SmartGuard AI - Predictive Maintenance Multi-Agent System
SmartGuard AI deploys 11 specialized AI agents with access to 18 tools and 12 ML models to perform comprehensive predictive maintenance analysis. The system collects sensor data, syncs with digital twins, detects anomalies, predicts failures, analyzes root causes, optimizes energy consumption, ensures safety compliance, plans maintenance schedules, calculates cost-benefit, and generates multi-view reports.
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Analysis Configuration interface for predictive maintenance with facility and equipment selection.
Multi-Agent Orchestration view with workflow progress and agent reasoning timeline.
Analysis Results Executive View with equipment health metrics and financial impact.
AI-generated Work Order with scheduled maintenance tasks and work instructions.
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex multi-agent predictive maintenance workflows require careful coordination to ensure data flows correctly between specialized agents and that analysis phases execute in proper sequence.
Core Logic
The Orchestrator serves as the master coordinator, validating analysis inputs, initializing agent states, managing the 11-phase workflow execution, and coordinating inter-agent communication. It tracks overall progress, handles errors with recovery actions, and ensures consensus building between agents before finalizing recommendations.
Data Collector Agent
Predictive maintenance requires gathering data from multiple heterogeneous sources including real-time sensors, historical databases, maintenance records, and technical documentation.
Core Logic
This agent uses specialized tools to query sensor data streams, access historian databases for time-series analysis, retrieve relevant documents from the knowledge base, and gather equipment specifications. It normalizes data formats, validates data quality, and prepares unified datasets for downstream analysis agents.
Digital Twin Agent
Understanding equipment behavior requires correlation between real-world sensor readings and virtual models that can simulate operating conditions and predict performance under various scenarios.
Core Logic
The Digital Twin Agent synchronizes physical sensor data with 3D digital twin models, runs thermal simulations, identifies thermal zones and hotspots, and maintains real-time equipment state representation. It calculates predicted wear, thermal efficiency, and overall health scores based on multi-physics simulation results.
Anomaly Detector Agent
Early detection of equipment anomalies is critical for preventing failures, but manual monitoring of hundreds of sensor readings is impractical and expert knowledge is required to identify subtle degradation patterns.
Core Logic
Using ML models trained on historical sensor data, this agent detects point, contextual, and collective anomalies with severity classification. It calculates deviation from expected values, identifies contributing factors, and generates anomaly descriptions with confidence scores. The agent distinguishes between normal operational variations and genuine equipment issues.
Failure Predictor Agent
Knowing that equipment will fail is valuable, but maintenance planning requires predicting when and how equipment will fail to optimize scheduling and parts procurement.
Core Logic
This agent runs predictive ML models to forecast equipment failures, calculating probability scores, confidence intervals, and predicted time-to-failure. It identifies specific failure modes, ranks features by importance, matches against historical failure patterns, and provides risk level assessments to enable proactive maintenance scheduling.
Root Cause Analyzer Agent
Identifying why equipment is degrading or failing requires systematic analysis of multiple data sources, historical incidents, physics-based models, and expert knowledge.
Core Logic
The Root Cause Analyzer performs systematic diagnostic analysis, identifying primary causes with confidence scores and evidence chains from sensor data, historical incidents, documentation, and physics models. It evaluates alternative hypotheses, calculates contributing factor impacts, and generates actionable recommendations to address underlying issues.
Energy Optimizer Agent
Heating systems are major energy consumers, and inefficiencies result in unnecessary costs and carbon emissions. Identifying optimization opportunities requires analysis of consumption patterns, benchmarking, and understanding of control strategies.
Core Logic
This agent calculates energy efficiency metrics including thermal efficiency, power factor, and coefficient of performance. It analyzes carbon footprint across scope 1, 2, and 3 emissions, benchmarks against industry standards, identifies optimization opportunities (scheduling, insulation, control improvements), and calculates payback periods for energy-saving investments.
Safety & Compliance Agent
Industrial heating equipment must comply with numerous safety regulations and certification requirements. Tracking compliance status, identifying gaps, and managing certification renewals is complex and risk-prone.
Core Logic
The Safety & Compliance agent checks regulatory compliance against standards including IEC 60079, ATEX, UL, and CE. It performs hazard assessments with severity and likelihood ratings, tracks certification status and expiration dates, reviews incident history, evaluates emergency readiness, and generates safety recommendations prioritized by risk reduction potential.
Maintenance Planner Agent
Translating predictive insights into actionable maintenance plans requires balancing equipment criticality, resource availability, production schedules, and cost optimization.
Core Logic
This agent creates optimized maintenance recommendations with priority rankings (immediate, urgent, scheduled, opportunistic), optimal execution windows, required parts and skills, estimated durations, and risk assessments if delayed. It generates work orders ready for CMMS integration and coordinates with spare parts inventory to ensure availability.
Cost Analyzer Agent
Justifying maintenance investments requires clear financial analysis comparing proactive maintenance costs against reactive failure costs, including downtime, production losses, and expediting premiums.
Core Logic
The Cost Analyzer performs comprehensive cost-benefit analysis comparing proactive vs reactive maintenance scenarios. It calculates labor, parts, downtime, and production loss costs, computes ROI, NPV, and payback periods, and provides confidence-weighted financial projections that demonstrate the business value of predictive maintenance actions.
Report Generator Agent
Stakeholders at different levels need different views of maintenance analysis - executives need summaries and KPIs, engineers need technical details, and operations needs actionable work plans.
Core Logic
The Report Generator compiles multi-view reports including Executive View (headline, risk level, key findings, financial impact, KPIs), Technical View (equipment analysis, anomalies, predictions, model performance, data quality), and Business View (cost analysis, maintenance recommendations, resource requirements, timeline, compliance status). Reports include complete audit trails for traceability.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization