Current challenges and future outlooks of pipeline structural health monitoring: A review

Öz

This paper presents a comprehensive literature review on data-driven structural health monitoring (SHM) approaches for pipelines. The review explores the common failure modes, driving signals, sensor technologies, and the application of smart techniques in pipeline SHM based on artificial intelligence (AI) and machine learning (ML). The analysis of a significant number of publications reveals that corrosion, erosion, cracks, and deformation are among the most prevalent failure modes, while a diverse range of driving signals, including time series data, vibration, temperature, and acoustic emissions, have been utilized for monitoring. The review also highlights the growing prominence of sensor technologies, such as optical fiber sensors, ultrasound techniques, and piezoelectric sensors. The application of AI and ML techniques, including supervised learning models, deep learning, and ensemble methods, has demonstrated significant potential in enhancing pipeline SHM capabilities, enabling accurate prediction and identification of failures and optimization of service strategies. Furthermore, the review identifies the emergence of promising technologies, such as energy harvesting, the Internet of Things (IoT), robotics, and drones, which offer creative approaches to tackle the issues in pipeline SHM. The review concludes by discussing key challenges, providing recommendations, and outlining future outlooks to guide the advancement of pipeline SHM through collaborative efforts, industry standards, and continued research and development, and to assist researchers, novice students, and practitioners to focus their work on worthy research points in order to avoid repetitions and to present beneficial novel studies.

Anahtar Kelimeler

• Structural Health Monitoring (SHM)
• Pipeline anomalies
• Data-Driven systems
• Artificial Intelligence (AI)
• Machine Learning (ML)
• Ensemble Learning

Boru hattı yapı sağlığı izlemesinde mevcut zorluklar ve geleceğe bakış: Bir inceleme

Öz

Bu makale, boru hatları için veri odaklı yapısal sağlık izleme (SHM) yaklaşımlarına dair kapsamlı bir literatür incelemesi sunmaktadır. İnceleme, yaygın arıza modlarını, tetikleyici sinyalleri, sensör teknolojilerini ve boru hattı SHM'sinde yapay zekâ (AI) ve makine öğrenimi (ML) tekniklerinin uygulanmasını araştırmaktadır. Önemli sayıda yayının analizi, korozyon, aşınma, çatlaklar ve deformasyonun en yaygın arıza modları arasında olduğunu ortaya koyarken, izleme için zaman serisi verileri, titreşim, sıcaklık ve akustik emisyonlar gibi çeşitli tetikleyici sinyallerin kullanıldığını göstermektedir. İnceleme ayrıca, optik fiber sensörler, ultrason teknikleri ve piezoelektrik sensörler gibi sensör teknolojilerinin artan önemini vurgulamaktadır. AI ve ML tekniklerinin, denetimli öğrenme modelleri, derin öğrenme ve toplu yöntemler dahil olmak üzere, boru hattı SHM yeteneklerini artırmada önemli bir potansiyel gösterdiği, doğru anomali tespiti, arıza tahmini ve bakım stratejilerinin optimizasyonunu sağladığı belirtilmektedir. Ayrıca, inceleme, enerji toplama, Nesnelerin İnterneti (IoT), robotik ve dronlar gibi boru hattı SHM'deki sorunları ele almak için yaratıcı yaklaşımlar sunan umut verici teknolojilerin ortaya çıkışını tanımlamaktadır. İnceleme, anahtar zorlukları tartışarak, önerilerde bulunarak ve boru hattı SHM'sinin ilerlemesini yönlendirmek için iş birliği çabaları, endüstri standartları ve sürekli araştırma ve geliştirme yoluyla gelecekteki beklentileri özetleyerek sona ermektedir; ayrıca araştırmacılara, acemi öğrencilere ve uygulayıcılara, çalışmalarını tekrarlardan kaçınmak ve faydalı yeni çalışmaları sunmak için değerli araştırma noktalarına odaklanmaları konusunda yardımcı olmaktadır.

Anahtar Kelimeler

• Yapısal Sağlık İzleme (SHM)
• Boru Hattı anormallikleri
• Veriye Dayalı sistemler
• Yapay Zeka (AI)
• Makine Öğrenimi (ML)
• Topluluk Öğrenimi

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