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Rotating machines have become indispensable in every field of life. Because of people's commitment to machines, the machine's faults have devastating consequences. Fault analysis of devices is common in the industry. Studies in the literature mostly work on simple models. These simple models become inefficient as data complexity increases. In this paper, three types of faults are detected in the drill bit of the CNC (Computer Numerical Control) machine. Deep LSTM (Long Short-Term Memory)-based classification model has been proposed for fault diagnosis of CNC machines. Different structures can be combined to obtain higher accuracy rates. The proposed method consists of two stages: (I) the feature extraction including time, frequency domain, and Morlet wavelet coefficients information from vibration signals from CNC machines, (II) the classification of fault types using deep LSTM model based on the extracted features. LSTM structures having two layers, three layers, four layers, and five layers have been proposed and then compared with each other with respect to the classification accuracy of fault diagnosis. The highest accuracy rate obtained in this study was 99.53%. It can be seen that deep LSTM gives outstanding results in the structures with sequential data input and the proposed system gives promising results in the field of fault analysis of machines.

The Fault Diagnosis based on Deep Long Short-Term Memory Model from the Vibration Signals in the Computer Numerical Control Machines