Digital Twin technology is emerging as the digitization platform to enhance the industrial information processing and management in concern with virtual and physical entities. It paves the path for integrated industrial data analysis by combining IoT and Artificial Intelligence for better data interpretation. At present in oil industry, pipelines prevail to be feasible mode, the risk probability rate is getting increased and maintenance system becomes difficult with attention to the earlier prediction of accidents risks by undertaking entire pipeline. This paper aims to provide the frame structure of Digital Twin based on machine learning and prognostics algorithms model to analyze and predict the risk probability rate of oil pipeline system. Prognostics focuses on the detection of a failure precursor by estimating risk condition with respect to the pressure data towards the evaluation of remaining useful life (RUL). The abnormality of pressure attribute is taken in prognostic analysis for risk probability estimation followed by Dirichlet Process Clustering and Canopy clustering to segregate the abnormal pressure drop and rise. Using multiple oil substation data integration platform, the features are extracted using manifold learning methods and the best feature probability rates are evaluated using kernel based SVM algorithm to provide on-time control action on the entire oil pipeline system through efficient wireless data communication between server and the oil substations. As a result, the proposed work creates Virtual Intelligent Integrated Automated Control System to predict the risk rate in oil industry by integrating entire transmission lines through enhanced wireless information networks in remote locations.

数字孪生技术正在作为数字化平台出现，以增强与虚拟和物理实体相关的工业信息处理和管理。它通过结合物联网和人工智能来更好地解释数据，为集成工业数据分析铺平了道路。目前在石油行业，管道成为可行的模式，风险概率越来越高，维护系统变得困难，需要通过整条管道提前预测事故风险。本文旨在提供基于机器学习和预测算法模型的数字孪生框架结构，以分析和预测石油管道系统的风险概率。预测侧重于通过估计与压力数据相关的风险条件来检测故障前兆，以评估剩余使用寿命 (RUL)。在风险概率估计的预后分析中采用压力属性的异常，然后进行狄利克雷过程聚类和冠层聚类以分离异常压降和升高。利用多油站数据集成平台，通过流形学习方法提取特征，并使用基于核的SVM算法评估最佳特征概率，通过服务器和服务器之间的高效无线数据通信，对整个输油管道系统提供准时控制动作。石油变电站。因此，