System for Detecting Signs of Equipment Anomalies Using Data Science technology J-dscomTM

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System for Detecting Signs of Equipment Anomalies Using Data Science technology J-dscom^TM

Improving production efficiencyImprovement of labor productivity

Data Science

System for Detecting Signs of Equipment Anomalies Using Data Science technology J-dscom^TM

Useful technology/system for detecting signs of anomalies that was developed from the user’s perspective!

Solution Point

Features 01

Features of J-dscom^TM

? Realizes comprehensive and highly accurate monitoring by monitoring level (such as entire process level, facility level, sensor level).

? Realizes efficient, large volume (massive data) monitoring using the heatmap screen as a platform.

? Simplifies and speeds up identification of factors by automatic creation of graphs for variables of interest.

Features 02

Technology highlight

? Entire Process Level

? Monitors breakdown of the correlation between variables in entire process
(Lasso regression).

? Equipment Level PCA waveform monitoring

? Monitors equipment with uniform signal waveforms.

? Detects waveform disturbances when an anomaly occurs by principal component analysis (PCA).

? Equipment Level DBM correlation monitoring

? Monitors equipment in which a correlation exists between variables.

? Can be applied even in cases of large nonlinearity and large variations.

Features 03

Detection example

Transition of anomaly score of variables related to rolled material conveying equipment

Useful technology/system for detecting signs of anomalies that was developed from the user’s perspective!

Links 01

Information related to this technology

Collaborating group companies

Links 02