In order to effectively utilize the large amount of high-dimensionality historical data generated by energy meters during operation, this paper proposes a DBN-MLP fusion neural network method for multi-dimensional analysis and fault-type diagnosis of smart energy meter fault data. In this paper, we first use DBN to strengthen the feature extraction ability of the network and solve the problem of many kinds of feature data and high dimensionality of historical data. After that, the processed feature information is input into the MLP neural network, and the strong processing ability of MLP for nonlinear numbers is used to solve the problem of weak correlation among data in the historical data set and improve the accuracy rate of faults diagnosis. The final results show that the DBN-MLP method used in this paper can effectively reduce the number of training iterations to reduce the training time and improve the accuracy of diagnosis.

Hydraulic systems play a pivotal and extensive role in mechanics and energy. However, the performance of intelligent fault diagnosis models for multiple components is often hindered by the complexity, variability, strong hermeticity, intricate structures, and fault concealment in real-world conditions. This study proposes a new approach for hydraulic fault diagnosis that leverages 2D temporal modeling and attention mechanisms for decoupling compound faults and extracting features from multisample rate sensor data. Initially, to address the issue of oversampling in some high-frequency sensors within the dataset, variable frequency data sampling is employed during the data preprocessing stage to resample redundant data. Subsequently, two-dimensional convolution simultaneously captures both the instantaneous and long-term features of the sensor signals for the coupling signals of hydraulic system sensors. Lastly, to address the challenge of feature fusion with multisample rate sensor data, where direct merging of features through maximum or average pooling might dilute crucial information, a feature fusion and decoupling method based on a probabilistic sparse self-attention mechanism is designed, avoiding the issue of long-tail distribution in multisample rate sensor data. Experimental validation showed that the proposed model can effectively utilize samples to achieve accurate fault decoupling and classification for different components, achieving a diagnostic accuracy exceeding 97% and demonstrating robust performance in hydraulic system fault diagnosis under noise conditions.

Rapid and accurate fault diagnosis of smart meters can greatly improve the operational and maintenance ability of power systems. Focusing on the historical fault data information of smart meters, a fault diagnosis model of smart meters based on an improved capsule network (CapsNet) is proposed. First, we count the sample size of each fault type, and a mixed sampling method combining undersampling and oversampling is used to solve the problem of distribution imbalance of sample size. The one-hot encoding method is adopted to solve the problem of the fault samples containing more discrete and disordered data. Then, the strong adaptive feature extraction capability and nonlinear mapping capability of the deep belief network (DBN) are utilized to improve the single convolution layer feature extraction part of a traditional capsule network; DBN can also address the problem of high data dimensions and sparse data due to one-hot encoding. The important features and key information of the input sample are extracted and used as the input of the primary capsule layer, and the dynamic routing algorithm is used to construct the digital capsule. Finally, the results of experiments show that the improved capsule network model can effectively improve the accuracy of diagnosis and shorten the training time.

The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis process. The experimental comparison results show that the proposed method can diagnose power battery failure modes with an accuracy of 96.86%, and various evaluation indexes are superior to CNN, CapsNet, CBAM-CapsNet, and other neural networks at accurately identifying fault types with higher diagnostic accuracy and robustness.

High-density polyethylene (HDPE) pipes are widely used for urban natural gas transportation. Pipes are usually welded using the technique of thermal butt fusion, which is prone to manufacturing defects that are detrimental to safe operation. This paper proposes a spatiotemporal singular value decomposition preprocessing improved total focusing method (STSVD-ITFM) imaging algorithm combined with ultrasonic phased array technology for non-destructive testing. That is, the ultrasonic real-value signal data are first processed using STSVD filtering, enhancing the spatiotemporal singular values corresponding to the defective signal components. The TFM algorithm is then improved by establishing a composite modification factor based on the directivity function and the corrected energy attenuation factor by adding angle variable. Finally, the filtered signal data are utilized for imaging. Experiments are conducted by examining specimen blocks of HDPE materials with through-hole defects. The results show the following: the STSVD-ITFM algorithm proposed in this paper can better suppress static clutter in the near-field region, and the average signal-to-noise ratios are all higher than the TFM algorithm. Moreover, the STSVD-ITFM algorithm has the smallest average error among all defect depth quantification results.

The built-in relay in a meter is a key control component of a smart meter, and its reliability determines whether the user can use electricity safely and smoothly. In this paper, the degradation characteristics of the arc-burning energy are enhanced by the method of K-means clustering to replace degradation data, such as the overtravel time, release time, and other data. In existing methods, the meter needs to be disassembled to describe the degradation trend of the meter relay. The proposed method is combined with a bidirectional long short-term memory (Bi-LSTM) neural network to predict the degradation trend of the relay’s performance. In this paper, K-means clustering is used to enhance the extraction of arc energy data features, and then the arc energy data obtained from the reliability lifetime test is assessed to predict the degradation trend of the meter relay by means of a bidirectional LSTM.