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Infraestructura inteligente y sistemas conectados
Mantenimiento predictivo
Sistemas de mantenimiento predictivo y basado en condición: análisis de vibraciones, detección de anomalías y modelos de modos de falla conectados a los flujos de mantenimiento que actúan sobre ellos.
Panorama general
Predictive maintenance promises to fix equipment just before it fails. In practice, the hard part is rarely the model. It is collecting the right signals, labeling past failures honestly, earning the trust of reliability engineers and connecting alerts to work orders that actually get scheduled.
We approach predictive maintenance as a reliability program supported by engineering. Starting from critical assets and known failure modes, we build condition monitoring, vibration and process analytics and anomaly detection, then integrate findings with CMMS and EAM systems so insight becomes planned work.
Aplicaciones de negocio
- 01
Rotating equipment health
Analyze vibration spectra, temperature and motor current on pumps, fans, compressors and gearboxes to detect imbalance, misalignment, looseness and bearing wear before they lead to unplanned stops.
- 02
Anomaly detection across process signals
Learn normal multivariate behavior for equipment or process units and flag deviations that single-tag thresholds miss, giving reliability and process engineers early and explainable signals worth investigating.
- 03
Fleet and vehicle component health
Use J1939 fault codes, operating hours and sensor trends to anticipate component issues across vehicle and mobile-equipment fleets, scheduling service work around operational demand rather than fixed intervals.
- 04
Maintenance strategy optimization
Compare time-based, condition-based and predictive approaches by asset class using actual failure and maintenance history, helping reliability teams focus effort where it reduces risk most.
Enfoque de ingeniería
- 01
Prioritize assets by criticality
With reliability engineers, we rank assets by consequence of failure and identify dominant failure modes, drawing on FMEA results or existing reliability studies where they exist.
- 02
Assess data readiness
We review available sensors, historian data, maintenance records and failure labels, identifying gaps such as missing vibration coverage or inconsistent work-order coding.
- 03
Build monitoring before prediction
Condition monitoring with engineer-validated thresholds delivers value early and produces the labeled history that anomaly detection and remaining-life models later depend on.
- 04
Develop explainable models
Models favor interpretable features such as spectral bands, trends and operating context, so engineers understand why an alert fired and can judge whether to act on it.
- 05
Close the loop with maintenance
Alerts create or inform work requests in CMMS or EAM systems, and maintenance outcomes feed back to refine models, thresholds and the asset priority list.
Tecnologías y plataformas
Condition data sources
- Triaxial vibration sensors
- OPC UA
- MQTT
- AVEVA PI System
- SAE J1939
Analytics and machine learning
- Python
- SciPy
- scikit-learn
- PyTorch
- MLflow
Data platform
- TimescaleDB
- InfluxDB
- Apache Kafka
- Databricks
Maintenance systems
- IBM Maximo
- SAP Plant Maintenance
- HxGN EAM
- Fiix
Los nombres de tecnologías son marcas comerciales de sus respectivos propietarios y no implican asociación ni respaldo.
Lo que recibirá
- Asset criticality ranking and failure-mode priorities
- Data readiness assessment for selected asset classes
- Condition monitoring dashboards with validated thresholds
- Anomaly detection models with explanation features
- CMMS or EAM integration for alert-driven work requests
- Model performance review process with reliability engineers
Preguntas frecuentes de los líderes
Do we need years of failure data to start?
No. Many assets have too few recorded failures for purely data-driven prediction. We usually begin with condition monitoring and anomaly detection, which rely on healthy-state data rather than failure examples, and introduce failure-specific models as labeled history accumulates through the program.
Can you guarantee reduced downtime?
We do not promise specific outcomes, because results depend on asset condition, data quality and how maintenance teams act on alerts. What we commit to is a transparent method, models engineers can scrutinize, and measurement of alert accuracy and maintenance response so value is judged on evidence.
Will this replace our reliability engineers?
No. Predictive maintenance gives reliability engineers earlier and broader visibility, but the judgment about whether and when to intervene remains theirs. Our systems are designed to support that judgment with evidence and context, and engineer feedback is how the models improve over time.
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