AI-driven predictive maintenance
Enerwire’s AI Maintenance Intelligence is a cloud-based predictive maintenance solution that ingests historical records and live data from the smart manufacturing stack to forecast failures, classify anomalies, and prescribe optimal interventions. Using machine learning and advanced analytics, the system learns normal asset behavior, detects deviations early, and recommends maintenance actions that minimize unplanned downtime, extend equipment life, and stabilize production. It integrates seamlessly with existing OT/IT systems and scales across lines and plants.
- Category Artificial Intelligence
- Services : Artificial Intelligence
- Clients : Enerwire
- Location : San Salvador
- Date : August 2025
Impact and Benefits
- Industrial IoT (IIoT)
- Edge-to-Cloud Architecture
- Cloud Computing & Big Data
- Advanced Analytics & Machine Learning
- Predictive Maintenance (PdM)
- Interoperability & APIs
- Cybersecurity
Key Features & Functionality
- Data fusion pipeline: Aggregation of sensor, PLC/SCADA, CMMS/ERP, and quality data with time alignment and contextualization.
- ML models for failure prediction: Supervised and unsupervised models for Remaining Useful Life (RUL), anomaly detection, and health scoring.
- Condition monitoring: Real-time feature extraction (temperature, speed, cycles, etc) and trend analytics.
- Prescriptive recommendations: Maintenance playbooks with severity, cause hypotheses, and suggested actions/parts.
- Alerting and workflows: Threshold- and model-driven alerts routed to maintenance and operations with SLA tracking.
- Integration with CMMS/ERP: Automatic work order creation, parts reservation, and feedback loop to retrain models.
- MLOps & governance: Versioned models, performance monitoring, drift detection, and human-in-the-loop review.
- Dashboards & KPIs: MTBF/MTTR, downtime by cause, forecasted risk, and maintenance compliance by asset/line/shift.
Impact and Benefits
At Enerwire, we are developing an AI-based predictive maintenance layer that combines cloud-scale analytics with machine learning models trained on historical and real-time production data. This enables early anomaly detection, precise failure forecasts, and prescriptive interventions that reduce stops, optimize maintenance schedules, and protect critical assets. By unifying high-frequency sensor signals with equipment history and work orders into intuitive dashboards, the system improves operational efficiency, lowers maintenance spend, and strengthens our Industry 4.0 foundation for continuous improvement.
The implementation of this system at Enerwire will deliver significant benefits, including:
- Reduced unplanned downtime through early detection and targeted interventions.
- Lower maintenance costs via optimized scheduling, fewer emergency repairs, and better parts planning.
- Extended asset lifespan and improved stability by operating within optimal windows.
- Higher OEE and throughput from fewer failures and faster recovery.
- Data-driven maintenance culture with closed-loop learning and auditable decisions.