Structural Health Monitoring

Structural health monitoring (SHM) is an upcoming technology in civil, mechanical and aerospace engineering. In the last ten to fifteen years, SHM technologies have emerged creating an exciting new field within various branches of engineering. SHM is a new approach to collect data about critical structural elements using sensors to provide indicators when some anomalies are detected in a structure. This approach will continuously update the data in a structure on current conditions of a structure including the detection of changes in chemical and electrical properties of materials related to deterioration, such as corrosion and chloride attack, steel corrosion and fatigue, alkali-silica reaction, and PH, humidity and changes in the service environment or exposure. SHM can also continuously monitor structure physical properties, such as loadings, stresses, strains, accelerations, cracks, etc. SHM refers to the broad concept of assessing the ongoing, in-service performance of structures using a variety of measurement techniques. The sensors include accelerometers, strain gauges, displacement transducers, level sensing stations, anemometers, temperature sensors and dynamic weight-in-motion sensors.

OntoSpace™ – the complexity management system from Ontonix – can be used in a variety of SHM applications, in particular in spacecraft and aircraft. OntoSpace™ process data arranged in rectangular tables, in which the columns represent the variables (or sensors) and the rows correspond to samples gathered at a given frequency. Our advanced and patented model-free algorithm can efficiently process even the most pathological data, with clusters, outliers and bifurcations. Incomplete data records can also be treated, as well as discrete variables. Data must be either in comma separated values (csv) format or as ASCII text files, such as those generated by MATLAB®.

Once OntoSpace™ has processed the data, it generates the so-called Process Maps, such as those illustrated below. These, together with measures of entropy, are used to quantify the complexity of the system in question. An example of a Process Map is illustrated above.

But why is it so interesting to measure complexity and to track it over time? Ontonix has shown that changes in system complexity point to traumatic events that often might be invisible if viewed on a channel-per-channel base. In fact, complexity establishes a single holistic health measure. The idea is as follows. Every dynamical system possesses an upper complexity limit – called critical complexity – close to which the structure of the Process Maps begins to break down. In other words, the system is close to collapsing. The state of health of a system is therefore given by the difference between the current value of complexity and the corresponding critical

complexity. Figure 2 below illustrates the time-history of complexity (orange curve) and of the critical complexity. Variations in complexity, as mentioned, can be related to events taking place within the system.

Figure 2. The concept behind complexity-based SHM. Sudden changes in complexity establish a holistic state-of-health index which takes into account the entire Process Map and entropy in the system. Slow drift in complexity generally indicates a build-up of energy before collapse.