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Pydantic AI Predictive Motor Maintenance Agent with Vast.ai Model Runtime

Vibration and motor temperature telemetry often signal impending Motor Control Center (MCC) failures before physical trips occur. Build a type-safe Python agent using Pydantic AI and run inference via Vast.ai GPU runtimes to process high-frequency motor vibration spectra. Success is achieved by predicting motor failure windows within a 24-hour error margin while outputting strictly validated Pydantic models.

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What you are building

Construct a type-safe Pydantic AI agent executing on GPU infrastructure via Vast.ai to process high-frequency FFT vibration data and predict motor failure windows.

How work is evaluated

Evaluation

Evaluates remaining useful life (RUL) estimation accuracy and structural validity of motor telemetry analysis.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

pydantic_schema_valid

Ensures agent output strictly passes Pydantic BaseModel validation.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

rul_mae_hours

Mean Absolute Error in RUL prediction hours compared to ground truth. • target: 12 • range: 0-100

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Define Pydantic schema models for high-frequency motor vibration FFT spectra.

  • Build a Pydantic AI agent with explicit dependencies and typed output guarantees.

  • Configure cloud GPU deployment on Vast.ai for fast spectral feature inference.

  • Calculate Remaining Useful Life (RUL) estimates based on bearing fault frequencies.

Resources

Dataset notes

Vibration sensor telemetry records including peak acceleration, RMS velocity, and bearing geometry parameters from motor control centers.

Challenge setup

Evaluates remaining useful life (RUL) estimation accuracy and structural validity of motor telemetry analysis.

Challenge input

JSON object with motor_id, peak_acceleration_g, and peak_frequency_hz

Pydantic AI

Required framework for type-safe Python agent development.

Vast.ai

Cloud GPU infrastructure for high-throughput spectral analysis models.

Evaluated output

JSON object with health_status, estimated_rul_hours, and bearing_fault_detected

Evaluation checks
  • Ensures agent output strictly passes Pydantic BaseModel validation.
  • Mean Absolute Error in RUL prediction hours compared to ground truth. • target: 12 • range: 0-100
Configured evaluation evidence
  • Benchmark: PdM-VibrationBench-2026
  • Rul Mae Hours target: 12
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Pydantic AI · Required
  • Vast.ai · Optional
Orchestration
  • Pydantic AI · Required

Evaluation contract

  • pydantic_schema_valid · Weight 1
  • rul_mae_hours · Weight 1

Metrics

  • rul_mae_hours · Target 12

Benchmarks

  • PdM-VibrationBench-2026
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with motor_id, peak_acceleration_g, and peak_frequency_hz

    Output: JSON object with health_status, estimated_rul_hours, and bearing_fault_detected

    Execution harness: Python

Evaluation tools

  • Pydantic AI · Agent Framework
  • Vast.ai · Inference & Model Runtime

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Pydantic AI Predictive Motor Maintenance Agent with Vast.ai Model Runtime