Mantenimiento predictivo y monitorización de estado
Detectar desgaste de rodamientos, degradación de engranajes y filtros obstruidos a partir de datos en tiempo real — a menudo con nada más que un micrófono y procesado de señal.
Reservar llamadaDe reactivo a predictivo
Equipment failures are rarely sudden. Bearings degrade over weeks — their acoustic signature shifts as surface pitting develops. Gearboxes give measurable warnings months before seizure. Filters restrict flow gradually, visible in pressure and acoustic data long before the machine trips.
Reactive maintenance means unplanned downtime, emergency sourcing, and production losses. Scheduled maintenance is wasteful — replacing components that still have service life. Predictive maintenance is neither: act when the data says to, not on a calendar and not in a crisis.
Qué se puede monitorizar
Wear Monitoring — Bearings, Gears & Impellers
Bearing defect frequencies (BPFO, BPFI, BSF, FTF) appear as narrow spectral peaks long before audible noise develops. Gear mesh harmonics shift as tooth wear progresses. Envelope analysis isolates these indicators from broadband background noise — reliably, at signal-to-noise ratios where the human ear hears nothing unusual.
Abnormal Condition Detection — Filters, Pumps & Motors
A clogged fuel or hydraulic filter raises differential pressure and alters the acoustic signature of the fluid path. Pump cavitation produces characteristic broadband noise in the 1–10 kHz range. Motor current signature analysis (MCSA) detects eccentricity, broken rotor bars, and bearing defects from the supply current alone — no mechanical access required.
Enfoque técnico
Signal Acquisition
Low-cost sensors are usually sufficient: a microphone for airborne acoustics, a MEMS accelerometer for structure-borne vibration, or a current clamp for motor signature analysis. Often a single sensor positioned at the housing is enough.
Feature Extraction
FFT, envelope analysis, cepstral analysis, and time-domain metrics — RMS, kurtosis, peak-to-peak — isolate fault-characteristic frequencies and track their amplitude over time against a healthy baseline.
Baseline & Anomaly Detection
Establish a healthy-machine reference, then monitor deviation using threshold rules, statistical process control, or a lightweight anomaly detection model. No cloud infrastructure or specialist hardware required.
Integration & Alerting
Results feed into existing SCADA, PLC HMI, or a standalone dashboard. Configurable alarm thresholds, trend plots, and plain-language fault descriptions — not just a red light.
Por qué funciona
No Expensive Sensors Required
A USB microphone or low-cost MEMS accelerometer costing a few euros can detect bearing faults, gear damage, and filter blockage with the right signal processing. The value is in the analysis, not the hardware.
Scheduled on Your Terms
Act on data, not guesswork. Schedule the replacement during the next planned production window — not during an emergency shutdown at 2 a.m.
Non-Invasive Installation
Sensors are surface-mounted or clamped. No machine modifications, no PLC changes, no production interruption during installation or commissioning.
Interpretable Alarms
Fault-characteristic frequencies have physical meaning. When the system alarms, the data shows exactly which component and why — not just an anomaly score.
Stack tecnológico
Sensor Types
Accelerometers (MEMS or piezoelectric) for structure-borne vibration. Microphones for airborne acoustics. Current transformers (CT clamps) for motor current signature analysis (MCSA). Ultrasonic thickness sensors for corrosion/erosion monitoring. Thermal cameras for hot-spot detection. Oil debris sensors for lubrication system monitoring. In most cases a single well-placed low-cost sensor is sufficient.
Signal Processing
Fast Fourier Transform (FFT) decomposes the vibration spectrum into frequency components. Bearing defect frequencies (BPFO, BPFI, BSF, FTF) are calculated from geometry and appear as predictable spectral peaks. Envelope analysis demodulates high-frequency resonance to isolate impulsive fault energy. Cepstral analysis separates source and transmission path effects in gear diagnostics.
Machine Learning Methods
Anomaly detection: isolation forest, autoencoders, LSTM autoencoders track deviation from a healthy-machine baseline without requiring labelled failure data. Classification models (SVM, random forest, gradient boosted trees) identify fault type once labelled examples exist. Remaining Useful Life (RUL) prediction: survival models, LSTM networks trained on run-to-failure data estimate time to next intervention.
Integration & Alerting
Results fed to existing SCADA / PLC HMI via OPC UA, MQTT, or REST API. Edge deployment (on-site compute) for latency-sensitive applications. Cloud deployment for fleet-wide cross-asset models trained on data from multiple sites. Configurable alarm levels: early warning (trend), advisory (schedule soon), urgent (inspect this week).
Métricas de ROI documentadas
| Metric | Improvement | Source |
|---|---|---|
| Unplanned downtime | −30 to −50% | Deloitte / DOE studies |
| Maintenance costs | −12 to −25% | MarketsandMarkets 2024 |
| Emergency repairs | −60 to −80% | Industry aggregate |
| Overall Equipment Effectiveness (OEE) | +10 to +25% | Grand View Research |
| Spare parts inventory | −30 to −50% | Industry average |
| Payback period | 6–36 months | Process-dependent; high-criticality assets 6–18 mo |
| Adopters reporting positive ROI | 95% | Grand View Research 2024 |
Reactivo vs. Preventivo vs. Predictivo
| Aspect | Reactive | Preventive | Predictive |
|---|---|---|---|
| When action taken | After failure | On fixed schedule | When data indicates need |
| Cost driver | Emergency repairs + downtime | Over-maintenance + wasted parts | Monitoring system investment |
| Downtime | Unplanned, long | Planned, sometimes unnecessary | Planned, minimal |
| Parts replaced | When failed | By time interval | By actual condition |
| Operational risk | High | Medium | Low |
| Typical savings vs reactive | Baseline | 10–25% cost reduction | 25–50% cost reduction |
Patrones de diseño relevantes
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Si sospecha un problema de fiabilidad o quiere configurar la monitorización antes de la próxima parada — una llamada de 30 minutos es suficiente para evaluar qué señales recoger.
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