## 1 Introduction
Noise pollution significantly alters human mood (e.g., stress, anxiety and loss of attention) and body functioning (e.g., heart and breathing rates, and blinks), which in turn affects work performance and life quality of nearby inhabitants [14; 15]. As machinery failure and noise related to rotation speed are generated by vibration, the accurate and prompt detection of vibration signals can lead to a reduction of both of them, including their side effects. When a specific feature extraction method is applied, the repeatability of selected features must be somehow guaranteed, since the classifier in use searches for singularities to identify patterns of interest. As the nature of vibration signals depends highly on rotation speed, which constantly changes in accordance with machinery operation, feature repeatability is not necessary. Therefore, the extraction and selection of relevant and valuable information from raw signals seems to be feasible in moving toward an efficient \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Device to Be Verified** & **Input** & **Signal Processing** & **Analysis Domain** & **DNN** & **Accuracy** & **Ref.** \\ \hline Bearings & & STFT & Frequency domain & LAMSTAR network & 98\% & [9] \\ \hline Air compressor & Audio signals & WPE transform & Time-frequency & Auto-encoder network (AEN) & 97.22\% & [16] \\ \hline Bearings of a rotatory & & & & & & \\ machine & Six bearing states & FFT & Frequency & AEN & 95.5\% & [17] \\ \hline Gear & & FFT & Time-frequency & AEN activated by a ReLU function & 97.27\% & [18] \\ \hline  & Vibration signals & Two layers of the net for noise reduction & DAE & 98.86\% & [19] \\ \hline Bearings & & WPE transform & Time-frequency & CNN & 98.83\% & [20] \\ \hline Buildings & Vibration signals & & & 1D-CNN & 99.79\% & [21] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Application of DNN model in fault detection. detection system, regardless of vibration level. As well as the importance of extracting all that information in a short time, it is also important to be able to apply it in real time. In light of the above evidence, a method based on DL (specifically, 1D-CNN) to identify spindle anomalies via vibration signals is proposed in this paper. In the Methods section, the 1D-CNN implementation is described according to three steps: (1) design in line with signal length, number of layers and quantity and size of filters per layer, (2) configuration based on hyper parameters and (3) training and validation to achieve the highest performance. In the Results section, the CNN performance based on three databases is reported on the basis of three evaluation conditions: (1) increasing the number of iterations, (2) varying the number of classes and (3) eliminating the most identifiable classes. Databases used in the present work were (1) the standard reference provided by Case Western Reserve University (CWRU) [22], (2) the bearing failure dataset provided by [23] and (3) the dataset generated by the National Science Foundation (NSF) for Intelligent Maintenance Systems (IMS) [24]. In the Discussion section, the 1D-CNN performance to identify spindle anomalies based on vibration signals is evaluated in comparison with other computational methodologies. In addition, scientific contribution and future work are specified. To conclude the paper, a Conclusions section is provided. Finally, the main contribution of the 1D-CNN network has been to detect faults at early damage stages. Additionally, by eliminating the easiest identifiable classes, 1D-CNN yields the highest accuracy in classifying more diffuse classes. Furthermore, the low computational cost shows the network's suitability for real-time applications in industry, at around 8 ms per signal.