Automatically controlled machine tools have been used extensively in the industrial field, and fault analysis methods have garnered increasing attention. This paper first describes the software and hardware design of a machine tool and then presents a fault analysis of the machine tool. The fault types of machine tools are analyzed. A signal is obtained from a vibration sensor, the characteristic value is extracted, and the fault is analyzed using a back-propagation neural network (BPNN). The experimental results show that the BPNN yields the best performance when the structure is 8-9-8, and its recognition rate is 97.22% for different types of faults. Meanwhile, the recognition rate of naive Bayes is only 76.73%, and that of a support vector machine is only 85.55%, which is significantly lower than that of the BPNN. The results show that the BPNN is effective in fault analysis and can be promoted and applied more extensively.

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