# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
###### Abstract
A one-dimensional (1D) CNN long short-term memory architecture was adopted for predicting the operational life of machining tools. In that architecture, prior to prediction, an ensemble discrete wavelets transform is applied to eliminate the noise of the vibration signals, and statistical feature extraction is performed based on time and frequency domains [12]. A signal-to-image spatial transformation technique was employed to generate grayscale images, thus decreasing the computation time. In addition, they adopted an attention mechanism for improving the accuracy and efficiency of the CNN, and their method achieved a classification accuracy of over 99% [13]. A squeeze-and-excitation-enabled CNN model was proposed for diagnosing faults in bearings. The model assigns learnable weights to each feature extraction channel, allowing the model to focus on the major features. The framework achieved a classification accuracy of over 99% in experiments [14]. A CNN model was integrated with an adaptive batch normalization algorithm to resolve the drawbacks of the high computation time and low versatility of deep CNN [15]. A distribution adaptive deep CNN model was applied for fault diagnosis based on a 1D CNN and achieved an accuracy of 90.29% [16]. A multiscale CNN for feature extraction from signal data was developed for diagnosing faults in bearings [17]. To solve the data imbalance problem, data augmentation techniques, such as permutation and time-warping, have been used to fuse the location features of the input data. The multiscale CNN not only learns better feature expression than conventional CNN by means of multiscale convolution operation but also reduces the number of parameters required and training time taken. Numerous studies have reported that deep learning approaches have outstanding feature extraction ability and high classification accuracy. However, approaches that require many learnable parameters require more powerful hardware to run, which impedes their applicability. Literature reviews have revealed that spindle vibration signals can be used to diagnose bearing failures; however, those approaches have limitations, such as manual feature extraction and the existence of numerous parameters during model training. Therefore,a proper and effective deep learning approach that is able to classify the bearing failures from different signal conditions is important to ensure the safety of the automation process. In this study, a vector-based convolutional FNN (vector-CFNN) was developed for fault diagnosis of spindle vibration signals. Moreover, a fusion layer that connects the feature extraction part and the classification part was introduced for fusing the characteristics of feature maps to enhance the model's classification performance. The contributions of this study are as follows:  
* The vector-CFNN model with fewer parameters was proposed to classify bearing failure by using spindle vibration signals. * The fusion layer was introduced to improve the model's classification accuracy by fusing the spatial and depth information of feature maps. * Compared with other methods, vector-CFNN increases accuracy by up to 25% and requires only approximately 1000 learnable parameters. The remainder of this paper is organized as follows. In Section 2, the structure of CNNs is described. In Section 3, the developed vector-CFNN is detailed. In Section 4, the results of experimental evaluations of the proposed vector-CFNN against existing approaches, such as ANN, FNN, conventional CNN, and CFNN, are presented. Finally, in Section 5, the paper is concluded.