Conditional anomaly detection based on a latent class model

Abstract

In recent years, the progress of the Internet of Things has promoted data utilisation in manufacturing industries and has created new possibilities for monitoring the condition of production equipment. By applying anomaly detection procedures to the data acquired from sensors, it is possible to capture early signs of occurring anomalies, which leads to improvement of operating rates and prevention of accidents. However, in conventional anomaly detection procedures, it is not always possible to properly detect anomalies when usage situations change. This is because the definition of anomalies changes depending on the usage situation. In other words, when ‘environment variables’ indicating usage conditions and ‘monitoring variables’ indicating monitoring targets exist, it is necessary to regard them as a conditional anomaly detection problem, which is a problem of detecting anomalies occurring in a monitoring variable on the condition that an environmental variable has occurred. In this paper, we propose a novel analysis procedure to solve such conditional anomaly detection problems. In particular, we propose a conditional anomaly detection procedure when categorical environmental variables and continuous monitoring variables are observed. Through Monte Carlo simulation, we show that the proposed procedure can accurately detect ‘conditional anomalies’ that cannot be detected by conventional procedures.

Keywords:

• auxiliary variables
• expectation maximisation algorithm
• Gaussian mixture model
• latent class model
• statistical anomaly detection

Acknowledgements

The authors are thankful to Professors Jens J. Dahlgaard and Su Mi Dahlgaard-Park for their comments on this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Japan Society for the JSPS KAKENHI Grant Numbers JP18K11202 and JP18K13953.