## 4 Conclusions and Future Work  
The vibration signals were measured with accelerometers to prevent accidents that can occur in large equipment, such as a gravitational accelerator. In this paper, four signals that can arise when a defect occurs in the rotating part of a gravitational accelerometer were analyzed. The existing vibration data can also be converted into image data, such as spectrograms, which are mainly used in speech recognition, and they can also be applied to an image-based deep learning model. The measured data were used to train and test a deep learning model using the spectrogram visualization based on the MFCC and STFT, and the proposed method was evaluated. The major methods used in this experiment were to convert vibration signals to images and apply a modified DNN model to a fault model. The proposed deep learning architecture enabled a diagnosis of the four conditions, such as Normal, Rubbing, Misalignment, and Unbalance. Both MFCC and STFT models showed an average accuracy of 99.5%. According to the experiment, there was no difference in performance due to processing between STFT and MFCC in the four classifications of vibration data. In addition, the proposed model was compared with GA-SVM, PCA-SVM, and MLP, which are machine learning models made with hand-crafted features. The experimental results showed that the proposed models have better performance in terms of accuracy, recall, precision, and F1-Score compared to hand-crafted feature-based models. So, performance, accuracy, and learning speed were compared with the existing deep learning method. These results suggest that the proposed method can be used successfully as a fault diagnosis and assessment model if the monitoring environment is constructed by attaching sensors in an assessment of the stability of gravity acceleration equipment in the future. In addition, it was confirmed that VGG19, which replaced FCL with GAP, works well for vibration data learning to be applied in this paper. In comparison with the deep learning model, it was confirmed that the parameter was reduced by about seven times compared to the existing VGG19 because there was no FCL. As the data to be applied in this paper, the performance of the proposed deep learning model was almost similar, which was confirmed by Early Stopping that the complexity of the data is higher than that of the model. Finally, using CAM, it was possible to measure abnormal areas of data that humans cannot see, and a failure score to quantify this was proposed. The Failure Score proposed in this paper can act as a measure to check how much difference there is compared to the Normal class. The proposed method can show the area of the defect. This is possible because the one-dimensional signal is expanded in two dimensions. Based on the characteristics of signal, which is periodic difference between each class, we applied CAM and proposed a fault score. The method proposed in this study had the following limitations. The patterns of the fault data need to be prepared in advance. It is believed to bring high accuracy because the data complexity is lower than that of the model. This is believed to be because it is a repetitive signal due to the nature of vibration data. Second, training takes considerable time and requires additional hardware, such as GPUs. Considering these limitations, a  
Figure 11: Violin plot result proposed in this paper using Fault Score CAM. method that can reduce the computation cost so that the proposed method can be used in small edge devices will be needed before this method can be commercialized. Methodology, H.Y. ([PERSON]); validation, H.Y. ([PERSON]) and J.C.; resources, K.-S.K. ; data curation, H.Y. ([PERSON]); writing--original draft, S.L. ; writing--review and editing, I.S. and J.K.; supervision, J.Y. and B.C. ; funding acquisition, G.L.