CNN Based Fault Diagnosis of Rolling Bearings Using MCU
The deep neural network is applied to intelligent fault diagnosis(IFD)of rolling bearings,and artificial intelligence is realized in low cost miniaturized platform in this paper.The author optimized and improved a neural network architecture of two-dimensional convolutional neural network(CNN2D),and deployed it to STM32H743VI MCU to realize the identification and classification of bearing fault vibration signals.The training and validation of the network uses the Case Western Reserve University(CWRU)bearing fault data set and obtains data containing 10 fault types.The neural network of CNN2D is trained by Keras tool based on Tensorflow deep learning framework.Verification shows that the accuracy of fault identification can reach 98.90%.Then CubeAI tool is used to deploy the network to the microcontroller.The random bearing data is obtained through communication between serial port and computer,and the measured running time of each diagnosis is about 19 ms.
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