Long term SHM outputs periodically updated information regarding the ability of the structure to continue performing its intended function.
[1] The SHM process involves selecting the excitation methods, the sensor types, number and locations, and the data acquisition/storage/transmittal hardware commonly called health and usage monitoring systems.
To directly monitor the state of a system it is necessary to identify features in the acquired data that allows one to distinguish between the undamaged and damaged structure.
Damage accumulation testing, during which significant structural components of the system under study are degraded by subjecting them to realistic loading conditions, can also be used to identify appropriate features.
Since the beginning of the 19th century, railroad wheel-tappers have used the sound of a hammer striking the train wheel to evaluate if damage was present.
Several fundamental axioms, or general principles, have emerged:[6] SHM System's elements typically include: An example of this technology is embedding sensors in structures like bridges and aircraft.
The area of the SHM process that receives the most attention in the technical literature is the identification of data features that allows one to distinguish between the undamaged and damaged structure.
Damage accumulation testing, during which significant structural components of the system under study are degraded by subjecting them to realistic loading conditions, can also be used to identify appropriate features.
The operational implementation and diagnostic measurement technologies needed to perform SHM produce more data than traditional uses of structural dynamics information.
To further aid in the extraction and recording of quality data needed to perform SHM, the statistical significance of the features should be characterized and used in the condensation process.
The portion of the SHM process that has received the least attention in the technical literature is the development of statistical models for discrimination between features from the undamaged and damaged structures.
Statistical model development is concerned with the implementation of the algorithms that operate on the extracted features to quantify the damage state of the structure.
When data are available from both the undamaged and damaged structure, the statistical pattern recognition algorithms fall into the general classification category, commonly referred to as supervised learning.