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Improving production efficiencyImprovement of labor productivity

Data Science

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

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

Solution Point

  • Comprehensive and highly accurate monitoring by monitoring level.
  • Realizes efficient, massive data monitoring.
  • Simplifies and speeds up identification of factors.

Features 01

Features of J-dscomTM

? 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 of J-dscomTM

Features 02

Technology highlight
? Entire Process Level
? Monitors breakdown of the correlation between variables in entire process
(Lasso regression).
(a) Normal relation
(b) Abnormal relation
? Equipment Level PCA waveform monitoring
? Monitors equipment with uniform signal waveforms.
? Detects waveform disturbances when an anomaly occurs by principal component analysis (PCA).
PCA waveform monitoring graph
? 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.
DBM correlation monitoring graph

Features 03

Detection example
Transition of anomaly score of variables related to rolled material conveying equipment
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Useful technology/system for detecting signs of anomalies that was developed from the user’s perspective!
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