Intelligent Fault Diagnosis & Auto-Resolution System
Multi-agent AI system ingests SCADA alarms, applies ML classification, performs root cause analysis, attempts remote resolution via SCADA commands, and dispatches technicians when needed..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
AI Agents
Specialized autonomous agents working in coordination
Workflow Orchestrator Agent
Complex fault diagnosis requires coordinated task execution with proper sequencing and error handling.
Core Logic
Central coordinator managing workflow phases, delegating to specialized agents, tracking progress, handling failures with retry logic, enforcing authorization, and generating executive summaries with circuit breaker patterns.
Alarm Intelligence & Classification Agent
Raw SCADA alarms contain duplicates and noise causing alarm fatigue without prioritization.
Core Logic
Ingests alarms, applies ML deduplication, correlates with weather, classifies severity using XGBoost, calculates financial impact, and outputs prioritized list with confidence scores.
Root Cause Diagnostic Engine Agent
Identifying root causes requires deep sensor analysis and pattern recognition beyond manual capability.
Core Logic
Retrieves sensor history, runs Isolation Forest anomaly detection, extracts diagnostic features, classifies root cause via XGBoost, searches vector database for similar cases, generates resolution recommendations with probability estimates.
Remote Resolution Executor Agent
Remote-fixable faults require safety verification, authorization, and result validation before SCADA execution.
Core Logic
Performs safety assessment (personnel, equipment, grid, regulatory), requests authorization, executes SCADA commands (reset, parameter adjustment), monitors status with retry logic, verifies resolution, escalates to dispatch if needed.
Field Dispatch Optimization Agent
Field intervention requires optimal technician selection, route planning, and parts availability coordination.
Core Logic
Queries technicians for skill match and availability, calculates optimal routes with traffic data, reserves parts from nearest warehouse, creates detailed work orders with diagnostics and safety procedures, dispatches with ETA tracking.
Continuous Learning Engine Agent
AI models require continuous improvement from resolution outcomes to prevent accuracy degradation.
Core Logic
Collects ground truth from work orders (root cause, resolution, time), updates XGBoost with incremental learning, adds cases to vector knowledge base, identifies new failure patterns, reports accuracy improvements.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
1 technologies
Architecture Diagram
System flow visualization