Predictive Maintenance AI

Machine learning system predicting equipment failures 72 hours in advance across multiple production facilities.

Client

Manufacturing Client

Duration

8 months

Year

2024

Team Size

10 engineers

Key Outcomes

Failure risk predictionProactive

Risk scoring and early indicators to support maintenance planning.

Maintenance prioritizationImproved

A practical framework to focus teams on high-impact assets first.

Operational analyticsEnhanced

Asset health views and model outputs integrated into decision workflows.

The Challenge

Turning Noisy Operational Data Into Reliable, Actionable Predictions

Predictive maintenance succeeds only when models are production-ready, monitored for drift, and integrated into maintenance workflows—beyond offline model accuracy.

Data readiness and labeling constraints

Sensor logs may be incomplete/noisy, and failure labels are often sparse.

Production deployment and monitoring

Models require versioning, evaluation, drift monitoring, and retraining strategies.

Operational adoption

Outputs must be interpretable and tied to actions (work orders, inspections, prioritization).

Our Approach

Production-Grade Predictive Maintenance: Data Pipeline, MLOps, and Workflow Integration

Sintesa delivered an end-to-end approach: data audit, feature pipelines, explainable modeling choices, MLOps practices, and integration with maintenance operations.

Data audit and feature engineering pipeline

Quality checks, normalization, feature generation, and reproducible datasets.

Modeling with explainability considerations

Balanced performance and interpretability for operational trust.

MLOps and drift monitoring

Model/version tracking, performance monitoring, and retraining triggers.

Integration into maintenance workflows

Risk-to-action mapping, dashboards, and handoff into work management processes.

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