# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 5 Conclusions  
In the modern era, the high complexity industrial system can ensure reliability and safety thanks to the sensor devices that have become necessary modules in comprehensive systems. Acoustic emission signals have emerged as an intelligent and optimized solution that simplifies the fault diagnostic procedure with a sequence of sensors. In this study, da data-driven methodology using an acoustic \begin{table}
\begin{tabular}{c l c c c} \hline \hline
**Scenarios** & **Type** & **Vibration Image Method** & **Wavelet Spectrogram** & **DSWI** \\ \hline \multirow{4}{*}{**(I) KNN + PCA**} & Normal & 35.50\% & 0\% & 93.50\% \\  & Outer & 41.90\% & 100\% & 88.70\% \\  & Inner & 30.60\% & 45.20\% & 61.30\% \\  & Roller & 98.40\% & 50.00\% & 0\% \\  & **Average accuracy** & **51.42\%** & **48.99\%** & **61.13\%** \\ \hline \multirow{4}{*}{**(2) MCSVM + PCA**} & Normal & 100\% & 75.20\% & 85.20\% \\  & Outer & 96.80\% & 84.23\% & 90.02\% \\  & Inner & 91.90\% & 86.80\% & 86.70\% \\  & Roller & 35.50\% & 81.40\% & 89.12\% \\  & **Average accuracy** & **80.97\%** & **81.91\%** & **87.76\%** \\ \hline \multirow{4}{*}{**(3) LeNet-5**} & Normal & 32.25\% & 96.77\% & 93.54\% \\  & Outer & 65.00\% & 100\% & 96.77\% \\  & Inner & 35.00\% & 59.67\% & 93.54\% \\  & Roller & 60.00\% & 100\% & 100\% \\  & **Average accuracy** & **46.77\%** & **89.11\%** & **95.97\%** \\ \hline \multirow{4}{*}{**(4) AlexNet**} & Normal & 64.52\% & 64.51\% & 98.38\% \\  & Outer & 100\% & 100\% & 100\% \\  & Inner & 13.33\% & 98.38\% & 93.54\% \\  & Roller & 96.77\% & 100\% & 100\% \\  & **Average accuracy** & **68.54\%** & **90.72\%** & **97.98\%** \\ \hline \multirow{4}{*}{**Proposed DCNN**} & Normal & 100\% & 75.80\% & 96.80\% \\  & Outer & 88.70\% & 100\% & 100\% \\ \cline{1-1}  & Inner & 59.70\% & 96.80\% & 98.40\% \\ \cline{1-1}  & Roller & 83.90\% & 100\% & 100\% \\ \cline{1-1}  & **Average accuracy** & **83.06\%** & **93.15\%** & **98.79\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The classification report of the test dataset for different types of input and different classification methodology. emission signal analyzed by envelope analysis and an enhanced continuous wavelet transform with the damage frequency band information was used to generate the new 2-D representation image (so-called DSWI) from the 1-D signal. This DSWI shows the discriminate pattern and correlates with the defect frequencies for each type of fault in bearings helping to improve the performance of the machine learning methods for bearing fault diagnosis. The purpose of this study is also to propose a DCNN architecture that is suitable for separating the DSWI from different types of faults in bearing. To validate the diagnostic result of the proposed approach, the data collected from an elaborately self-designed testbed are deployed. Then, the experimental findings imply that the DCNN classifiers achieved greater than 98% accuracy and other evaluation parameters also outperformed the current state-of-the-art. By incorporating the deep learning-based structure with the new time-frequency domain-based 2-D representation, the proposed method is efficacious, with great accuracy and no need for the feature selection stage. In addition, a collated comparison with some well-known methods in literature is executed and indicates that the DSWI with the DCNN algorithm can become a promising method for bearing fault diagnosis. All of the authors contributed equally to the conception of the idea, the design of experiments, the analysis and interpretation of results, and the writing and improvement of the manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by the Technology Infrastructure Program funded by the Ministry of SMEs and Startups (MSS, Korea).