Predictive Maintenance Digital Worker
Deploys a multi-agent AI system that continuously analyzes sensor data, predicts failures using Weibull reliability models, performs root cause diagnostics via FFT analysis, matches against historical failure patterns, and generates optimized maintenance schedules with ROI calculations—all coordinated through consensus-based decision making..
Problem Statement
The challenge addressed
Solution Architecture
AI orchestration approach
Robot selection and configuration interface with live sensor readings showing critical anomalies in vibration, temperature, and power consumption
Multi-agent orchestration dashboard tracking real-time analysis progress with six specialized AI agents collaborating on predictive maintenance
Comprehensive failure prediction results showing 87% failure probability in 6 days with maintenance recommendations and $116,800 potential savings
Agent consensus view displaying unanimous agreement among all specialist agents for immediate preventive bearing replacement
AI Agents
Specialized autonomous agents working in coordination
Orchestrator Agent
Complex multi-step predictive maintenance analysis requires coordinating multiple specialized AI capabilities in the correct sequence while aggregating their outputs into coherent, actionable recommendations.
Core Logic
Implements a Saga pattern workflow coordinator that plans analysis sequences, delegates tasks to specialist agents, manages inter-agent communication, aggregates results, and ensures consensus before finalizing recommendations. Uses O(n) complexity task planning with dependency resolution and rollback capabilities for failed analysis steps.
Sensor Analysis Agent
Raw sensor data streams contain noise and require sophisticated statistical analysis to distinguish genuine anomalies from normal operational variations across thousands of data points.
Core Logic
Applies dual-method anomaly detection using Z-Score statistical control (3-sigma threshold per ISO 13374-2) and Isolation Forest unsupervised learning. Processes 2,847+ data points per cycle with O(n log n) complexity, identifying temperature spikes, vibration anomalies, and pressure deviations with severity classification.
Predictive Engine Agent
Maintenance teams need accurate failure probability forecasts and time-to-failure estimates to schedule interventions before equipment fails but not so early that useful component life is wasted.
Core Logic
Executes Weibull reliability analysis (ISO 12489 compliant) with Maximum Likelihood Estimation to calculate failure probability curves. Combines with Holt-Winters time-series forecasting and Monte Carlo simulation (10,000 iterations) for degradation prediction. Outputs probability percentages (e.g., 87% failure within 7 days) with confidence intervals.
Diagnostic Expert Agent
Identifying the root cause of equipment degradation requires specialized domain expertise in vibration analysis, bearing diagnostics, and failure mode identification that correlates symptoms to specific mechanical issues.
Core Logic
Performs FFT (Fast Fourier Transform) vibration analysis per ISO 10816-3 standards to detect characteristic frequencies indicating bearing defects (BPFO, BPFI, BSF, FTF). Applies bearing defect detection algorithms and failure mode classification using ISO 15243 taxonomy. Identifies root causes like inner race wear with contributing factors.
Knowledge Base Agent
Historical maintenance records contain valuable lessons learned from past failures, but manually searching thousands of cases to find relevant precedents is impractical during time-critical analysis.
Core Logic
Implements vector similarity search using cosine similarity (O(log n) complexity, IEEE 802 compliant) against a corpus of 2,341+ historical failure cases. Retrieves top-k matching cases with similarity scores, extracts proven solutions, calculates success rates from historical outcomes, and surfaces relevant maintenance lessons.
Maintenance Planner Agent
Translating failure predictions into actionable maintenance plans requires balancing technician availability, spare parts inventory, production schedules, and cost optimization while meeting safety constraints.
Core Logic
Uses constraint satisfaction algorithms to find optimal maintenance windows that minimize production impact. Checks real-time parts inventory via API, assigns qualified technicians based on skills matrix, calculates comprehensive ROI including downtime avoidance and labor costs. Generates prioritized action timelines with 5+ scheduled maintenance events.
Worker Overview
Technical specifications, architecture, and interface preview
System Overview
Technical documentation
Tech Stack
5 technologies
Architecture Diagram
System flow visualization