# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 2 Related Work  
To address the limitations of lightweight diagnostic networks in edge computing, [PERSON] et al. proposed a Weighted Multi-Scale Convolution (WSMSC) method and an adaptive pruning technique that eliminates redundant network structures during training without requiring manual intervention [12]. This approach captures multi-time-scale features from vibration signals at a very low computational cost, enabling intelligent diagnosis of train bogie bearing faults in edge computing environments. [PERSON] et al. investigated the application of bearing fault diagnosis models at the edge, utilizing the Kendryte K210 edge AI device to accelerate the inference speed of convolutional neural networks [13]. In terms of data privacy and security, edge devices in bearing condition monitoring systems can function as gateways, preventing data from being transmitted from sensors to cloud servers [14]. Another approach involves using a decentralized model to ensure the privacy and security of machinery fault data within the Industrial Internet of Things (IIoT) [15]. It is evident that edge computing has become a significant research focus, showing excellent accuracy and real-time performance in the fault diagnosis of rotating machinery, such as motors and bearings. However, most studies focus on fault diagnosis using single-modal sensors. In practical industrial environments that integrate edge computing with fault diagnosis, information obtained from a single sensor can be limited and isolated. If the sensor experiences issues such as improper installation, severe noise interference, or poor connectivity, the data it collects may become entirely unreliable. Therefore, fault diagnosis methods based on multi-sensor data fusion are essential in industrial environments. As mentioned in this paper, Dempster-Shafer (D-S) evidence theory has been widely applied in many fields of MSIF [16]. D-S evidence theory can be used for feature-level fusion or decision-level fusion. Scholar [17] proposed a fault diagnosis method for distribution networks based on a Bayesian network and D-S evidence theory, where the fault probabilities obtained from two Bayesian networks are fused using D-S evidence theory, thus achieving fault diagnosis in the distribution network. Unlike probability theory, which only considers a single proposition, D-S evidence theory introduces the concept of fuzziness, allowing it to better express the uncertainty of the existence of a proposition [17]. Both domestic and international research on multi-sensor data fusion primarily focus on different fusion algorithms at various data levels. The data fusion levels are generally classified into data-level fusion, feature-level fusion, and decision-level fusion, with specific methods for each level presented in Table 1.  
Currently, there is limited research on multi-source fault diagnosis and prediction technologies for production line equipment in complex working environments. Furthermore, existing studies face challenges such as low fault diagnosis accuracy with single sensors under complex conditions, insufficient evidence, and data redundancy. Therefore, it is necessary to investigate edge-computing-driven equipment fault diagnosis methods based on multi-level event interconnected intelligence. By utilizing edge computing technology, which leverages the embedded computing capabilities of IoT devices, in combination with  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods and Characteristics** & **Data-Level Fusion** & **Feature-Level Fusion** & **Decision-Level Fusion** \\ \hline \multirow{4}{*}{Methods} & Weighted fusion algorithms [18], Kalman filter [19], least squares method, etc. & Neural networks [20], compressed clustering method [21], principal & Bayesian estimation [22], Dempster-Shafer theory (DS theory) [16], fuzzy theory [23], expert systems, etc. & Bayesian estimation [22], Dempster-Shafer theory (DS theory) [16], fuzzy theory [23], expert systems, etc.