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Estimation of high energy steam piping degradation using hybrid recurrent neural networks
International Journal of Pressure Vessels and Piping ( IF 3.0 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.ijpvp.2020.104127
J.L. van Niekerk , P.S. Heyns , M.P. Hindley , C. Erasmus

Abstract The degradation of high energy piping systems is very complex to simulate due to the many variables that influence the useful lives of such systems. Estimation of the extent of degradation is however important in the maintenance planning process. In this research the use of data driven machine learning techniques to deal with this complex problem is investigated. A hybrid recurrent neural network is created that consists of a combined recurrent neural network and a feed forward neural network. The hybrid model is trained on historical data that has been captured over a six-year time period. The following variables related to piping system components are used as inputs to the machine learning model: operating temperature and pressure time history, distance to the closest anchor point, distances to neighbouring supports, elevation survey readings, as well as the last known creep damage measurements on the component. The model is created in Python using the Tensorflow library. A recurrent neural networks (RNN) is employed, namely the gated recurrent unit (GRU). The adaptive movement estimation optimization algorithm, called Adam, is used to optimize the machine learning model. The trained model is able to predict the degradation classification of a component with an accuracy of up to 92% on the training dataset and up to 55% on the validation data set. When using this model to predict components with high creep damage, more than 400 voids per m m 2 a hit rate of 25% is achieved. The current system employed at operating power stations shows a historic hit rate of 14%. This is a significant increase in performance and could be used to compile more efficient inspection plans. The model is successful in recognising patterns within the data and offers an automated way to parse large data sets that consist of a temporal and static data mixture simultaneously. Conventional data driven models are only able to look at either temporal data or static data. This suggests a generic approach to make objective decisions on similar complex data driven problems and its application is not limited to this particular problem. The methods applied in this research are expected to perform even better on problems where the frequency of data collection is higher than what is used in this research.

中文翻译:

使用混合递归神经网络估计高能蒸汽管道退化

摘要 由于影响高能管道系统使用寿命的许多变量,高能管道系统的退化模拟非常复杂。然而,退化程度的估计在维护计划过程中很重要。在这项研究中,研究了使用数据驱动的机器学习技术来处理这个复杂的问题。创建了一个混合递归神经网络,它由组合的递归神经网络和前馈神经网络组成。混合模型根据在六年时间段内捕获的历史数据进行训练。以下与管道系统组件相关的变量用作机器学习模型的输入:工作温度和压力时间历程、到最近锚点的距离、到相邻支架的距离、高程测量读数,以及部件上最后一次已知的蠕变损坏测量值。该模型是使用 Tensorflow 库在 Python 中创建的。采用循环神经网络(RNN),即门控循环单元(GRU)。自适应运动估计优化算法,称为 Adam,用于优化机器学习模型。经过训练的模型能够以高达 92% 的训练数据集和高达 55% 的验证数据集的准确率预测组件的退化分类。当使用该模型预测具有高蠕变损伤的组件时,每毫米 2 超过 400 个空隙,命中率达到 25%。运行中的发电站采用的当前系统显示历史命中率为 14%。这是性能的显着提高,可用于编制更有效的检查计划。该模型成功地识别了数据中的模式,并提供了一种自动化方法来解析同时包含时间和静态数据混合的大型数据集。传统的数据驱动模型只能查看时间数据或静态数据。这提出了对类似的复杂数据驱动问题做出客观决策的通用方法,其应用不限于这个特定问题。本研究中应用的方法有望在数据收集频率高于本研究中使用的频率的问题上表现得更好。该模型成功地识别了数据中的模式,并提供了一种自动化方法来解析同时包含时间和静态数据混合的大型数据集。传统的数据驱动模型只能查看时间数据或静态数据。这提出了对类似的复杂数据驱动问题做出客观决策的通用方法,其应用不限于这个特定问题。本研究中应用的方法有望在数据收集频率高于本研究中使用的频率的问题上表现得更好。该模型成功地识别了数据中的模式,并提供了一种自动化方法来解析同时包含时间和静态数据混合的大型数据集。传统的数据驱动模型只能查看时间数据或静态数据。这提出了对类似的复杂数据驱动问题做出客观决策的通用方法,其应用不限于这个特定问题。本研究中应用的方法有望在数据收集频率高于本研究中使用的频率的问题上表现得更好。这提出了对类似的复杂数据驱动问题做出客观决策的通用方法,其应用不限于这个特定问题。本研究中应用的方法有望在数据收集频率高于本研究中使用的频率的问题上表现得更好。这提出了对类似的复杂数据驱动问题做出客观决策的通用方法,其应用不限于这个特定问题。本研究中应用的方法有望在数据收集频率高于本研究中使用的频率的问题上表现得更好。
更新日期:2020-09-01
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