基于FCM聚类的石油化工压力管道疲劳寿命预测

Fatigue Life Prediction of Petrochemical Pressure Pipelines based on FCM Clustering

由于传统寿命预测方法依赖于失效数据信息,而大多数产品的失效数据非常有限,导致寿命预测准确性差。为此,研究了基于模糊c-均值聚类(fuzzy c-means clustering,FCM)的石油化工压力管道疲劳寿命预测方法,采用小波支持向量回归机建立退化轨迹模型,利用遗传算法优化模型参数;通过压力管道疲劳缺陷扩展计算模型,确定压力管道存在疲劳缺陷区域;使用FCM算法监测数据聚类过程,实现拐点估计与寿命预测。测试结果表明:利用FCM聚类算法监测不同聚类中心拐点的隶属度后,疲劳寿命预测结果与实际结果误差低于1.0 m/s,说明该方法具有较高的寿命预测准确性。

Due to the limited failure data available for most products,traditional life prediction methods that rely on failure data information often yield poor accuracy.Therefore,the fatigue life prediction method of petrochemical pressure pipelines is studied based on Fuzzy clustering(FCM).A degradation trajectory model was established using wavelet support vector regression,and the model parameters were optimized using a genetic algorithm.The calculation model for fatigue defect extension in pressure pipelines was employed to determine the areas of fatigue defects.The FCM algorithm was used for monitoring the data clustering process to achieve inflection point estimation and life prediction.The test results show that by using the FCM clustering algorithm to monitor the membership of different cluster center's inflection points,the error between the fatigue life prediction results and the actual results was less than 1.0 m/s,indicating a high level of accuracy in life prediction using this method.