A Novel Pipeline Age Evaluation: Considering Overall Condition Index and Neural Network Based on Measured Data

Abstract

Today, the chemical corrosion of metals is one of the main problems of large productions, especially in the oil and gas industries. Due to massive downtime connected to corrosion failures, pipeline corrosion is a central issue in many oil and gas industries. Therefore, the determination of the corrosion progress of oil and gas pipelines is crucial for monitoring the reliability and alleviation of failures that can positively impact health, safety, and the environment. Gas transmission and distribution pipes and other structures buried (or immersed) in an electrolyte, by the existing conditions and due to the metallurgical structure, are corroded. After some time, this disrupts an active system and process by causing damage. The worst corrosion for metals implanted in the soil is in areas where electrical currents are lost. Therefore, cathodic protection (CP) is the most effective method to prevent the corrosion of structures buried in the soil. Our aim in this paper is first to investigate the effect of stray currents on failure rate using the condition index, and then to estimate the remaining useful life of CP gas pipelines using an artificial neural network (ANN). Predicting future values using previous data based on the time series feature is also possible. Therefore, this paper first uses the general equipment condition monitoring method to detect failures. The time series model of data is then measured and operated by neural networks. Finally, the amount of failure over time is determined.

Pipeline failure evaluation and prediction using failure probability and neural network based on measured data

“…References [ [9] , [10] , [11] , [12] , [13] ] have also done life estimation on railways. Reference [ 14 ] has investigated the functional life of pipeline using a general condition index and neural networks.…”

Optimal planning and partitioning of multiple distribution Micro-Grids based on reliability evaluation

“…Our paper proposes a reliability-based model in which the optimal sites and sizes of DERs, DRSs and ESSs in MMGs and also TSs placement in the distribution network have been determined, simultaneously. In references [ 28 – 30 ], methods for estimating the life of electrical network equipment have been examined.…”

Performance Improvement of Speech Emotion Recognition Systems by Combining 1D CNN and LSTM with Data Augmentation

“…Consequentially, this paper aims to develop a speech emotion recognition system with better accuracy and more stable performance by combining a convolutional neural network (CNN) and long short-term memory neural networks (LSTM) with a data augmentation technique. It is worthwhile to mention that the convolutional neural network is one of neural network architecture and is a machine learning method that is widely applied in many applications; see, for example, the papers [20][21][22][23][24].…”