SHMTools: Structural Health Monitoring Software for Aerospace, Civil, and Mechanical Infrastructure

Abstract

The cover depicts the various applications that benefit from the SHMTools software suite, from aircraft to bridges to mechanical structures. SHMTools provides more than 100 advanced algorithms that quickly prototype and evaluate damage-detection processes for Structural Health Monitoring. The drag-and-drop analysis interface is pictured on the left-hand side of the cover. Using SHMTools, it is possible to detect damage in such structures without removing them from service to correct problems before they become hazardous.

Features of Cross-Correlation Analysis in a Data-Driven Approach for Structural Damage Assessment

“…“Structural health monitoring (SHM) is the integration of a sensory system, a data acquisition system, a data processing and archiving system, a communication system, a damage detection system, and a modeling system to acquire knowledge about the integrity and load worthiness of in-service structures on either a temporary or continuous basis” [ 1 ]. Likewise, according to Farrar and Worden [ 2 ], SHM can be defined as the process of implementing a damage identification strategy for aerospace, civil, and mechanical engineering infrastructure. The benefits of implementing damage identification strategy through an SHM system are an avoidance of premature breakdowns, a reduction of maintenance costs, continuous remote diagnosis, and economic benefits in terms of an operational life extension.…”

“…These features are exploited by classification, regression, or clustering algorithms with the ability to identify abnormal conditions (i.e., possible damage). Thus, by implementing the scheme depicted in Figure 1 , basic SHM levels can be achieved: diagnosis constituted by damage detection (Level 1), location (Level 2), quantification (Level 3), and prognostics by estimating the damage evolution (Level 4), where feature extraction receives most of the attention in the literature [ 2 ]. Since the SHM process requires features with high sensitivity to distinguish between undamaged and damaged conditions, this process should be robust to noisy measurements.…”