Data Stream Classification for Structural Health Monitoring via On-Line Support Vector Machines

An important objective of building monitoring is to diagnose the building states and evaluate possible damage. This is a data classification problem. The building states come from many on-line sensors. Normal classification methods, such as support vector machine (SVM), cannot classify this large data stream. In this paper, the classical SVM is extended to an on-line classifier (OLSVM). This SVM can classify large data stream directly. It is applied for on-line structural health monitoring. The experiment results of a lab scale prototype show the proposed algorithm can detect the damage with the data stream. This method can also be applied to big data classification, when the data set are transformed into a data stream.