CMMS for Solar & Wind
Keep distributed renewable assets producing at peak.
Solar and wind sites are built from equipment that fails in distinct, well-documented ways — inverter IGBT failures, gearbox wear, string underperformance — and the discipline that catches these early is the same asset-hierarchy and work-order rigour behind any CMMS, applied to panels, strings, inverters, trackers, and turbine components as individually tracked assets under a plant > area > line > functional-location structure.
Field data capture without forcing logins
Renewable sites are geographically distributed and often staffed by contractors or rotating technicians who aren't system users. AssetAI handles this by letting a field tech scan the asset QR code, enter their name and a company PIN, and log a fault — no app install, no credentials to manage. This matters when:
- A tracker fault is found by a visiting O&M contractor, not a plant employee.
- Connectivity is poor at the site — the platform ships as an installable PWA with offline breakdown capture, so the report is saved locally and syncs once a signal is available.
- Severity matters more than paperwork — marking a breakdown as emergency skips approval and raises the work order immediately, while a machine-stopped flag starts the downtime clock at the moment it's reported.
None of this depends on pulling data from SCADA, inverter monitoring platforms, or turbine controllers. Meter readings (run-hours, cycles) and condition parameters (like vibration mm/s) are entered manually, via QR scan, WhatsApp, or a generic API call — and the system rejects any reading lower than the previous one, so run-hour logs stay monotonic across shifts and sites even when several people are logging data on the same asset.
Why the failure record matters more than the fix
A single restored inverter or restrung panel is worth little if the cause is never captured. AssetAI blocks closing a breakdown or corrective work order until a failure cause and remedy are recorded — a small enforcement rule that, applied consistently, builds a genuine failure Pareto across inverter and turbine failure modes over time. That record then feeds two decisions plant teams actually face:
- Repair or replace an ageing component — the RRR score weighs cost, failure trend, downtime cost, and asset age, relevant when a gearbox or converter keeps coming back for the same fault.
- Justify AMC renewal versus in-house repair — AMC and warranty coverage is tracked per asset with a fixed precedence (warranty > AMC > expired > none) and auto-stamped onto each service call, matching how OEM contracts on inverters and turbines are usually structured.
Downtime cost is computed as downtime hours × per-asset cost/hour, with an optional units-lost figure if rated output is entered per asset — a direct way to express an inverter outage in rupees of lost generation, without attempting to forecast yield or correlate it with weather or irradiance data, which this system does not do. Where production logs exist, OEE (availability × performance × quality) is computed per asset the same way it's used in broader TPM practice — see OEE explained for the mechanics.
For hazardous jobs like working at height or electrical isolation, the system provides a Safety Measure master and a printed checklist with sign-off lines rather than a full permit-to-work workflow — worth knowing upfront if that's a hard requirement. Compare this against other sectors under industries, check the complete list of capabilities on features, or book a demo against your own site's asset register.
Once the field-capture and failure-record habits are in place, the bigger payoff shows up in how AssetAI schedules preventive work and quantifies what a failure actually cost — both of which matter more for solar and wind than for a typical shop-floor line, because the assets are dispersed, expensive, and run on hours or cycles rather than a fixed shift calendar.
Scheduling PM around run-hours, cycles and thresholds — not the calendar
Inverters and turbines don't wear out on a monthly cadence; they wear out on cycle counts, run-hours, and load. AssetAI supports this directly:
- Usage-based PM triggers off meter readings — run-hours, cycle counts — fed in manually, via QR scan, WhatsApp, or a generic API call, and the system rejects any reading lower than the last one so the log stays monotonic across shifts and sites.
- Condition-based PM works off a monitored parameter and an alarm threshold captured on the schedule — vibration in mm/s on a turbine gearbox, for instance — and automatically raises a PdM work order once the threshold is crossed.
- There's no live SCADA or inverter-monitoring feed behind any of this — every reading is entered, scanned, or pushed via API. That's a deliberate scope boundary, not an oversight, and it's worth knowing up front if you're evaluating this against a telemetry-heavy platform (see all features for the full list of what's included).
Warranty precedence, downtime cost and the repair-or-replace call
Solar and wind assets typically carry OEM warranty followed by an AMC, and knowing which one is live at the moment a fault is logged changes who pays for the call-out. AssetAI stamps every service call with a fixed precedence — warranty, then AMC, then expired, then none — pulled straight from the asset's coverage record, so there's no manual lookup during a breakdown.
That same asset record feeds two decisions plant and O&M teams actually have to make:
- What did the outage cost? Downtime hours are multiplied by a per-asset cost/hour, with an optional units-lost figure if rated output has been entered — turning an inverter outage into a rupee number, though it stops short of forecasting generation or factoring in irradiance or wind speed.
- Repair, review, or replace? The RRR score, computed from cost history, failure trend, downtime cost and age, gives a structured basis for retiring an ageing gearbox or blade component instead of relying on a technician's gut feel.
Where a production log exists, OEE — availability × performance × quality, explained here — rolls up per asset, giving a like-for-like comparison across inverters or turbines even though the platform doesn't touch SCADA data directly. Teams building a broader TPM or ISO-aligned maintenance program (see standards) can treat this as the failure-cost and asset-hierarchy layer underneath it, without expecting it to double as a weather or yield-forecasting tool. For sites still deciding how this fits their O&M contracts and budget, the pricing page and a walkthrough via book a demo are the fastest way to see it against your own asset list.
CMMS for Solar & Wind FAQs
How do I set up preventive maintenance schedules for inverters that run on cycle counts instead of calendar dates?
AssetAI runs usage-based PM directly off meter readings — you log inverter cycle counts manually, via QR scan, WhatsApp, or API, and the system triggers a maintenance work order when that reading hits your threshold. This means your preventive maintenance schedule stays tied to actual equipment stress, not arbitrary calendar intervals. Each asset stores its own rated output and cost-per-hour, so when a PM is due, you know exactly which inverter needs service and can plan labour accordingly without guessing whether conditions have been met.
Why does my team keep closing work orders without documenting what actually failed on the turbine gearbox?
AssetAI blocks you from closing any breakdown or corrective work order until a failure cause and remedy are recorded in the system. Over time, this builds a genuine failure Pareto across your turbine failure modes — gearbox bearing wear, seal leaks, misalignment — so you can see which problems repeat and where design or maintenance changes would help most. Without this gate, those patterns stay invisible and costs keep climbing.
Can field technicians at remote sites file faults without needing system logins or mobile app installation?
Yes. A tech at a remote wind site can scan the asset QR code with any phone camera, enter their name and company PIN, and report a fault immediately — no login, no app download, no connectivity requirements for submission. This removes friction where site staff aren't formal system users and internet is unreliable. The fault lands in your queue as a breakdown work order, ready for triage and condition-based or usage-based maintenance dispatch.
How do I track warranty and AMC coverage so I don't bill the wrong party for inverter repairs?
Each asset — inverter, string, panel, turbine — stores warranty and AMC data with a fixed coverage precedence: warranty covers first, then AMC, then expired, then none. When a service call is logged, the system auto-stamps which coverage applies, so your technician and billing team see immediately whether that gearbox repair is under OEM warranty, covered by your maintenance contract, or chargeable to the operator. This matches the way OEM AMC contracts actually work on your equipment.
What's the real cost of an inverter outage if I know its rated output and operating cost?
AssetAI calculates downtime cost as downtime hours multiplied by the per-asset cost per hour that you define. You can also add units-lost — MWh or kWh not generated during the outage — so a 6-hour inverter failure shows you not just labour and spare parts, but the actual generation revenue lost. That figure becomes your real lever for deciding whether to pay more upfront for redundancy, spare equipment, or faster response teams. See OEE explained for how downtime feeds into your plant's overall effectiveness.
How does condition-based predictive maintenance work if my turbine has vibration monitoring?
You set an alarm threshold (e.g., 7.5 mm/s vibration) on your condition-based PM schedule. When a monitored parameter — vibration, temperature, pressure — reaches that threshold, AssetAI generates a work order automatically, so you can service the gearbox or generator before failure occurs. Unlike usage-based PM which counts run-hours, condition-based PM listens to the asset's actual health state, letting you act on early warning signs rather than waiting for a calendar or meter to trigger action.
How do I ensure spare parts inventory stays adequate when solar inverters fail unpredictably across multiple plant locations?
Track failure rates by inverter model and age in AssetAI's asset history. Set reorder points based on your lead time and failure frequency—not guesswork. Link critical spares to preventive maintenance plans so parts arrive before scheduled replacements. Flag slow-moving stock quarterly to avoid dead inventory.