🏭 Manufacturing · Data Flow
Predictive Maintenance Pipeline
How vibration and temperature data from machines becomes a maintenance work order before the machine fails.
Drawing diagram…
What this diagram shows
- Sensors stream vibration, temperature and current
- ML model estimates remaining useful life
- Predicted failures create work orders in the CMMS
Prompt used
Data flow for predictive maintenance: vibration, temperature and motor current sensors on pumps and motors stream data to an edge gateway that computes features (RMS, FFT peaks), sends them to the cloud, where an ML model estimates failure probability and remaining useful life. High-risk predictions raise an alert to the maintenance planner and create a work order in the CMMS (Maximo); completed work order data is fed back to improve the model.
Mermaid code
flowchart LR S[Vibration, temperature, current sensors] -->|Raw signals| E[Edge Gateway] E -->|Features: RMS, FFT peaks| C[Cloud Ingestion] C --> H[(Sensor History)] H --> M[ML Model - failure probability] M -->|Remaining useful life| D[Maintenance Dashboard] M -->|High risk| A[Alert to Planner] A --> W[CMMS Work Order - Maximo] W -->|Completed work, root cause| L[(Labelled Failures)] L -->|Retraining| M
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