# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 1 Introduction  
Fault diagnosis is a critical task in large-scale industrial maintenance. With the advent of Industry 4.0 and the continuous increase in automation, a vast amount of equipment operational data are being generated, leading to the rapid development of data-driven fault diagnosis methods [1, 2]. As a type of machinery widely used across various industries, rotating machinery holds a significant position in industrial systems such as petrochemical, nuclear and thermal power, and steel forging [3]. Electric motors and gear reducers are key rotating machinery in the steel forging industry. In recent years, data-driven intelligent fault diagnosis techniques have been applied to the fault diagnosis of rotating machinery. To address the limitations of the cross-scale coupling relationship between amplitude modulation and frequency modulation characteristics in nonlinear vibration signals, [PERSON] et al. proposed a fault diagnosis method for rotating machinery based on improved holographic Hilbert spectrum analysis [4]. This method provides a more comprehensive representation and visualization of the internalmodulation relationships in nonlinear fault vibration signals and demonstrates enhanced fault identification capabilities. Simultaneously, sudden unbalanced faults in rotating machinery remain a challenging issue in the field of condition monitoring in engineering practice. To address the lack of fault data, many scholars have undertaken related research efforts [5]. [PERSON] et al. employed normalized generalized nonlinear convolution activation to address the problem of distributional changes caused by scale inconsistency in nonlinear functions. The proposed nonlinear sparse blind deconvolution method for early bearing fault diagnosis offers superior noise adaptability, reduced computational time, and enhanced robustness [6]. While these fault diagnosis methods exhibit high accuracy and strong generalization capabilities, they often rely on deep learning models with complex structures. These models typically require raw signals or time-frequency transformations of the original signals as inputs. Additionally, both the training and testing datasets are usually offline data, which limits their ability to meet the real-time diagnostic requirements of specific application scenarios [7]. Consequently, a significant number of scholars have turned their attention to online fault diagnosis [8; 9; 10]. These studies typically utilize cloud computing or host computers, where trained models are stored to perform online diagnostics on real-time data. However, these research outcomes encounter two primary challenges in engineering applications: 1. Uploading real-time high-frequency data to the cloud requires substantial network bandwidth and continuously depletes the storage and computational resources of cloud servers. Moreover, the storage and processing capabilities of host computers are often inadequate to meet the demands of practical applications. 2. If feature data pre-processed by lower-level computers are chosen for upload, the data volume is reduced; however, this approach introduces latency issues. Even if the cloud can perform effective diagnostics, the time delays inherent in communication and computation processes may compromise the fidelity of the original signals, potentially adversely affecting the timeliness and accuracy of fault diagnosis [7]. Therefore, for machines that require real-time diagnostics and dynamic control, cloud computing is unable to meet the stringent response time requirements. To overcome challenges such as high bandwidth load and resource limitations inherent in traditional cloud computing models in industrial manufacturing, edge computing technology has been introduced.