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An anomaly detection approach for multiple monitoring data series based on latent correlation probabilistic model

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Abstract

Condition monitoring systems are widely used to monitor the working condition of equipment, generating a vast amount and variety of monitoring data in the process. The main task of surveillance focuses on detecting anomalies in these routinely collected monitoring data, intended to help detect possible faults in the equipment. However, with the rapid increase in the volume of monitoring data, it is a nontrivial task to scan all the monitoring data to detect anomalies. In this paper, we propose an approach called latent correlation-based anomaly detection (LCAD) that efficiently and effectively detects potential anomalies from a large number of correlative isomerous monitoring data series. Instead of focusing on one or more isomorphic monitoring data series, LCAD identifies anomalies by modeling the latent correlation among multiple correlative isomerous monitoring data series, using a probabilistic distribution model called the latent correlation probabilistic model, which helps to detect anomalies according to their relations with the model. Experimental results on real-world data sets show that when dealing with a large number of correlative isomerous monitoring data series, LCAD yields better performances than existing anomaly detection approaches.

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Notes

  1. KOMTRAX: http://www.komatsuamerica.com/komtrax

  2. IEM: http://www.sanygroup.com/group/en-us/

  3. The response time delay is a physical property, determined by the manufacturing of the equipment. The response time delay can be shortened by improving the manufacturing, which is beyond the scope of this paper. In this paper, we make the reasonable assumption that most sensors of the same type have the same response time.

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Author information

Author notes

  1. Jianwei Ding, Yingbo Liu, Li Zhang & Jianmin Wang

    Present address: East Main Building, School of Software, Tsinghua University, 100084, Beijing, China

Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Jianwei Ding

  2. Institute of Information System & Engineering, School of Software, Tsinghua University, Beijing, China

    Jianwei Ding, Yingbo Liu, Li Zhang & Jianmin Wang

  3. General Research Institute of SANY Group, Changsha, China

    Yonghong Liu

Authors
  1. Jianwei Ding
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  2. Yingbo Liu
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  4. Jianmin Wang
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Correspondence to Jianwei Ding.

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Ding, J., Liu, Y., Zhang, L. et al. An anomaly detection approach for multiple monitoring data series based on latent correlation probabilistic model. Appl Intell 44, 340–361 (2016). https://doi.org/10.1007/s10489-015-0713-7

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