An optimal sensor placement strategy for reliable expansion of mode shapes under measurement noise and modelling error

Abstract

Modal expansion techniques are typically used to expand the experimental modal displacements at sensor positions to all unmeasured degrees of freedom. Since in most cases, sensors can be attached only at limited locations in a structure, an expansion is essential to determine mode shapes, strains, stresses, etc. throughout the structure which can be used for structural health monitoring. Conventional sensor placement algorithms are mostly aimed to make the modal displacements at sensor positions of different modes as linearly independent as possible. However, under the presence of modelling errors and measurement noise, an optimal location based on this criterion is not guaranteed to provide an expanded mode shape which is close to the real mode shape. In this work, the expected value of normal distance between the real mode shape and the expanded mode shape is used as a measure of closeness between the two entities. Optimal sensor locations can be determined by minimizing this distance. This new criterion is applied on a simple cantilever beam and an industrial milling tower. In both cases, by using an exhaustive search of all possible sensor configurations it was possible to find sensor locations which resulted in a significant reduction in the distance when compared to a conventional optimal sensor placement strategy. Sufficiently accurate sub-optimal sequential sensor placement algorithm is also suggested as an alternative to the exhaustive search which is then compared with a genetic algorithmbased search. The efficiency of this new sensor placement criterion is further verified using Monte Carlo simulations for some realistic modelling error conditions.

Advancements in Optimal Sensor Placement for Enhanced Structural Health Monitoring: Current Insights and Future Prospects

“…These contributions encompass various key facets, such as the reduction in error variance [11] for heightened precision, as well as the enhancement of damage detection [80] and early damage sensing [81]. Furthermore, researchers have directed their attention to the refinement of sensors, addressing factors including sensor normal distance [82] and apparatus configuration [33]. It is worth noting that the Kalman filter has emerged as an invaluable tool in assessing the effectiveness of sensor placement [83] and facilitating the seamless fusion of data [84].…”

Efficient Modal Identification and Optimal Sensor Placement via Dynamic DIC Measurement and Feature-Based Data Compression

“…It becomes crucial to optimally place a limited number of these sensors to capture as much information as possible. Subsequently, the reconstructed shape features obtained from these limited point sensors can expand the full field displacement [75,76]. The strain field may also be estimated from the reconstructed displacement fields [77,78].…”

Optimal sensor location for adaptive control system in tropical smart greenhouse

“…Several studies about the placement of sensors have been carried out by several researchers, including Ostachowicz et al [9], Oleynik et al [10], Yang et al [11], Suryanarayana et al [12], Chen and Gorle [13] and Jaya et al [14]. These studies are an attempt to identify and determine the location of sensor placement for maximum control and productivity in cultivation.…”