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Mathematical Problems in Engineering
Volume 2015, Article ID 693450, 13 pages
Research Article

Modeling the Process of Event Sequence Data Generated for Working Condition Diagnosis

1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
2Institute of Information System & Engineering, School of Software, Tsinghua University, Beijing 100084, China
3School of Software, Tsinghua University, East Main Building, Beijing 100084, China

Received 1 June 2015; Accepted 5 July 2015

Academic Editor: Xiaoyu Song

Copyright © 2015 Jianwei Ding et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of telemetry data in the process. The main task of surveillance focuses on analyzing these routinely collected telemetry data to help analyze the working condition in the equipment. However, with the rapid increase in the volume of telemetry data, it is a nontrivial task to analyze all the telemetry data to understand the working condition of the equipment without any a priori knowledge. In this paper, we proposed a probabilistic generative model called working condition model (WCM), which is capable of simulating the process of event sequence data generated and depicting the working condition of equipment at runtime. With the help of WCM, we are able to analyze how the event sequence data behave in different working modes and meanwhile to detect the working mode of an event sequence (working condition diagnosis). Furthermore, we have applied WCM to illustrative applications like automated detection of an anomalous event sequence for the runtime of equipment. Our experimental results on the real data sets demonstrate the effectiveness of the model.