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Challenge

Predictive Vibration Analysis Chain with LangChain

Vibration spectra from rotating machinery provide early warnings of bearing race fatigue long before thermal spikes occur. In this challenge, developers will construct a LangChain pipeline using LLM chains and prompt templates to translate FFT vibration metrics into standard ISO 10816 motor severity classifications.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build a LangChain processing chain to evaluate ISO 10816 vibration severity and output motor health scores.

Delivery guide

How work is evaluated

Evaluation

Evaluates severity classification accuracy for vibration metric inputs.

Datasets

Challenge datasets

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

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

ISO Severity Matching

Vibration of 7.5 mm/s on Class II motor must yield UNSATISFACTORY

Binary check

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

Dimension 2

Classification Accuracy

Accuracy across 10 test vibration severity boundaries • target: 1 • 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

What you should walk away with

  • Construct LangChain `PromptTemplate` and `LLMChain` or LCEL runnable components

  • Evaluate vibration RMS values (mm/s) against ISO 10816 motor class limits

  • Generate qualitative health indicators (Good, Satisfactory, Unsatisfactory, Unacceptable)

  • Provide maintenance timing recommendations

Resources and assets

Reference links and supporting material

Dataset notes

ISO 10816 vibration standard threshold table and test motor telemetry samples.

Challenge setup

Evaluates severity classification accuracy for vibration metric inputs.

Challenge input

JSON with vibration_rms_mms, motor_class

LangChain

Assigned framework

Evaluated output

JSON with iso_category, recommended_action

Evaluation checks
  • Vibration of 7.5 mm/s on Class II motor must yield UNSATISFACTORY
  • Accuracy across 10 test vibration severity boundaries • target: 1 • range: 0-1
Configured evaluation evidence
  • Benchmark: Standard Classification Benchmark
  • Classification Accuracy target: 1
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.

View technical recipe

Configured tools

Action Space
  • Langchain · Required
  • LangChain · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional

Evaluation contract

  • ISO Severity Matching · Weight 1
  • Classification Accuracy · Weight 1

Metrics

  • Classification Accuracy · Target 1

Benchmarks

  • Standard Classification Benchmark
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with vibration_rms_mms, motor_class

    Output: JSON with iso_category, recommended_action

    Execution harness: Python

Evaluation tools

  • LangChain · Chain orchestration framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Predictive Vibration Analysis Chain with LangChain