# Predictive Maintenance Based on Identity Resolution and Transformers in IIoT  
[PERSON]  
1 State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China; [EMAIL_ADDRESS]. structures and attention to the time dependence of sequence modeling. The recursive structure contributes to the characteristic expression of the sensor signal, but the long-term and short-term dependence on a high-frequency vibration signal are hardly captured. Our proposed framework aims to enable real-time maintenance forecasting, which requires a sequence model with the capability of state sensing (i.e., classification of the current state of the device) and sequence prediction (i.e., prediction of future signal sequences). In particular, the future state awareness of a device relies strongly on the capability of sequence prediction. Therefore, an efficient model needs to be designed to efficiently capture the dependence of sequence prediction over time. Notably, a novel transformer model [9] has emerged to support sequence-to-sequence prediction tasks, with its key components consisting of a series of stacked self-attention modules. The self-attention module is structured to extract global dependencies between data, thus enabling the parallel computing capability of the algorithm and thus improving the operational efficiency of the prediction algorithm. Transformers have shown good performance in time-series data prediction, such as the success of ChatGPT [10; 11]. Therefore, motivated by transformers' stacking structure and efficient prediction, this article proposes a framework for predictive maintenance based on the structure of transformers. According to the report from an intelligent platform of GE, a manufacturer of healthcare products generates 5k samples every 33 milliseconds, which amounts to roughly four trillion samples per year. Additionally, one manufacturing facility equipped with 100 machine tools and 10 cameras produces an annual data volume of approximately 72 terabytes [12]. Due to the complexity of computing and further scaling considerations, traditional on-premise servers are not sufficient to handle these large volumes of data, making it necessary to deploy cloud service centers. However, simulation research indicates that cloud data centers may experience higher latency and network usage because IIoT devices are geographically distant from them. Edge computing brings resources from the cloud down to the edge of the network, expanding the resources of end devices and thus significantly reducing system latency and the occurrence of network congestion. Consequently, edge computing can facilitate the execution of latency-sensitive applications. Therefore, edge computing technology is employed in this paper to support predictive maintenance. Simultaneously, IIoT technology deploys a variety of sensors on devices, and by collecting and analyzing real-time data from these sensors, the health of a device can be tracked and identified in real time. However, in order to bridge the gap between the product, machine, workshop, and factory, to enable the underlying data collection to scale and to share data between information systems, an efficient and effective identification technology is required to assist in the management of equipment. It is more important to uniquely identify the equipment than the workshop. Identity resolution technology can provide support to equipment management and traceability [12]. In our proposed framework, the identity resolution technology DID is employed to perform auxiliary positioning. In this paper, a framework consisting of identity resolution and a transformer for predictive maintenance (ITPM) is proposed. The ITPM consists of two models, the state awareness model and the sequence prediction model, respectively, which are utilized to analyze vibration signals. Firstly, the state awareness model can sense the condition of devices based on the current sensor signal sequences.