Structural Health Monitoring - The Fundamental Axioms of SHM

The Fundamental Axioms of SHM

Based on the extensive literature that has developed on SHM over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or general principles, have emerged. The axioms are listed as follows:

• Axiom I: All materials have inherent flaws or defects;
• Axiom II: The assessment of damage requires a comparison between two system states;
• Axiom III: Identifying the existence and location of damage can be done in an unsupervised learning mode, but identifying the type of damage present and the damage severity can generally only be done in a supervised learning mode;
• Axiom IVa: Sensors cannot measure damage. Feature extraction through signal processing and statistical classification is necessary to convert sensor data into damage information;
• Axiom IVb: Without intelligent feature extraction, the more sensitive a measurement is to damage, the more sensitive it is to changing operational and environmental conditions;
• Axiom V: The length- and time-scales associated with damage initiation and evolution dictate the required properties of the SHM sensing system;
• Axiom VI: There is a trade-off between the sensitivity to damage of an algorithm and its noise rejection capability;
• Axiom VII: The size of damage that can be detected from changes in system dynamics is inversely proportional to the frequency range of excitation.