A fleet of 10,000 smart meters. One failure means a month of lost data. A technician visit costs are determined per project, but 70% of calls are false alarms. The classic approach—scheduled maintenance or reactive response—is inefficient: either you miss failures or waste resources. We built an ML system that predicts failures 30 days in advance with 88% recall and reduces unplanned downtime by 30%. Over 5 years, we have implemented over 50 projects in predictive maintenance for IoT—from industrial sensors to medical equipment. The system processes telemetry in real time, detects anomalies, and generates a risk score for each device. The dispatch algorithm optimally allocates resources, reducing technician visits by 40%.
How AI Solves the Class Imbalance Problem
Failures account for 0.1–2% of observations. Standard logistic regression gives recall < 30%. We use LightGBM with class_weight='balanced' and population training: first train a global model on the entire fleet, then fine-tune per device. This yields 88% recall even on rare failures. For LSTM RUL, which requires more data, we apply synthetic failure augmentation (SMOTE).
How We Build the Predictive Maintenance Pipeline
Device → MQTT Broker (EMQX) → Kafka → InfluxDB/TimescaleDB ↓ ML Pipeline (Kubeflow) ↓ Model Registry (MLflow) ↓ Triton Inference Server Stack: MQTT for telemetry collection, Kafka for buffering, InfluxDB for time-series storage. ML pipeline on Kubeflow, experiments logged in MLflow. Inference via Triton Inference Server with ONNX Runtime. For edge devices—quantized ONNX Runtime (4x model size reduction with <1% accuracy loss).
| Criteria | LightGBM (binary) | LSTM RUL |
|---|---|---|
| Failure prediction accuracy | 88% recall | 82% recall |
| Inference time on CPU | 0.3 ms | 1.2 ms |
| Required data | 100+ failures per type | 500+ failures, sequences |
| Interpretability | SHAP, feature importance | Attention weights |
LightGBM is easier to deploy (4 weeks). LSTM for critical systems where RUL accuracy matters.
Why LightGBM Wins on Speed and Interpretability
For tasks with 0.1% failures, LightGBM delivers stable 88% recall without complex tuning. Inference on CPU takes 0.3 ms—enabling predictions on every telemetry message in real time. Unlike LSTM, LightGBM is easily interpretable via SHAP: you see which features drive failure risk (temperature, vibration, reboot count).
How Federated Learning Protects Data
For medical devices with HIPAA/GDPR requirements, raw data cannot be transmitted. Federated learning trains local models on each device, sending only weight updates to a central server. This eliminates private data leakage.
Work Process: From Audit to Monitoring
| Phase | Duration | Result |
|---|---|---|
| Discovery | 1-2 weeks | Fleet audit, data collection, metric definition (MAE RUL, ROC-AUC) |
| Data pipeline | 2-3 weeks | MQTT → Kafka → InfluxDB, cleaning, deduplication, schema unification |
| Model training | 2-4 weeks | Experiments in MLflow, model selection (LightGBM/LSTM), cross-validation |
| Deployment | 1-2 weeks | ONNX/Triton, Grafana dashboard with risk scores, OTA update mechanism |
| Monitoring | 2 weeks | Data drift, alerting, automatic retraining on distribution shift |
What's Included in Deliverables
- Architecture and pipeline documentation.
- Trained model in ONNX or TorchScript format.
- Grafana dashboard: risk scores, inference history, alerting.
- Repository with code and configs for reproducibility.
- OTA model update instructions for devices.
- Two-day workshop for your team.
- 6-month warranty on production model (support and refinement).
Timelines and Cost
Timelines range from 4 weeks (basic classifier) to 4 months (full LSTM + dispatch + OTA). Cost is calculated individually—depends on number of devices, types, and required accuracy. Get a consultation—we will send an estimate and timeline for your project. Maintenance savings can amount to significant reductions per year per thousand devices. Order a pilot project—we'll deploy an MVP on your fleet in 2 weeks.
Why Choose Us?
6-month warranty on production model. MLOps certifications (Kubeflow, MLflow). Experience with LSTM and LightGBM in production. Average reduction of unplanned downtime: 30%.