# Intelligent Fault Diagnosis of Rolling Element Bearings Based on Modified AlexNet +
###### Abstract  
The reliable and safe operation of industrial systems needs to detect and diagnose bearing faults as early as possible. Intelligent fault diagnostic systems that use deep learning convolutional neural network (CNN) techniques have achieved a great deal of success in recent years. In a traditional CNN, the fully connected layer is located in the final three layers, and such a layer consists of multiple layers that are all connected. However, the fully connected layer of the CNN has the disadvantage of too many training parameters, which makes the model training and testing time longer and incurs overfitting. Additionally, because the working load is constantly changing and noise from the place of operation is unavoidable, the efficiency of intelligent fault diagnosis techniques suffers great reductions. In this research, we propose a novel technique that can effectively solve the problem of traditional CNN and accurately identify the bearing fault. Firstly, the best pre-trained CNN model is identified by considering the classification's success rate for bearing fault diagnosis. Secondly, the selected CNN model is modified to effectively reduce the parameter quantities, overfitting, and calculating time of this model. Finally, the best classifier is identified to make a hybrid model concept to achieve the best performance. It is found that the proposed technique performs well under different load conditions, even in noisy environments, with variable signal-to-noise ratio (SNR) values. Our experimental results confirm that this proposed method is highly reliable and efficient in detecting and classifying bearing faults. CNN model; discrete wavelet transform; global average pooling; intelligent fault diagnosis; vibration image 2023  
[PERSON] et al. [12] conducted a study comparing the effectiveness of three different ANN types for detecting bearing faults: multi-layer perception (MLP), the radial basis function (RBF) network, and the probabilistic neural network (PNN). The preprocessing of the data, manual feature extraction, and pattern detection are often necessary for the ANN classification approach. Features are extracted from those ANNs using the crest-factor, envelope spectrum, root mean square (RMS), crest value, standard deviation, Kurtosis, variance, and estimation [13]. Due to complex vibration signals, it is difficult for the manual feature extraction technique of ANN [14] to extract these features. Because a signal is impacted by surrounding noise as a result of changes in working situations, existing intelligent fault identification systems in real industries have limitations with initial features and multiple unidentified complex failure causes. The ANN model does not produce sufficient results for the reason that the method of extracting features depends on a high level of professional skill. To overcome the limitations of the existing intelligent approach, the deep learning (DL) method has recently been used and produced satisfactory results. DL approaches have the benefit of automatically learning characteristic features and challenging nonlinear relationships from raw data [15; 16]. However, its application in bearing fault detection is still being developed [17]. One of the deep learning models is the deep convolutional neural network (DCNN), which is an effective tool for two-dimensional image processing [18]. It is resilient, can be trained on large amounts of data, and is unaffected by image distortion [19]. A new DL model based on a DCNN for detecting bearing problems in induction motors (IMs) was proposed by [PERSON] et al. [20]. In addition, DCNN provides advantages such as quick inference, and the capacity to encode richer, and higher-order network topologies. Because of its high classification accuracy, DCNN has found widespread use in computer vision [21]. SVM and CNN are combined by [PERSON], [PERSON] et al.