(1) Background: Rolling bearings are important components in mechanical equipment, but they are also components with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. (2) Methods: Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer (DBO) algorithm-optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory (VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search. Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal. Then, CNN-BiLSTM was used as the model for fault classification, and hyperparameters of the model were optimized using the CSADBO algorithm. (3) Results: Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. (4) Conclusions: Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on an improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide a theoretical reference for other related fault diagnosis problems.

Rolling bearings are important components in mechanical equipment, but they are also a component with a high failure rate. Once a malfunction occurs, it will cause mechanical equipment to malfunction and may even affect personnel safety. Therefore, studying the fault diagnosis methods for rolling bearings is of great significance and is also a current research hotspot and frontier. However, the vibration signals of rolling bearings usually exhibit nonlinear and non-stationary characteristics, and are easily affected by industrial environmental noise, making it difficult to accurately diagnose bearing faults. Therefore, this article proposes a rolling bearing fault diagnosis model based on an improved dung beetle optimizer algorithm(DBO) optimized variational mode decomposition-convolutional neural network-bidirectional long short-term memory(VMD-CNN-BiLSTM). Firstly, an improved DBO algorithm named CSADBO is proposed by integrating multiple strategies such as chaotic mapping and cooperative search; Secondly, the optimal parameter combination of VMD was adaptively determined through the CSADBO algorithm, and the optimized VMD algorithm was used to perform modal decomposition on the bearing vibration signal; Then, CNN-BiLSTM was used as the model for fault classification, and the hyperparameters of the model were optimized using the CSADBO algorithm; Finally, multiple experiments were conducted on the bearing dataset of Case Western Reserve University, and the proposed method achieved an average diagnostic accuracy of 99.6%. Experimental comparisons were made with other models to verify the effectiveness of the proposed model. The experimental results show that the proposed model based on improved DBO algorithm optimized VMD-CNN-BiLSTM can effectively be used for rolling bearing fault diagnosis, with high diagnostic accuracy, and can provide theoretical reference for other related fault diagnosis problems.

Data sharing provides necessary data support for collaborative production among Smart Factories (SFs) and improves the production efficiency of the manufacturing industry. However, the quality of shared data cannot meet the needs of collaborative production of high-precision manufacturing processes among SFs, which hinders in-depth cooperation among SFs to a certain extent. In order to solve the above problems, we propose a lightweight sensing data integrity detection method for the Industrial Internet of Things that performs integrity detection on the shared sensing data before the SF shares the data. Firstly, we design and implement a sensing data integrity feature extraction method to extract the integrity features of sensing data. Then, we design and implement a lightweight sensing data integrity detection method based on fuzzy support vector machines that detects the integrity of sensing data based on the integrity characteristics of sensing data. Finally, we conduct simulation experiments on the proposed method. Through theoretical and simulation experiments, it has been proven that compared with the traditional method, the accuracy of the proposed method is improved by 11.40%, the False Alarm Rate (FAR) is reduced by 79.49%, the Missing Alarm Rate (MAR) is reduced by 71.80%, the detection time is reduced by 72.40%, and the energy consumption is reduced by 12.04%.