AI Predictive Maintenance System

Core Logic

Serves as master coordinator using GPT-4 to manage the entire predictive maintenance workflow. Initializes analysis sessions, coordinates data flow between specialized agents, manages workflow state transitions, handles exceptions and re-routing, and compiles final recommendations. Maintains full observability with token usage, latency tracking, and cost accounting.

Core Logic

Collects and normalizes telemetry data using GPT-3.5 Turbo for efficient processing. Aggregates data from IoT sensors, edge devices, and cloud APIs. Validates data quality, handles missing values through interpolation, and structures telemetry snapshots for downstream analysis.

Core Logic

Applies machine learning anomaly detection algorithms to identify deviations from expected telemetry patterns. Uses isolation forest and statistical methods to detect anomalies across multiple metrics simultaneously. Calculates deviation severity, assigns confidence scores, and filters false positives through contextual analysis with adaptive thresholds.

Core Logic

Identifies failure signature patterns using GPT-4 with ML augmentation. Maintains a library of known failure patterns from historical incidents, calculates pattern match scores against current data, correlates with component-specific failure modes, and provides context from similar historical cases including typical outcomes and time-to-failure distributions.

Core Logic

Calculates failure probability and time-to-failure using GPT-4 with ML models. Identifies specific components at risk, predicts failure modes with confidence intervals, estimates remaining useful life, and provides feature importance analysis explaining which telemetry indicators drove the prediction.

Core Logic

Diagnoses specific failing components using GPT-4 for reasoning. Traces diagnostic paths through component relationships, identifies primary causes versus contributing factors, calculates evidence strength for each hypothesis, recommends confirmatory tests, and references similar incidents for validation.

Core Logic

Optimizes service timing using GPT-4 for constraint satisfaction. Analyzes laboratory operational schedules, identifies low-impact maintenance windows, coordinates with parts availability and service engineer schedules, and generates maintenance plans that minimize specimen processing disruption while addressing predicted failures before they occur.

Core Logic

Calculates comprehensive cost-benefit analysis using GPT-4. Estimates downtime costs based on specimen volume and turnaround requirements, calculates maintenance intervention costs including parts and labor, computes prevented failure costs, and demonstrates value of predictive versus reactive approaches.

Core Logic

Maintains synchronized virtual instrument models for simulation. Enables what-if scenario analysis comparing different intervention strategies, validates predictions against simulated outcomes, and optimizes maintenance windows through virtual testing.

Core Logic

Monitors quality metrics and SPC charts for early degradation signals. Applies Westgard rules for out-of-control detection, tracks process capability indices (Cp, Cpk), identifies quality trends before control limit breaches, and correlates quality changes with predicted component issues.

Core Logic

Ensures regulatory compliance across FDA 21 CFR Part 11, HIPAA, ISO 13485, and ISO 17025 frameworks. Validates that maintenance recommendations include required documentation, tracks certification expiration dates, identifies compliance gaps requiring remediation, and generates audit-ready compliance packages.

Core Logic

Provides low-latency local AI inference at the instrument level. Processes telemetry streams in real-time, makes immediate escalation decisions for critical anomalies, operates autonomously during network outages, and monitors model drift to trigger retraining when needed.