## 6 Deep Learning Fault Classification
### Classification of Large Feature Arrays Using DL Models  
Classifying 3900 feature arrays of size \(96\times 3921\) represented in double precision using deep learning models requires considerable computational resources. A possible solution consists of resizing the features array to reduce their resolution. The drawback of this approach is the loss of information that impacts the classification accuracy. Other solutions are the utilization of streaming CNN [52], application of full-resolution end-to-end-trainable CNN based on gradient check-pointing strategy [53] or using a multi-scale hard-attention architecture such as the Traversal Network (TNet) [54]. These approaches imply using a complex deep learning architecture that could not be directly adapted to recursive neural networks like the LSTM.  
Figure 7: Proposed approach for fault classification using vibration signals. The deep learning model can be a CNN, an LSTM or BiLSTM.  
We are proposing a novel patch-wise classification method for large feature arrays of MFCC data extracted from rotating machinery. The approach works under the assumption that the vibration signal represents a pseudo-periodical process. The patch-wise method takes some essential elements from the Random Forest algorithm. The combination of deep learning models with random forests has been successfully applied to small datasets [55]. However, instead of using a set of low-precision classifiers as a key element in our case, we are using a highly accurate classifier such as CNN or LSTM. The subdivision of each MFCC feature array into \(N_{B}\) patches could be interpreted as a data augmentation technique that allows obtaining numerous images of size \(96\times M\) to train the deep learning models. The number of images or patches for each dataset is presented in Table 5. Each dataset's total number of patches was calculated using the \(M\) value selected as explained in Section 7.  
We are augmenting the dataset with randomized patches of feature arrays where variations could be interpreted as affine transformations or noise contamination, similar to the processes used for image data augmentation. The patch-wise approach has the limitation of using local features. The performance is sub-optimal concerning the same classifier trained with the full-resolution feature array. We have incorporated the voting system to cope with this limitation. The approach is simple and can be used with any deep learning model, such as CNN, LSTM, or BiLSTM. In addition, as in nature, we can find several types of large images that are textured or have a pseudo-periodical appearance. The method could be applicable for classifying forest species using macroscopic images [56] or analysis of microscopy images [57].  
In this work, each of the DL models is trained and tested with the proposed methodology implemented on a MatLab software running on a laptop computer with a processor Intel i7-670 HQ @ 2.59 GHz with 12 GB of RAM, including an NVIDIA GeForce GTX 950M with GPU. The criteria for selecting a DL architecture, i.e., CNN or LSTM, in this approach aims to efficiently handle the size image dataset obtained after performing the size dataset augmentation by using our patch-wise splitting MFCC array proposal.