All materials have inherent flaws or defects.
- ucsdshm@gmail.com
- (858) 534-5993
What is Structural Health Monitoring?
The Fundamental Axioms of SHM*
The assessment of damage requires a comparison between two system states.
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.
Sensors cannot measure damage. Feature extraction through signal processing and statistical classification is necessary to convert sensor data into damage information.
Without intelligent feature extraction, the more sensitive a measurement is to damage, the more sensitive it is to changing operational and environmental conditions.
The length- and time-scales associated with damage initiation and evolution dictate the required properties of the SHM sensing system.
There is a trade-off between the sensitivity to damage of an algorithm and its noise rejection capability.
The size of damage that can be detected from changes in system dynamics is inversely proportional to the frequency range of excitation.
*Worden, Keith; Charles R. Farrar, Graeme Manson and Gyuhae Park (2007). "The Fundamental Axioms of Structural Health Monitoring". Philosophical Transactions of the Royal Society A 463 (2082): 1639–1664.
Structural health monitoring (SHM) is the general process of making an assessment, based on appropriate analyses of in-situ measured data, about the current ability of a structural component or system to perform its intended design function(s) successfully.
Damage prognosis (DP) extends this process by considering how the SHM assessment, when combined with probabilistic future loading and failure mode models with relevant sources of uncertainty adequately quantified, may be used to forecast remaining useful life (RUL) or similar performance-level variables in a way that facilitates efficient life cycle management of the structure or system. A successful SHM/DP strategy may enable significant ownership cost reduction through maintenance optimization, performance maximization during operation, and unscheduled downtime minimization, and/or enable significant life safety advantage through catastrophic failure mitigation.
Consequently, SHM/DP is a very multi-disciplinary field, as any SHM/DP strategy inevitably must include elements of data acquisition/sensing, data mining and feature extraction from acquired data, and statistical modeling and classification of features weighed by costs in a way that facilitates risk-informed decision-making. As such, SHM/DP research involves structural engineering, mechanical engineering, electrical engineering, computer science, statistical science, and even economics and policy.
UC San Diego has a very active leading research group in SHM/DP strategies for a number of applications. A broad sampling of the current projects may be found on this website at Project Archive. Through a unique partnership with Los Alamos National Laboratory called the Engineering Institute, UC San Diego has created the country’s only research-driven specialized graduate degree programs in SHM, DP, and validated simulations.
For more information, visit the website of the Los Alamos National Laboratory Engineering Institute.