Method and system for unsupervised anomaly detection and accountability with majority voting for high-dimensional sensor data

Abstract

One embodiment can provide a system for detecting anomaly for high-dimensional sensor data associated with one or more machines. During operation, the system can obtain sensor data from a set of sensor associated with one or machines, apply data exploration techniques on the sensor data to automatically process sensor data to identify a subset of feature sensors from the available set of feature sensors, apply an unsupervised machine-learning technique to the identified subset of feature sensors and the target sensor to learn a set of pair-wise univariate models, and determine whether and how an anomaly occurs in the operation of the one or more machines based on the set of pair-wise univariate models.