基于数据可靠性的工业机器人智能产线运维标准化展望OA

With the ongoing transition of intelligent manufacturing under the background of Industry 4.0,the core role of industrial robot intelligent production lines in the production process has become increasingly prominent.The operation and maintenance strategies are shifting from traditional corrective and preventive approaches toward predictive maintenance based on equipment conditions.The effectiveness of predictive maintenance,however,depends critically on high-quality data.A major challenge in current industrial practice is the lack of data reliability,manifested as poor accuracy,incompleteness,inconsistency and latency,all of which severely compromise the fault prediction and maintenance decision-making.To address this issue,a systematic review was conducted on operation and maintenance standardization strategies for industrial robot intelligent production lines based on data reliability.Firstly,the data reliability's influence mechanism was analyzed from technological,managerial and cost perspectives,revealing the root causes of data degradation during acquisition,transmission and processing stages and their constraining effects on operation and maintenance performance.Secondly,the gaps and needs in the existing operation and maintenance standardization system were identified,particularly concerning cost,feasibility,service model and personnel training system.Finally,policy suggestions were put forward to promote the operation and maintenance standardization construction throughout the entire data lifecycle,including establishing a data confidence level classification standard,unifying semantic interoperability protocols,and constructing a risk-sharing business model based on data credit,etc.The aim is to provide both theoretical basis and practical guidance for building a reliable,implementable and sustainable operation and maintenance standardization system for intelligent production lines.