Predictive Maintenance
“Which machines will fail in the next 7 days?”
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A real-world example
Which machines will fail in the next 7 days?
Unplanned downtime costs manufacturers $50B per year globally. Time-based maintenance over-services healthy equipment and misses early failure modes. Sensor-only models detect anomalies but generate too many false alarms and miss failures caused by interaction effects between equipment, parts, and operating conditions. For a plant with 500 machines, reducing unplanned downtime by 30% saves $8-12M annually.
How KumoRFM solves this
Graph-powered intelligence for manufacturing
Kumo connects equipment, sensors, maintenance logs, parts, and production runs into a factory graph. The GNN learns failure patterns that depend on equipment interactions: when machine A's vibration increase coincides with machine B's temperature drift downstream, and how specific part-equipment-operating condition combinations predict failure. PQL predicts which machines will fail within 7 days, giving maintenance teams time to schedule repairs during planned downtime windows.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
EQUIPMENT
| equipment_id | type | install_date | line |
|---|---|---|---|
| EQ001 | CNC Lathe | 2020-06-15 | Line-A |
| EQ002 | Press Machine | 2018-03-10 | Line-A |
| EQ003 | Conveyor Motor | 2022-01-20 | Line-B |
SENSORS
| sensor_id | equipment_id | metric | latest_value | threshold |
|---|---|---|---|---|
| SEN101 | EQ001 | Vibration (mm/s) | 4.8 | 6.0 |
| SEN102 | EQ001 | Temperature (C) | 72 | 85 |
| SEN103 | EQ002 | Pressure (bar) | 148 | 160 |
MAINTENANCE_LOGS
| log_id | equipment_id | type | description | date |
|---|---|---|---|---|
| ML201 | EQ001 | Preventive | Bearing replacement | 2025-01-15 |
| ML202 | EQ002 | Corrective | Hydraulic seal repair | 2025-02-10 |
| ML203 | EQ003 | Preventive | Belt tension adjust | 2025-02-20 |
PARTS
| part_id | equipment_id | name | age_hours | rated_life_hours |
|---|---|---|---|---|
| PRT301 | EQ001 | Spindle Bearing | 3,200 | 5,000 |
| PRT302 | EQ002 | Hydraulic Seal | 800 | 4,000 |
| PRT303 | EQ003 | Drive Belt | 1,500 | 3,000 |
PRODUCTION_RUNS
| run_id | equipment_id | duration_hours | load_pct | date |
|---|---|---|---|---|
| RUN501 | EQ001 | 12 | 92% | 2025-03-01 |
| RUN502 | EQ002 | 8 | 78% | 2025-03-01 |
| RUN503 | EQ003 | 16 | 95% | 2025-03-01 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT BOOL(MAINTENANCE_LOGS.type = 'Corrective', 0, 7, days) FOR EACH EQUIPMENT.equipment_id
Prediction output
Every entity gets a score, updated continuously
| EQUIPMENT_ID | TYPE | FAILURE_PROB_7D | RISK_TIER |
|---|---|---|---|
| EQ001 | CNC Lathe | 0.68 | High |
| EQ002 | Press Machine | 0.11 | Low |
| EQ003 | Conveyor Motor | 0.42 | Medium |
Understand why
Every prediction includes feature attributions — no black boxes
Equipment EQ001 -- CNC Lathe on Line-A
Predicted: 68% failure probability in next 7 days (High risk)
Top contributing features
Vibration trend (14-day slope)
+32% increase
30% attribution
Spindle bearing age vs rated life
64% consumed
24% attribution
Operating load above 90% for 5+ days
92% avg
20% attribution
Temperature drift correlated with downstream press
+3.5C
15% attribution
Similar equipment failure pattern on Line-B
Failed last month
11% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: A plant with 500 machines saves $8-12M annually by reducing unplanned downtime 30%. Kumo's factory graph detects multi-equipment interaction patterns and part degradation trajectories that sensor-only anomaly detection misses.
Related use cases
Explore more manufacturing use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
