The world of oil pipelines is subjected to serious issues due to occurrences of toxic spills, explosions and deformations like particle deposition, corrosions and cracks due to the contact of oil particles with the pipeline surface. Hence, the structural integrity of these pipelines is of great interest due to the probable environmental, infrastructural and financial losses in case of structural failure. Based on the existing technology, it is difficult to analyze the risks at the initial stage, since traditional methods are only appropriate for static accident analyses. Nevertheless, most of these models have used corrosion features alone to assess the condition of pipelines. To sort out the above problem in the oil pipelines, fault identification and prediction methods based on K-means clustering and Time-series forecasting incorporated with linear regression algorithm using multiple pressure data are proposed in this paper. The real-time validation of the proposed technique is validated using a scaled-down experimental hardware lab setup resembling characteristics exhibited by onshore unburied pipeline in India. In the proposed work, crack and blockages are identified by taking pressure rise and pressure drop inferred from two cluster assignment. The obtained numerical results from K-means clustering unveils that maximum datasets accumulated range of multiple pressures are within 16.147–10.638 kg/cm², 14.922–12.1674 kg/cm², 2.7645–1.2063 kg/cm² correspondingly. Hence by this final cluster center data, inspection engineers able to estimate the normal and abnormal performance of oil transportation in a simple-robust manner. The developed forecast model successfully predicts future fault occurrences rate followed by dissimilarity rate from clustering results holds the validity of 91.9% when applied to the historical pressure datasets. The models are expected to help pipeline operators without complex computation processing to assess and predict the condition of existing oil pipelines and hence prioritize the planning of their inspection and rehabilitation.

由于有毒的溢出，爆炸和变形（如油颗粒与管道表面接触所引起的颗粒沉积，腐蚀和破裂）的发生，石油管道世界面临着严重的问题。因此，由于在结构故障的情况下可能造成的环境，基础设施和财务损失，这些管道的结构完整性备受关注。基于现有技术，由于传统方法仅适用于静态事故分析，因此很难在初始阶段分析风险。然而，大多数这些模型仅使用腐蚀特征来评估管道的状况。为了解决石油管道中的上述问题，提出了基于K均值聚类和时间序列预测的故障识别与预测方法，并结合线性回归算法，利用多种压力数据进行了预测。拟议技术的实时验证是使用按比例缩小的实验硬件实验室设置（类似于印度陆上未埋管道显示的特征）进行验证的。在拟议的工作中，通过考虑从两个群集分配中推断出的压力上升和压力下降来确定裂缝和堵塞。从K均值聚类获得的数值结果表明，多重压力的最大数据集累计范围在16.147-10.638 kg / cm之内 拟议技术的实时验证是使用按比例缩小的实验硬件实验室设置（类似于印度陆上未埋管道显示的特征）进行验证的。在拟议的工作中，通过考虑从两个群集分配中推断出的压力上升和压力下降来确定裂缝和堵塞。从K均值聚类获得的数值结果表明，多重压力的最大数据集累计范围在16.147-10.638 kg / cm之内 拟议技术的实时验证是使用按比例缩小的实验硬件实验室设置（类似于印度陆上未埋管道显示的特征）进行验证的。在拟议的工作中，通过考虑从两个组分配推断出的压力上升和压力下降来确定裂缝和堵塞。从K均值聚类获得的数值结果表明，多重压力的最大数据集累计范围在16.147-10.638 kg / cm之内²，14.922-12.1674公斤/厘米²，2.7645-1.2063公斤/厘米²相应。因此，通过最终的群集中心数据，检查工程师能够以简单，可靠的方式估计石油运输的正常和异常性能。所建立的预测模型成功地预测了未来的断层发生率，然后从聚类结果中预测出相异率，将其应用于历史压力数据集时的有效性为91.9％。该模型有望帮助无需复杂计算过程的管道运营商评估和预测现有石油管道的状况，从而优先安排检查和修复计划。