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AI-Assisted Root Cause Analysis
SCADA alarms, protection events, sensor data, maintenance records, operator logs and environmental conditions can all hold part of the explanation. Engineers must reconstruct what happened across multiple systems and distinguish the initiating cause from its consequences often through significant manual analysis.
AI assisted root cause analysis connects event sequences, asset behavior and historical incidents helping engineers move from isolated alarms toward contextual understanding.
Reconstruct alarms, protection activity and operating conditions to reveal how an incident developed over time.
Identify unusual relationships and contributing factors that individual alarms may not expose.
Compare current incidents with similar assets, previous interventions and historical failure patterns.
Feed validated findings into maintenance strategies, procedures and asset knowledge to strengthen future prevention.
How NTT DATA helps
Bring together SCADA, historian data, alarms, protection events, asset condition, maintenance history, inspections and contextual information.
Apply AI, event-sequence analysis and time-series analytics to detect temporal patterns and interactions that are difficult to uncover manually.
Present possible contributing factors together with the operational and historical evidence supporting them not unexplained conclusions.
Incorporate validated findings into maintenance recommendations, operating procedures and evolving libraries of failure mechanisms and corrective actions.
We don’t ask AI to declare the root cause on its own. We accelerate hypothesis generation so engineers can validate findings against equipment and system behavior.
Indicative objectives depend on data availability, asset characteristics, incident complexity and implementation maturity
Reduce root-cause investigation time by 30–50% by accelerating the analysis of events, evidence and contributing factors.
Cut manual event correlation effort by 40–60% by connecting related alarms, operational events and system information more efficiently.
Reduce engineering diagnostic workload by 20–40% by providing clearer context and more focused evidence for incident analysis.
Lower recurring incidents linked to previously identified causes by 10–30% through better reuse of investigation findings and operational knowledge.
Move from detecting failures to understanding why they happen
