Adaptive Anti-Noise Fault Diagnosis Algorithm for Bearings Based on an Improved Convolution Neural Network
According to the research of bearing diagnosis methods in recent years,most of existing methods are developed by com-bining feature extraction and fault recognition.However,these fault diagnosis algorithms require expert experience and time-con-suming to design.Moreover,the performance of the algorithm will be affected by working conditions,signal noise,and other fac-tors.In order to solve the above problems,it proposes a Signal-to-Image conversion method and a fault diagnosis method,which based on the convolutional neural network(CNN)structure.The model does not need to analyze the intrinsic fault mechanism of mechanical equipment,which organically combines the feature extraction process and the fault classification process to realize fault diagnosis.Experiments on the bearing data set show that the proposed method can not only achieve more than 99% fault rec-ognition accuracy,but also obtain good adaptability under noisy signal.
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