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Industrial Equipment Vibration Diagnostics Agent using Claude Agents SDK

Unplanned mechanical breakdown of rotating high-speed packaging equipment causes severe line downtime. Build a diagnostic agent using Anthropic's Claude Agents SDK with extended thinking enabled to analyze high-frequency vibration spectrums and thermography logs. Identify root-cause bearing degradation modes with a 93% diagnostic accuracy rate.

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What you are building

Implement an equipment diagnostic agent using Claude Agents SDK with extended thinking to reason over multi-sensor vibration FFT spectrums.

How work is evaluated

Evaluation

Evaluates accuracy of bearing fault mode detection from spectral peak frequencies.

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

valid_fault_type

Ensure fault_mode matches standard bearing defect classifications

Binary check

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

Dimension 2

diagnostic_accuracy

Ratio of correct fault mode diagnoses across evaluation suite • target: 0.93 • range: 0-1

Binary check

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

Learning goals

  • Utilize Claude Agents SDK with Extended Thinking parameters for step-by-step diagnostic reasoning

  • Analyze fast Fourier transform (FFT) peak distributions alongside bearing geometry specs

  • Output precise fault classification alerts detailing failure stage and recommended maintenance interval

Resources

Dataset notes

Dataset of 30 dynamic FFT vibration spectra collected from motor bearings operating under varied load conditions.

Challenge setup

Evaluates accuracy of bearing fault mode detection from spectral peak frequencies.

Challenge input

JSON containing peak frequencies (Hz) and amplitudes (g)

Claude Agents SDK

Advanced extended thinking capabilities for technical root-cause reasoning.

Evaluated output

JSON detailing root cause fault mode and estimated remaining useful life (RUL)

Evaluation checks
  • Ensure fault_mode matches standard bearing defect classifications
  • Ratio of correct fault mode diagnoses across evaluation suite • target: 0.93 • range: 0-1
Configured evaluation evidence
  • Benchmark: Predictive Maintenance Vibration Suite (PdM-VIB-2025)
  • Diagnostic Accuracy target: 0.93
  • 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
  • Claude Code · Required
  • Anthropic · Optional
Observation
  • Claude Code · Required
Policy Serving
  • Claude 4.1 Opus · Optional

Evaluation contract

  • valid_fault_type · Weight 1
  • diagnostic_accuracy · Weight 1

Metrics

  • diagnostic_accuracy · Target 0.93

Benchmarks

  • Predictive Maintenance Vibration Suite (PdM-VIB-2025)
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON containing peak frequencies (Hz) and amplitudes (g)

    Output: JSON detailing root cause fault mode and estimated remaining useful life (RUL)

    Execution harness: Python

Evaluation tools

  • Claude Agents SDK · Agent Framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Industrial Equipment Vibration Diagnostics Agent using Claude Agents SDK