transportation could be fully used.Firstly,integrating a multi-scale convolutional neural network and bidirectional gated recurrent unit(MCNN-BiGRU),a degradation feature extraction model was constructed.The particle swarm optimization(PSO)was used to determine the model hyperparameters.Then,using MDT learning and multiple working con-dition data,the residual life prediction was carried out.Combining loss of maximum mean discrepancy(MMD)with correlation alignment(CORAL)the data distribution difference of each source domain was narrowed.This might solve the problem of low training accuracy of the model due to the small a-mount of data.Finally,the actual production data sets of a coal mine were used for verification.The results show that the prediction effectiveness of the MDT-MCNN-BiGRU model is better,and the model performance is further improved after the Savitzky-Golay filter denoising.Using the IMS data-set and comparing with the existing methods,the proposed method has high prediction accuracy and is of some significance in guiding the health management of coal mine transportation equipment.
belt conveyorresidual life predictionmultiple working conditionfeature extrac-tionmulti-source domain transfer(MDT)learning
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