Structural control and health monitoring as condition monitoring are some essential areas that allow for different system parameters to be designed, supervised, controlled, and evaluated during the system’s operation in different processes, such as those used in machinery, structures, and different physical variables in mechanical, chemical, electrical, aeronautical, civil, electronics, mechatronics, and agricultural engineering applications, among others.
Continuous monitoring of these structures is a need because these are subject to changes in environmental and operation conditions along their lifetime, which can result in changes and possible fails and damages in all the structure and their components. The proper development of these applications is associated with the use of reliable data from sensors or sensor networks, which requires the use of advanced signal processing techniques, sensor data fusion, and data processing (sometimes in real-time) to produce a reliable system and avoid accidents or failures in the process.
After a rigorous peer review process, a total of 19 papers were published, covering different aspects of condition monitoring, structural control, and health monitoring (SCHM).
Damage identification process is addressed into the structural health monitoring (SHM) task to determine different levels of the state of a structure. These levels include damage detection and localization, the knowledge of the type and extent of damage, the prediction of the remaining lifetime, and the development of smart structures. Each of these steps can be addressed from different points of view, but one of the more used is data-driven strategies. In [1], the use of data-driven algorithms is explored at each level of the damage diagnosis as well as the instrumentation and implementation process to show the current state of some of the developments of data-driven SHM.
In terms of the use of sensors for industrial applications, the design of algorithms and methodologies to process all data from sensors still remains as an open research topic. In the case of the development of artificial taste recognition systems, it implies the use of data-driven algorithms and methodologies to monitor the condition and quality of a process as in the case of the food industry. The work of [2] explores the use of a new nonlinear feature extraction-based approach using manifold learning algorithms to improve the classification accuracy in an electronic tongue sensor array. The paper results show that it is possible to perform the classification in a data set belonging to seven different aqueous matrices with nine samples per class for a total of 63 samples with an accuracy of 96.83%.
During its operation, the structures are subject to conditions that can result in failure or damage and affect its normal behavior. Structures using reinforced concrete can be affected by corrosion resulting in risk for operation. As a contribution to monitoring these kinds of structures, in [3] the authors develop a device that can be embedded into the concrete at various locations and depths to monitor corrosion. Results show that it is possible to determine the corrosion in a structure by measuring its electrical resistance with the developed device.
SHM and condition monitoring have multiple applications in civil infrastructure and multiple works address developments to monitor beams. In [4] it is possible to find an alternative to the techniques that make use of structural analysis and strain gauge measurements to locate the neutral axis of a T-Beam bridge. This work shows the analysis of ultrasonic coda waves in a sensor network and explores its advantages in locating the neutral axis and evaluate the global structural health and inner damages.
Read more: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7956286/