A Structural Health Monitoring Software Tool for Optimization, Diagnostics and Prognostics



Seth S.Kessler Eric B.Flynn Christopher T.Dunn Michael D.Todd


Development of robust structural health monitoring (SHM) sensors and hardware alone is not sufficient to achieve desired benefits such as improved asset availability and reduced sustainment costs. For SHM systems to be practically deployed as part of an integrated system health management (ISHM), tools must be created for SHM life- cycle management (LCM). To that end, SHM-LCM software has been developed to expedite the adoption of SHM into ISHM. The SHM-LCM software is a flexible application intended to manage the cradle-to-grave life- cycle of an SHM system for generic applications. There are 4 core modules to facilitate critical roles: Optimization, Calibration, Visualization, and Action. The Optimization module seeks to devise optimal sensor placement and excitation parameters in order to achieve probability of detection (POD) coverage requirements. The Calibration module is designed to guide a user through a series of material level tests in order to customize algorithm variables to the system being designed. The Visualization module is dedicated to generating a diagnostic composite picture based on data downloaded from the diagnostic server, which is “stitched” to the original 3D mesh, providing users with a manipulatable GUI to toggle between probability of damage distributions for various calibrated damage modes. Finally, The Action module generates residual performance plots (ultimate load or deflection for example) as a function of probability of damage, so detection confidence can be weighed against impact to the vehicle’s capabilities. SHM- LCM software will enable SHM systems to be incorporated into ISHM by engineers rather than experts, making the technology more accessible, and commercially practical.

How to Cite

S.Kessler, S., B.Flynn, E., T.Dunn, C., & D.Todd, M. (2011). A Structural Health Monitoring Software Tool for Optimization, Diagnostics and Prognostics. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.2016
Abstract 22 | PDF Downloads 13



prognostics, diagnostics, Bayesian, lamb waves, visualization, guided waves, beamforming, phased array

Fasel T. R., Kennel M. B., M. D. Todd, E. H. Clayton, M. Stabb, and G. Park, (2009). “Damage State Evaluation of Experimental and Simulated Bolted Joints Using Chaotic Ultrasonic Waves,” Smart Structures and Systems, vol 5(4), pp. 329-344.

Flynn E. and M. D. Todd (2010). “Optimal Placement of Piezoelectric Actuators and Sensors for Detecting Damage in Plate Structures,” Journal of Intelligent Material Structures and Systems, vol. 21(2), pp. 265- 274.

Holmes C, Drinkwater BW, Wilcox PD (2005). Post- processing of the full matrix of ultrasonic transmit– receive array data for non-destructive evaluation. NDT and E International. vol. 38, pp.701–711.

Kay SM (1998). Fundamentals of Statistical signal processing, Volume 2: Detection theory. Prentice Hall PTR.

Kessler S.S. and P. Agrawal.(2007) "Application of Pattern Recognition for Damage Classification in Composite Laminates." Proceedings of the 6th International Workshop on Structural Health Monitoring, Stanford University.

Kessler S.S. and A. Raghavan (2008). "Vector-Based Localization for Damage Position Identification from a Single SHM Node." Proceedings of the 1st International Workshop on Prognostics & Health Management, Denver, CO.

Kessler S.S. and A. Raghavan (2009). "Vector-based Damage Localization for Anisotropic Composite Laminates." Proceedings of the 7th International Workshop on Structural Health Monitoring, Stanford University
Technical Papers