Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm—A Comprehensive Numerical Study

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 8; p. 3887
• Main Authors: Altabey, Wael A., Wu, Zhishen, Noori, Mohammad, Fathnejat, Hamed
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 11.04.2023; MDPI
• Subjects: Algorithms; Analysis; Artificial intelligence; composite pipelines; Convolutional Neural Network (CNN); Corrosion; damage detection; deep learning; Energy consumption; Failure; Fiber Bragg grating (FBG) sensory system; Hydrocarbons; Machine learning; Methods; Natural gas; Neural networks; Pipe lines; R&D; Research & development; Sensors; structural health monitoring (SHM); damage detection; deep learning; Fiber Bragg grating (FBG) sensory system; structural health monitoring (SHM); composite pipelines; Convolutional Neural Network (CNN)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23083887

In this paper, a structural health monitoring (SHM) system is proposed to provide automatic early warning for detecting damage and its location in composite pipelines at an early stage. The study considers a basalt fiber reinforced polymer (BFRP) pipeline with an embedded Fiber Bragg grating (FBG) sensory system and first discusses the shortcomings and challenges with incorporating FBG sensors for accurate detection of damage information in pipelines. The novelty and the main focus of this study is, however, a proposed approach that relies on designing an integrated sensing-diagnostic SHM system that has the capability to detect damage in composite pipelines at an early stage via implementation of an artificial intelligence (AI)-based algorithm combining deep learning and other efficient machine learning methods using an Enhanced Convolutional Neural Network (ECNN) without retraining the model. The proposed architecture replaces the softmax layer by a k-Nearest Neighbor (k-NN) algorithm for inference. Finite element models are developed and calibrated by the results of pipe measurements under damage tests. The models are then used to assess the patterns of the strain distributions of the pipeline under internal pressure loading and under pressure changes due to bursts, and to find the relationship of strains at different locations axially and circumferentially. A prediction algorithm for pipe damage mechanisms using distributed strain patterns is also developed. The ECNN is designed and trained to identify the condition of pipe deterioration so the initiation of damage can be detected. The strain results from the current method and the available experimental results in the literature show excellent agreement. The average error between the ECNN data and FBG sensor data is 0.093%, thus confirming the reliability and accuracy of the proposed method. The proposed ECNN achieves high performance with 93.33% accuracy (P%), 91.18% regression rate (R%) and a 90.54% F1-score (F%).