Data-Driven Anomaly Detection Based on Multi-Sensor Data Fusion

Abstract

In the age of IoT, a huge amount of real time data is produced every second from the colossal number and different types of sensors deployed. A generic and intelligent method to monitor these large data streams from a wide range of sources without human supervision or the use of expert knowledge is a big challenge. In this paper we propose, develop, and test a generic method for anomaly detection which is completely data-driven without human supervision. The proposed method is able to detect the underlying correlations amongst multiple sensors and detect the data patterns from all correlated sensor data through time. Anomalies are detected from marginal deviations from the normal identified patterns. The proposed method is applied to Building Management System’s data which include various types of sensors and proves the generality of the proposed method.