某气田集输管线热缩套下存在严重外腐蚀导致管线失效,经开挖发现,集输管线热缩套普遍无粘结力并剥离,多处发现热缩套内有水存在,钢管表面布满腐蚀坑,腐蚀速率0.08 mm/a~0.88 mm/a,通过化学成分分析、金相分析、硬度分析、强度性能分析...某气田集输管线热缩套下存在严重外腐蚀导致管线失效,经开挖发现,集输管线热缩套普遍无粘结力并剥离,多处发现热缩套内有水存在,钢管表面布满腐蚀坑,腐蚀速率0.08 mm/a~0.88 mm/a,通过化学成分分析、金相分析、硬度分析、强度性能分析、防腐层理化测试与形貌分析、腐蚀形貌分析、腐蚀产物截面分析、腐蚀产物成分分析等方法对其失效原因进行分析。结果表明:失效直接原因为管体外壁发生了严重的氧腐蚀,根本原因管线运行温度(>50℃)超过防腐层设计耐温能力,导致防腐层老化严重,失去保护作用。根据造成失效的原因,提出了具体对策。There is serious external corrosion under the heat shrink sleeve of a gas field gathering pipeline, which leads to the failure of the pipeline. After excavation, it is found that the heat shrink sleeve of the gathering pipeline generally has no bonding force and is peeled off. Water is found in the heat shrink sleeve in many places, and the surface of the steel pipe is covered with corrosion pits, and the corrosion rate is 0.08 mm/a~0.88 mm/a. The failure causes were analyzed by chemical composition analysis, metallographic analysis, hardness analysis, strength analysis, physical and chemical test and morphology analysis of anti-corrosion layer, corrosion morphology analysis, corrosion product cross section analysis, corrosion product composition analysis and other methods. The results show that the direct cause of failure is serious oxygen corrosion in the outer wall of the pipe, and the root cause is that the operating temperature of the pipeline (>50˚C) exceeds the designed temperature resistance of the anti-corrosion layer, resulting in serious aging of the anti-corrosion layer and loss of protection. According to the causes of failure, the specific countermeasures are put forward.展开更多
钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井...钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井顶部驱动装置故障诊断方法,利用以Transformer为基础的双向编码器模型(Bidirectional Encoder Representations from Transformers,BERT)构建了混合神经网络模型BERT-BiLSTM-CRF与BERT-BiLSTM-Attention,分别实现了顶驱故障文本数据的命名实体识别和关系抽取,并通过相似度计算,实现了故障知识的有效融合和智能问答,最终构建了顶部驱动装置故障诊断方法。研究结果表明:①在故障实体识别任务上,BERT-BiLSTM-CRF模型的精确度达到95.49%,能够有效识别故障文本中的信息实体;②在故障关系抽取上,BERT-BiLSTM-Attention模型的精确度达到93.61%,实现了知识图谱关系边的正确建立;③开发的问答系统实现了知识图谱的智能应用,其在多个不同类型问题上的回答准确率超过了90%,能够满足现场使用需求。结论认为,基于知识图谱的故障诊断方法能够有效利用顶部驱动装置的先验知识,实现故障的快速定位与智能诊断,具备良好的应用前景。展开更多
文摘某气田集输管线热缩套下存在严重外腐蚀导致管线失效,经开挖发现,集输管线热缩套普遍无粘结力并剥离,多处发现热缩套内有水存在,钢管表面布满腐蚀坑,腐蚀速率0.08 mm/a~0.88 mm/a,通过化学成分分析、金相分析、硬度分析、强度性能分析、防腐层理化测试与形貌分析、腐蚀形貌分析、腐蚀产物截面分析、腐蚀产物成分分析等方法对其失效原因进行分析。结果表明:失效直接原因为管体外壁发生了严重的氧腐蚀,根本原因管线运行温度(>50℃)超过防腐层设计耐温能力,导致防腐层老化严重,失去保护作用。根据造成失效的原因,提出了具体对策。There is serious external corrosion under the heat shrink sleeve of a gas field gathering pipeline, which leads to the failure of the pipeline. After excavation, it is found that the heat shrink sleeve of the gathering pipeline generally has no bonding force and is peeled off. Water is found in the heat shrink sleeve in many places, and the surface of the steel pipe is covered with corrosion pits, and the corrosion rate is 0.08 mm/a~0.88 mm/a. The failure causes were analyzed by chemical composition analysis, metallographic analysis, hardness analysis, strength analysis, physical and chemical test and morphology analysis of anti-corrosion layer, corrosion morphology analysis, corrosion product cross section analysis, corrosion product composition analysis and other methods. The results show that the direct cause of failure is serious oxygen corrosion in the outer wall of the pipe, and the root cause is that the operating temperature of the pipeline (>50˚C) exceeds the designed temperature resistance of the anti-corrosion layer, resulting in serious aging of the anti-corrosion layer and loss of protection. According to the causes of failure, the specific countermeasures are put forward.
文摘钻井顶部驱动装置结构复杂、故障类型多样,现有的故障树分析法和专家系统难以有效应对复杂多变的现场情况。为此,利用知识图谱在结构化与非结构化信息融合、故障模式关联分析以及先验知识传递方面的优势,提出了一种基于知识图谱的钻井顶部驱动装置故障诊断方法,利用以Transformer为基础的双向编码器模型(Bidirectional Encoder Representations from Transformers,BERT)构建了混合神经网络模型BERT-BiLSTM-CRF与BERT-BiLSTM-Attention,分别实现了顶驱故障文本数据的命名实体识别和关系抽取,并通过相似度计算,实现了故障知识的有效融合和智能问答,最终构建了顶部驱动装置故障诊断方法。研究结果表明:①在故障实体识别任务上,BERT-BiLSTM-CRF模型的精确度达到95.49%,能够有效识别故障文本中的信息实体;②在故障关系抽取上,BERT-BiLSTM-Attention模型的精确度达到93.61%,实现了知识图谱关系边的正确建立;③开发的问答系统实现了知识图谱的智能应用,其在多个不同类型问题上的回答准确率超过了90%,能够满足现场使用需求。结论认为,基于知识图谱的故障诊断方法能够有效利用顶部驱动装置的先验知识,实现故障的快速定位与智能诊断,具备良好的应用前景。
