# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
###### Abstract
In the data-driven field, most of the analyzed data are acquired through real-time transmission from sensors associated with physical equipment. However, in practical production environments, certain key signals may not be directly accessible through traditional sensors, leading to a significant waste of potentially valuable data. This lack of signal acquisition or incomplete data collection introduces issues of data incompleteness in fault diagnosis systems, which further impacts the accuracy and reliability of the models. Moreover, with the continuous growth of data in industrial fields, efficiently processing large-scale data has become another major challenge. Big data processing not only consumes vast computational resources but also faces issues such as processing delays. Additionally, the problem of data imbalance in fault data acquisition is particularly prominent, as fault data are often much less abundant than normal data. This can cause models to be biased toward normal-state data during training, thereby affecting the Precision and generalizability of the diagnostic models [1]. With the continuous advancements in computer hardware and sensor technologies, digital twin technology has emerged as a powerful tool for integrating multi-source data, enabling precise modeling and simulation of physical equipment in virtual environments. Digital twins can not only replicate the normal operational states of equipment but also simulate their performance under extreme conditions, generating more diverse and comprehensive data. These data provide facility managers with deeper insights, supporting decision-making in areas such as equipment maintenance, fault prediction, and optimization scheduling. Compared to traditional methods that rely solely on real production data, digital twin technology offers more virtual data resources, bridging gaps in the collection of real-world data, and significantly enhancing the accuracy and reliability of algorithmic models. Moreover, digital twins can improve the system's adaptability by incorporating real-time data updates and dynamic simulations, thus facilitating more intelligent production and operational management [2]. In the field of fault diagnosis, numerous studies have proposed fault diagnosis methods for industries such as manufacturing, biology, and chemistry. However, these methods often fail to consider the temporal information widely present in production activities, leading to the insufficient utilization of time-series data when faults occur. Currently, fault diagnosis in the field is mainly divided into three models: physics-based fault diagnosis methods, knowledge-based fault diagnosis methods, and data-driven fault diagnosis methods. However, the first two models rely heavily on the expertise of industry specialists, and their diagnostic results often exhibit a certain degree of latency. In contrast, data-driven fault diagnosis methods typically treat it as a classification problem, using real-time operational data for fault detection. However, this approach fails to fully consider the cumulative nature of faults, which may result in insufficient accuracy and timeliness of fault diagnosis. Therefore, there is an urgent need for a diagnostic method that can address the issue of fault occurrence delay. This method should effectively integrate temporal information, predict the development trend of faults, and enhance the accuracy and reliability of fault diagnosis, providing a more reliable solution for fault diagnosis. Digital twin is a technology that combines the digital model of physical systems with virtual simulation techniques. This technology enables the simulation and optimization of actual systems in a virtual environment to improve their performance and efficiency, achieving a synergistic effect where the virtual reflects the real and the real drives the virtual [3]. Convolutional neural networks (CNNs) were initially widely applied in image recognition and have significantly contributed to the development of deep learning.