What we build
A monitoring stack that raises the work order — not just the alarm.
Each capability is a production component — not a proof-of-concept — wired into your SCADA and CMMS, documented for your operations team, monitored continuously.
Multi-asset telemetry ingest
SCADA, inverter, BMS, PLC, and OPC UA feeds normalised into one schema — PV, BESS, EV, and wind on a single pipeline. Multi-OEM adapters reused across the fleet.
Anomaly detection on live signals
Autoencoder and residual-analysis models flag drift against each asset's own baseline — output, thermal, and acoustic signals caught before they surface as faults.
Remaining useful life (RUL) prognostics
Survival models fuse anomaly scores with duty cycle and failure history to estimate time-to-failure — so maintenance lands before the outage, not after.
CMMS work-order dispatch
Maximo, UpKeep, ServiceNow, and SAP PM wired directly — work orders raised with the parts list, SLA, and route pre-filled. Field crews arrive with the right kit, first visit.
Revenue + uptime attribution
Every anomaly caught tagged with avoided revenue loss, downtime saved, and MTBF impact — attribution flows to InsightAX so the commercial team sees the bottom line.
Drift monitoring + model feedback
Technician findings written back to the training set every close-out. Population drift, feature drift, and fleet-wide performance tracked — models sharpen monthly, not quarterly.
Asset classes we monitor
One engine, every asset class.
Same pipeline, same anomaly + RUL models, same CMMS write-back — tuned per asset class. PV, BESS, EV, wind, and balance-of-plant all run on the same stack, only the model weights, OEM adapters, and fault-mode libraries change.
Utility-scale PV plants
Central and string inverters, DC combiners, and trackers on one pipeline. Thermal anomalies, output residuals, and PR ratio drift caught across every string and every inverter.
BESS pack + PCS monitoring
Cell-level temperature and voltage imbalance, coolant and contactor health, PCS fault signatures — catching pre-fault signals that defend the warranty envelope.
EV charger fleets
OCPP telemetry, connector wear, power-electronics thermal drift, and session-failure signatures — on-grid availability measured, not assumed, across the charger fleet.
Small-scale wind turbines
Gearbox vibration, pitch-drive health, and yaw-motor current signatures per IEC 61400-25 — prognostics tuned to duty cycles and wind-regime patterns.
Balance-of-plant + aux systems
HVAC, fire panel, auxiliary transformer, and switchgear condition monitoring — often the silent cause of uptime loss, now on the same pipeline as the primary assets.
C&I rooftop fleets
Distributed rooftops across multi-site C&I portfolios — condition-based maintenance replaces fixed-cadence visits, truck-rolls fall, PR ratios climb.
Model families we deploy
No single model covers every fault-mode. So we ensemble.
Anomaly, prognosis, imaging, and dispatch are distinct problems with distinct horizons. Dedicated models per task give you coverage, resilience, and a transparency layer a single tool can't match.
Autoencoder and residual-analysis ensemble catches drift against each asset's own baseline — calibrated per OEM, per duty profile, per site. Thin-file assets covered via fleet-cohort transfer.
Survival-analysis model estimates remaining useful life per fault-mode, fusing anomaly scores with duty cycle and OEM failure history. Confidence bands tagged per prediction.
Convolutional network identifies PV-module hot-spots, BESS pack thermal anomalies, and connection-fault signatures from drone and fixed infrared imagery. Flight-to-work-order in hours.
Agent raises, prioritises, and routes CMMS work orders — parts list, SLA, and crew assignment pre-filled. Approval workflow preserved; technicians confirm close-out in the field app.
Data sources wired into every model
Every signal that moves the decision — integrated.
Pulled in parallel from SCADA, PLC, OPC UA, drone, and CMMS rails — normalised into a single feature schema, versioned alongside the models that consume them.
Explainability, not just alerts
A red light doesn't dispatch a crew. A reasoning trail does.
Every anomaly flagged and every RUL estimate arrives with a reasoning trail — contributing features, baseline comparison, and confidence band — ready for the operator, technician, and insurer that will eventually read it.
- Feature contributions on every anomaly flag
- Confidence band on every RUL estimate
- Model + feature version logged per decision
- Aligned to ISO 55000, IEC 62443, IEC 61850
Why Axccelerate for asset monitoring
Not an alarm clock.
A reliability stack.
A dashboard shows you when something broke. Our stack forecasts it, raises the work order, routes the crew, and writes the feedback back into the model.
Pricing
Priced to the asset fleet, not the ticket count.
Monitoring deployments are custom — we scope against your asset classes, OEMs, and CMMS integrations before quoting.
Glossary
The vocabulary behind every alert.
Quick reference for the acronyms that show up in renewable-asset monitoring — the terms your reliability team, technicians, and insurance reports will all use.
- SCADA
- Supervisory Control and Data Acquisition
Plant-level control and monitoring layer that aggregates sensor data and exposes operator controls. Our historian pulls are almost always SCADA-rooted.
- OPC UA
- Open Platform Communications Unified Architecture
Vendor-neutral industrial protocol for exposing device data. The modern standard for inverters, PLCs, and edge gateways; our preferred ingest where available.
- PLC
- Programmable Logic Controller
Embedded industrial controller running the deterministic logic for site systems — trackers, HVAC, fire panels, switchgear. Source of fast-cadence signals for anomaly models.
- RUL
- Remaining Useful Life
Estimated time-to-failure for a component, expressed in hours, cycles, or calendar-days. Survival models produce RUL with a confidence band per fault-mode.
- MTBF
- Mean Time Between Failures
Average time between asset failures in a fleet — the headline reliability KPI. Our job is to push it up by catching pre-fault signals before they escalate.
- MTTR
- Mean Time To Repair
Average time to restore an asset to service after a failure. Parts pre-staging, skilled-crew routing, and RUL-driven scheduling all pull MTTR down materially.
- CMMS
- Computerised Maintenance Management System
System-of-record for work orders, asset hierarchy, and maintenance history — Maximo, ServiceNow, SAP PM, UpKeep. The authoritative home for every dispatched action.
- Anomaly detection
- Baseline-deviation signal
Machine-learning technique for flagging behaviour that deviates from an asset's learned baseline — catching faults earlier than fixed-threshold rules can.
- PR ratio
- Performance Ratio · PV
Ratio of actual PV output to theoretical output under measured irradiance. The headline KPI for solar-plant health; drift detection on PR surfaces structural issues.
- Curtailment
- Forced output reduction
Grid-operator-requested generation reduction — costly when lost to uncommunicated faults. Our models distinguish curtailment from genuine degradation so reporting stays honest.
- Condition-based maintenance
- CBM · signal-triggered
Maintenance triggered by measured asset condition rather than a fixed calendar interval. Our primary operating mode; truck-rolls drop, uptime rises.
- Drift detection
- Model + data drift
Monitoring for shifts in input-feature distributions (data drift) or model-performance decay (concept drift). Triggers retraining or human review before decisions degrade.
- Digital twin
- Live asset replica
A live synchronised digital representation of a physical asset — inputs, state, and expected outputs — used for simulation, anomaly reasoning, and what-if prognosis.
- Edge inference
- On-device ML execution
Running lightweight model inference on edge gateways where bandwidth or latency rules out cloud round-trips — common on remote wind and rooftop sites.
Your reliability stack, engineered.
30-minute scoping with a senior engineer and a reliability operator. You'll leave with a detection plan, integration sketch, and realistic timeline — not a sales pitch.