Equipment & infrastructure monitoring
Explore classification of equipment states from prepared sensor and maintenance records. Help port and offshore teams understand which patterns support an inspection category.
NEURYTIKA · CELESTE ECOFLYERS
Give operators useful categories and the evidence behind them. Explore a Neurytika pilot for maritime infrastructure, equipment health and environmental monitoring.
Explore an Explainable AI Pilot ↗See the 60-second example →Explore classification of equipment states from prepared sensor and maintenance records. Help port and offshore teams understand which patterns support an inspection category.
Evaluate categories defined by environmental specialists using structured observation features and historical labels. Review atypical measurements and the evidence behind each classification.
Scope an integration using prepared observations from aerial, maritime or land-based sensors. Agree how records are aligned, classified and presented for an operator’s review.
EXAMPLE PILOT · MARITIME EQUIPMENT HEALTH
Prepare historical temperature, vibration and signal-quality summaries with known equipment-state labels. Compare ERC with a relevant baseline on reserved records, then review the feature combinations supporting the proposed maintenance categories with your engineers.
Celeste EcoFlyers connects aerospace engineering with explainable AI research. Our development direction is to bring inspectable classification into onboard monitoring and broader cognitive architectures. An embedded pilot scopes the target computer, data preparation, interfaces and measured resource use.
Neurytika is developed by Celeste EcoFlyers in France. Its Evidence Ratio Classifier (ERC) represents relationships between discrete feature combinations and target classes, making their statistical support available for inspection. The research is published in Neurocomputing.