# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
###### Abstract  
Fault diagnosis is essential in industrial production. With the advancement of IoT technology, real-time data acquisition and storage have become feasible, enabling deep learning-based fault diagnosis methods to achieve remarkable results. However, existing approaches often overlook the temporal characteristics of fault occurrences and struggle with data imbalance between normal and faulty conditions, impacting diagnostic performance. To address these challenges, this paper proposes an integrated fault diagnosis method that incorporates data balancing, feature extraction, and temporal information analysis. The approach consists of two key components: (1) dataset construction using digital twin technology and (2) an integrated fault diagnosis model (CNN-BLSTM-attention). Digital twin technology generates virtual data under various operating conditions, mitigating the small-sample issue. The proposed model leverages a sliding window mechanism to capture both feature and temporal information, enhancing fault pattern recognition. Experimental results demonstrate that, compared to traditional methods, this approach effectively reduces noise interference and achieves a high diagnostic accuracy of 96.46%, validating its robustness in complex industrial settings. This research provides valuable theoretical and practical insights for improving fault diagnosis in industrial equipment such as screw presses. fault diagnosis; deep learning; data-driven; digital twin; screw press 2025  
28 January 2025 19 March 2025 19 April 2025 3 April  
To ensure the efficient operation of production activities, work equipment needs to remain in normal condition for extended periods. This often results in a severe imbalance between normal and fault data, with fault data samples being significantly fewer. Such an imbalance poses challenges to the performance of diagnostic algorithms. By using high-fidelity digital twin models to generate operational data of equipment under various conditions, the range of fault samples can be expanded, thereby significantly enhancing the performance of the algorithm. 2. The production data collected in a production environment are typically multidimensional, with different data features impacting the performance of the algorithm to varying degrees. To further improve the robustness of the fault diagnosis algorithm, data preprocessing is required to extract important features and reduce redundant information before feeding the multidimensional data into the network model. By extracting data features, we can better understand fault mechanisms and enhance the interpretability of the diagnostic algorithm. 3. The process leading to a fault often involves the gradual accumulation of defects. There exists an informational time delay from the generation of defects to the occurrence of faults in production. By capturing temporal information, we can better understand the evolution of faults, improve fault diagnosis algorithms, and enhance the accuracy and reliability of fault diagnosis. This adaptation allows the algorithm to better cope with changes and challenges in real production environments. In the field of feature extraction, traditional fault feature extraction has typically relied on prior knowledge, which limits the systematic integration of domain expertise. Additionally, due to differences in fault mechanisms across different equipment, fault features often lack generality, reducing the efficiency of fault diagnosis. With the rapid development of artificial intelligence, deep learning-based feature extraction methods have gradually been applied in the fault diagnosis domain. These methods have the capability to automatically learn hidden fault features embedded in multidimensional data, capturing fault characteristics more comprehensively and accurately. This enhances the efficiency of fault feature extraction and, compared to traditional methods, can adapt to various types of equipment and faults.