# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
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Department of Computer Science and Engineering, VK Raman Global University, Bhubaneswar 752054, India; [EMAIL_ADDRESS]  
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Department of Petroleum Engineering, Oklahoma State University, Stillwater, OK 7 changes due to wear and tear and corrosion, leading to bending, breakage, leaks, and other damages (Scheme 1). Across more than two decades, the energy obtained through wind has gained enormous traction in the markets around the world, increasing from 2.4 to 11.4% in the EU between 2000 and 2015, which equals a 128.5 GW increase in use [4]. This is a result of the quick advances in the wind industry and the targets set by the European Union. The offshore placements of the wind farms have become unexpectedly the most profitable among all and produce more than all other renewable energy sources combined [5]. In Europe itself, 84 sites existed at the end of 2015 [6; 7]. The structural health monitoring (SHM) systems play an important role in maximizing the potentials of the wind energy by increasing their life and efficiency through advanced monitoring. SHM allows for the identification of the damage and the implementation of those techniques. The basic causes of damage would include stresses, hysteresis, and wind or atmospheric erosions [8] not only in wind farms but also in other infrastructures associated with the generation of power and transportation in the energy sector; e.g., turbines, motors, pumps, and pipelines also require detailed inspection. Monitoring finds its applications in the civil, infrastructural, and aerodynamic fields, requiring some amount of studies for geometry and structures. The two major and most influential aspects that control the monitoring include the sensing technology and the interpretation algorithm that allows for information obtained from sensors to be converted into legible quantities that can in turn be used to provide the best solution manually or through technologies [9]. The basic elements of an ideal monitoring system would require the best measurements of damages and provide a reliable analysis in time to save and guard it from damages. The normalization and integration of data using the techniques of data science are the important aspects of processing for the SHM systems because the data received is basically in raw forms and needs to be utilized meaningfully. The preprocessed data are required to be worked on to extract the cracks, leaks, or structural damages or to predict future damages in structures [10]. Figure 1: Damages and their types. The key function of SHM is to maintain track of changes in the structural system's dynamic properties for detecting and locating damage, as well as to automatically determine whether the damage is harmful to the structure. Damage detection usually entails data processing to look for changes in structural dynamic characteristics (such as modal frequency, damping ratio, and mode shape) as well as inter-story drifts. Because structural damage results in a loss of stiffness, and the dynamic characteristics of a structure are directly connected to stiffness, it is reasonable to utilise natural frequency variations as a damage indicator. Changes in natural frequency are not always a reliable sign of damage, as the reaction of the damaged structure is nonlinear, and in most cases hysteretic, according to examinations of recorded data from structures. Furthermore, different environmental variables (for example, temperature) can alter the natural frequency of buildings without causing any structural harm (Scheme 1).