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.
| Original language | English |
|---|---|
| Pages (from-to) | S227-S239 |
| Journal | Total Quality Management and Business Excellence |
| Volume | 30 |
| Issue number | sup1 |
| DOIs | |
| Publication status | Published - 2019 Sep 27 |
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Keywords
- auxiliary variables
- expectation maximisation algorithm
- Gaussian mixture model
- latent class model
- statistical anomaly detection
ASJC Scopus subject areas
- Business, Management and Accounting(all)
Cite this
Conditional anomaly detection based on a latent class model. / Ohkubo, Masato; Nagata, Yasushi.
In: Total Quality Management and Business Excellence, Vol. 30, No. sup1, 27.09.2019, p. S227-S239.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Conditional anomaly detection based on a latent class model
AU - Ohkubo, Masato
AU - Nagata, Yasushi
PY - 2019/9/27
Y1 - 2019/9/27
N2 - 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.
AB - 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.
KW - auxiliary variables
KW - expectation maximisation algorithm
KW - Gaussian mixture model
KW - latent class model
KW - statistical anomaly detection
UR - http://www.scopus.com/inward/record.url?scp=85073541125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073541125&partnerID=8YFLogxK
U2 - 10.1080/14783363.2019.1665847
DO - 10.1080/14783363.2019.1665847
M3 - Article
AN - SCOPUS:85073541125
VL - 30
SP - S227-S239
JO - Total Quality Management and Business Excellence
JF - Total Quality Management and Business Excellence
SN - 1478-3363
IS - sup1
ER -