Fault Diagnosis of Rotating Machinery Bearings Based on Improved DCNN and WOA-DELM

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance

A Design for a Lithium-Ion Battery Pack Monitoring System Based on NB-IoT-ZigBee

With environmental issues arising from the excessive use of fossil fuels, clean energy has gained widespread attention, particularly the application of lithium-ion batteries. Lithium-ion batteries are integrated into various industrial products, which necessitates higher safety requirements. Narrowband Internet of Things (NB-IoT) is an LPWA (Low Power Wide Area Network) technology that provides IoT devices with low-power, low-cost, long-endurance, and wide-coverage wireless connectivity. This study addresses the shortcomings of existing lithium-ion battery pack detection systems and proposes a lithium-ion battery monitoring system based on NB-IoT-ZigBee technology. The system operates in a master-slave mode, with the subordinate module collecting and fusing multi-source sensor data, while the master control module uploads the data to local monitoring centers and cloud platforms via TCP and NB-IoT. Experimental validation demonstrates that the design functions effectively, accomplishing the monitoring and protection of lithium-ion battery packs in energy storage power stations

Class-Balanced Modulation for Facial Expression Recognition

Facial expression recognition (FER) aims at determining the types of facial expressions for given facial images, which has a broad application prospect in psychological diagnosis, human-computer interaction, etc. In practical tasks, various databases tend to have imbalanced data distributions among basic facial expressions. Such an issue has caused imbalanced feature distribution and inconsistent classifier optimization for various facial expressions, seriously affecting the performance of expression recognition models. To solve this issue, this paper proposes a class-balanced modulation mechanism for facial expression recognition (CBM-Net), which attempts to address the imbalanced data distribution problem by modulating the FER model in feature learning and classifier optimization stages. CBM-Net includes two modules of feature modulation and gradient modulation. The feature modulation module struggles to balance feature distributions for all facial expressions by increasing the separability between classes and the tightness within classes in the feature direction. The gradient modulation module uses the statistical information of batch training samples to reversely adjust the optimization gradient of each classifier to ensure that the convergence speed of each classifier is consistent, so that the performance of each classifier can be optimal at the same time. Qualitative and quantitative experiments on four popular datasets show that CBM-Net is effective in class-balanced modulation, and its effect is quite good compared with many advanced methods

Emerging Roles of Ubiquitin-Specific Protease 25 in Diseases

The balance of ubiquitination and deubiquitination plays diverse roles in regulating protein stability and cellular homeostasis. Deubiquitinating enzymes catalyze the hydrolysis and removal of ubiquitin chains from target proteins and play critical roles in various disease processes, including cancer, immune responses to viral infections and neurodegeneration. This article aims to summarize roles of the deubiquitinating enzyme ubiquitin-specific protease 25 (USP25) in disease onset and progression. Previous studies have focused on the role of USP25 in antiviral immunity and neurodegenerative diseases. Recently, however, as the structural similarities and differences between USP25 and its homolog USP28 have become clear, mechanisms of action of USP25 in cancer and other diseases have been gradually revealed