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Scalable Industry 4.0 Predictive Maintenance System with LangChain

Scaling Industry 4.0 pilot projects across multi-site manufacturing plants fails due to fragmented sensor schemas and rigid ML pipelines. Build a scalable predictive maintenance orchestration agent using LangChain and LangGraph to standardise vibration logs and achieve under 5% false alarm rates.

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What you are building

Develop an enterprise-wide predictive maintenance coordinator using LangChain stateful agents and custom toolchains.

How work is evaluated

Evaluation

Evaluates accuracy of bearing health stage classification and work order triggers across diverse plant telemetry.

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

work_order_flag

Verifies work order is correctly triggered when vibration exceeds threshold.

Binary check

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

Dimension 2

rul_error_mae

Mean Absolute Error of estimated RUL days • target: 1.5 • range: 0-10

Binary check

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

Learning goals

  • Build a LangChain custom tool that ingests vibration RMS and peak acceleration data

  • Implement LangGraph state graph for tracking bearing health degradation stages

  • Standardize disparate plant data formats (e.g., US, Germany, South Korea facilities)

  • Generate automated SAP PM work orders when remaining useful life (RUL) falls below threshold

Resources

Dataset notes

Vibration time-series datasets from motor drives across European and Asian manufacturing sites.

Challenge setup

Evaluates accuracy of bearing health stage classification and work order triggers across diverse plant telemetry.

Challenge input

JSON object with plant_id, equipment_id, rms_vibration_mm_s, peak_accel_g

LangChain

Framework for building stateful LLM chains and tools

Evaluated output

JSON object with health_status (string), rul_days (int), generate_work_order (boolean)

Evaluation checks
  • Verifies work order is correctly triggered when vibration exceeds threshold.
  • Mean Absolute Error of estimated RUL days • target: 1.5 • range: 0-10
Configured evaluation evidence
  • Benchmark: PdM-Scale-Bench-2025
  • Rul Error Mae target: 1.5
  • 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
  • Langchain · Required
  • LangChain · Optional
  • LangGraph · Optional
Orchestration
  • Langchain · Required
  • LangChain · Optional
  • LangGraph · Optional

Evaluation contract

  • work_order_flag · Weight 1
  • rul_error_mae · Weight 1

Metrics

  • rul_error_mae · Target 1.5

Benchmarks

  • PdM-Scale-Bench-2025
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with plant_id, equipment_id, rms_vibration_mm_s, peak_accel_g

    Output: JSON object with health_status (string), rul_days (int), generate_work_order (boolean)

    Execution harness: Python

Evaluation tools

  • LangChain · agent_framework

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Scalable Industry 4.0 Predictive Maintenance System with LangChain