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.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an enterprise-wide predictive maintenance coordinator using LangChain stateful agents and custom toolchains.
How work is evaluated
Evaluates accuracy of bearing health stage classification and work order triggers across diverse plant telemetry.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
work_order_flag
Verifies work order is correctly triggered when vibration exceeds threshold.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
rul_error_mae
Mean Absolute Error of estimated RUL days • target: 1.5 • range: 0-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
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)
- Verifies work order is correctly triggered when vibration exceeds threshold.
- Mean Absolute Error of estimated RUL days • target: 1.5 • range: 0-10
- Benchmark: PdM-Scale-Bench-2025
- Rul Error Mae target: 1.5
- 1 public reference case
- 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
- Langchain · Required
- LangChain · Optional
- LangGraph · Optional
- 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
- 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.