A Comparative Study Between SDP-CNN and Time–Frequency-CNN-Based Approaches for Fault Detection

The image-based approach is widely used in fault detection (FD) algorithms of mechanical systems. The images are derived from the vibrational signals transformed from the time to time–frequency domain, and they are used to develop a convolutional neural network (CNN) to automate the FD process. Nowadays, images are also obtained from the transformation of vibrational signals from the time domain to symmetrized dot pattern (SDP) coordinates, achieving high CNN testing accuracy. This paper shows a comparison of image-CNN approaches for FD using images obtained from time–frequency transforms and those obtained from the SDP transform as input. The comparison was conducted using experimental data from two publicly available bearing datasets, examining both the accuracy of the CNNs and the computational time required for the vibrational signal transformations. The results show that the SDP-CNN approach achieves the same accuracy as spectrogram-CNN approaches but with a significantly reduced computational time. These results support the future real-time implementation of the SDPCNN approach for FD in mechanical systems such as bearings.