将根本原因分析集成到机器学习解释中,以预测未来的故障
Taufik Aditiyawarman1, Johny Wahyuadi Soedarsono1, Agus Paul Setiawan Kaban1
1Prof Johny Wahyuadi Laboratory, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia.
Heliyon
|June 30, 2023
概括
本研究引入了用于海上管道腐蚀评估的AI模型,改进了风险评估和缓解策略. 它识别了二氧化碳腐蚀,并对风险进行了分类,以提高管道的寿命和安全性.
科学领域:
- 材料科学与工程 材料科学与工程
- 腐蚀科学 腐蚀科学
- 在工程领域的人工智能.
背景情况:
- 现有的海上管道检查方法缺乏有效的数据利用,以预测损失和减轻损失.
- 根源原因分析数据在预测潜在的管道故障时未得到充分利用.
- 需要先进的方法来评估和减轻管道腐蚀风险.
研究的目的:
- 开发一种新的人工智能 (AI) 模型,用于评估海上管道腐蚀.
- 通过故障分析知识,提高潜在损失和腐蚀缓解的预测.
- 通过结合实验和建模方法,建立可行和实际的检查方法.
主要方法:
- 利用元素组成,硬度和抗拉性测试来分析金属特性和腐蚀产品.
- 使用扫描电子显微镜与能量散射X射线 (SEM-EDX) 和X射线微波测量 (XRD) 来研究腐蚀机制.
- 实施了高斯混合模型 (GMM) 与皮尔森多线性矩阵用于风险评估和损害预测.
主要成果:
- 确定了广泛和浅坑腐蚀和道化作为明显的损害机制.
- 通过拉伸和硬度测试确认材料为API 5L X42 PSL 1标准.
- 使用SEM-EDX和XRD分析确定CO2腐蚀是腐蚀产品的主要原因.
- 转基因菌分析显示了三种不同的风险水平:低,中,高,通过轮得分和贝叶斯信息标准进行验证.
结论:
- 这种由人工智能驱动的模型有效地评估了海上管道风险,并预测了损害机制.
- 推的缓解策略包括化学注射 (雨,生物杀伤剂) 和对二氧化碳腐蚀的防腐.
- 该研究为基于风险的检查和集群的海上管道完整性提供了指导方针.
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