# Bearing Fault Diagnosis Using a Vector-Based Convolutional Fuzzy Neural Network
###### Abstract  
The spindle of a machine tool plays a key role in machining because the wear of a spindle might result in inaccurate production and decreased productivity. To understand the condition of a machine tool, a vector-based convolutional fuzzy neural network (vector-CFNN) was developed in this study to diagnose faults from signals. The developed vector-CFNN mainly comprises a feature extraction part and a classification part. The feature extraction phase encompasses the use of convolutional layers and pooling layers, while the classification phase is facilitated through the deployment of a fuzzy neural network. The fusion layer plays an important role by being placed between the feature extraction and classification parts. It combines the characteristics and then passes the feature information to the classification part to improve the model's performance. The developed vector-CFNN was experimentally evaluated against existing fusion methods; vector-CFNN required fewer parameters and achieved the highest average accuracy (99.84%) in fault diagnosis relative to conventional neural networks, fuzzy neural networks, and convolutional neural networks. Moreover, vector-CFNN achieved superior fault diagnosis using spindle vibration signals and required fewer parameters relative to its counterparts, indicating its feasibility for online spindle vibration monitoring. spindle vibration; vector convolutional neural network; feature fusion; fault diagnosis 20231223337 2023123337 20231223337 20231223337 20231223337 20231223337 20231223337 20231223337 20231223337 20231223337 20231223337 2023123337 20  
(SVM) kernel functions: C-support vector and v-support vector classifiers. The results revealed that C-support vector classification yields an accuracy of 94.5%, which is higher than that of v-support vector classification [7]. In addition, fuzzy neural network (FNN) offers the advantages of both neural networks and fuzzy logic, making it a powerful hybrid tool. FNN integrates expert knowledge into the system and adopts human-like fuzzy reasoning, which makes the system outputs easier to interpret. A singular value decomposition and FNN were adopted to extract and diagnose the fault features in diesel engine crankshaft bearing [8]. While the aforementioned techniques exhibit greater versatility in comparison to models based on physical principles, most of them rely on manual feature extraction. Moreover, conventional machine learning methods cannot accurately interpret large amounts of data. In this respect, deep learning approaches, which can extract features automatically, have recently been employed in prediction and classification tasks. Fault diagnosis methods based on deep learning have become a key research topic. In particular, convolutional neural networks (CNNs), which are capable of local feature connection and weight sharing, have been employed for fault diagnosis. For instance, a residual learning algorithm was built in a CNN model for alleviating the information missing during back-propagation. The proposed architecture is able to handle vibration signal with variable length, and minimal prior knowledge of fault diagnosis is needed. The results revealed that the proposed CNN model offers a novel fault diagnosis technique for rotating machinery [9]. Considering the feature extraction ability of deep learning lacks time delay information caused by faults occurrence, a combination of CNN and long short-term memory network (LSTM) model was developed in [10]. Finally, the results were superior to those of CNN, LSTM, ANN, K-nearest neighbor, and support vector machine models. Furthermore, a CNN framework was applied with the proposed slope and threshold adaptive tanh activation function to diagnose bearing faults. The relationship of non-linear features and input signal was established corresponding to the shape of activation function. The experimental results demonstrated that average accuracy in two bearing datasets reached 90.00% and 90.77%, respectively [11].