Bearing fault diagnosis is essential for the safe and stable operation of rotating machinery. Existing methods use signals from a single dimension, limiting diagnostic generality and accuracy. To address these limitations and make improved use of signal features from multiple dimensions, a novel convolutional neural network model with multi-dimensional signal inputs and multi-dimensional task outputs called MIMTNet is proposed. First, frequency domain signals and a time frequency graph are obtained by using the short-time Fourier transform and a wavelet transform to process original time domain signals simultaneously. Then, the time domain signals, the frequency domain signals, and the time frequency graph are fed into the model and a special aggregation is performed after extracting features from the three corresponding branches. Finally, the outputs of the three-dimensional tasks are acquired by different full connection layers to process the aggregated features of bearing position, damage location within the bearing, and the damage size. Two common bearing vibration signal datasets are used to verify the generalization ability of our proposed method. And experimental results show that the proposed method effectively improves the bearing diagnosis capability of the deep learning model.

In this article, a new hybrid deep learning (DL) algorithm is developed to make a computer-assisted forecasting energy management (EM) system. Applying the Copula function, the Hankel matrix is created for processing gathered automatic metering infrastructure (AMI) load information in the smart network. This processing of the data results in model optimization through the suggested new pooling-based deep neural network (PDNN). Through increased size and variation of AMI data, the suggested PDNN reduces overfitting issues during testing and training. The real-time AMI southern grid data of Tamil Nadu electricity is used as the benchmark. The suggested DL model performs better than the traditional EM forecasting techniques in both mean absolute error and accuracy by 12.7% and 9.5%, respectively.

As a special insulation control, insulators protect transmission lines. In the process of detecting insulators by UAV, locating the insulators is a prerequisite for defect detection. However, In complex transmission line contexts, heavily occluded insulators are abundant. This kind of insulator is difficult to locate accurately due to its small size and extreme aspect ratio. To address this issue, an occlusion insulator detection system based on YOLOX is proposed in this work. Firstly, an improved SPP module is used to extract the semantic information of the backbone's P5 layer. Secondly, the multi-feature fusion module of attention mechanism (AFF-BiFPN) fully uses deep, shallow, and original feature information, extracting effective information about occluded insulators and small defects. To enhance the accuracy of insulator localization and the detection performance of the network, an adaptive anchor frame coarse extraction method is proposed. In addition, a data augmentation is proposed to simulate the occluded insulators. Experiments show that the method can detect defective insulators in the foreground while locating the occluded insulators in the background. On the test set, the insulator identification precision is 90.71%, and the recall is 88.25%.

With the development of modernization, micro-motors are widely used in industry. The quality of its core armature components directly affects the performance of micro-motors. Detection methods rely on manual visual inspection, with the problems of fluctuating accuracy and low detection efficiency. A high-precision, high-efficiency detection system is proposed to address these issues. Deep learning techniques are applied to handle complex patterns. The detection model consists of feature-extraction module, feature-fusion module and classifier module. The feature-fusion module improves the ability to detect artifacts, which are easily misclassified. Multi-dimensional feature fusion is performed using the BiFPN structure, and an attention mechanism is adopted for feature screening. In this module, we propose a novel channel attention mechanism to be more suitable for the feature-fusion module than other existing models. Experimental results indicate that the proposed method has the highest accuracy for micro-motor armature defect detection and significantly improves the accuracy of detection for confusable artifacts. The proposed inspection system can perfectly achieve the detection of surface defects of micro-motor armatures.