Build vs. Buy for Predictive Maintenance in Wind: A Guide to the Right Data Path
- Michael Tegtmeier
- Nov 16
- 7 min read
Lena’s Normal Day, Before Everything Stopped Being Normal
When Lena took over as technical lead for a 420 MW onshore fleet, things looked stable. Her SCADA thresholds were tuned. The CMS vendor dashboard lit up when it mattered. The full-service agreements promised broad coverage. If a turbine tripped, the crew knew the site, the gate code, the spare parts shelf.
On paper, her wind O&M decision support setup worked.
Then the gaps started showing. Lead times for spares stretched to months. Liability caps crept into the service contracts. Insurance renewals came with new exclusions. Unplanned downtime costs ballooned. The CFO asked for 15% OPEX reduction and fewer surprises—this quarter. The board wanted a predictive maintenance roadmap and a unified datalake strategy, in 90 days.
The internal debate landed squarely on Lena’s desk: do we build predictive maintenance and the IT‑infrastructure for renewables in‑house—or buy a proven solution?
Why Infrastructure Decisions Matter Now
The stakes were technical, economic, and personal:
Technical: Thresholds and OEM alarms miss slow-burn failures. SCADA at 10s/1s resolution can’t see early bearing degradation or pitch loop drift. Integrating high-frequency CMS, oil analysis, met masts, and service reports into one datalake for reliable anomaly detection is non-trivial. Model drift and validation require MLOps discipline few wind teams are staffed for.
Economic: A main bearing failure can exceed €300k in parts + crane + downtime. A 0.3–0.7% AEP loss from yaw misalignment or pitch imbalance is millions over a portfolio. Every week of delay in root-cause detection is real money.
Personal: “I can’t go to the board with a science project,” Lena told her OT engineer. “I need accuracy numbers, deployment requirements, and a cost–benefit that holds up with finance and insurance.”
What many teams try first but fail
DIY pipelines with OPC UA connectors, an on‑prem historian mirror, some Python models, Grafana, and alerts.
A POC on a handful of turbines that works—until it’s time to scale across vendors, sites, and component types.
Data gaps. Nasty edge cases. No common schema. False positives. Alert fatigue. The person who wrote the model changes roles.
The result: six months in, the platform exists, but the value is unclear, the model governance is unproven, and insurers still don’t trust the risk reduction. Meanwhile, the O&M calendar doesn’t stop.
Reframing the Problem as Risk Infrastructure
At an insurer roundtable, Lena heard a sentence that changed the brief: “We price portfolios lower when we trust the operator’s risk infrastructure.” Not “a model.” Not “a dashboard.” A risk infrastructure.
She piloted Turbit with a small, representative cluster. She wasn’t expecting what happened next:
Month 1: The Turbit Datahub ingested SCADA (IEC 61400-25/OPC UA), CMS streams, met mast data, oil reports, and OEM service PDFs into one datalake built for AI. Time alignment across 1 Hz SCADA and high-frequency condition data just… worked.
Month 3: AI Monitoring trained per‑turbine, per‑component models with baselines for each site’s conditions—no generic “one‑model‑fits‑all.” Alerts arrived with a probable root cause and recommended actions.
Day‑to‑day: The Turbit Assistant became the team’s “wind ChatGPT,” answering “Have we seen this generator bearing signature before?” by parsing Lena’s oil lab PDFs, service work orders, and SCADA history. It could generate a draft work order and route it.
Insurance: With Turbit Blue, her insurer recognized the proactive risk reduction. Claim processes aligned, terms improved. No extra admin.
It felt less like a tool and more like a new operating system for wind O&M decision support.
How the Ideal Stack Works
1) Datahub: A datalake built specifically for renewables
What it does: Unifies all renewable data—SCADA, CMS, vibration, met mast, blade monitoring, oil analysis, thermography, service logs, status codes—into one high‑resolution, time‑aligned store.
Why it matters: You can’t do serious predictive maintenance or cross‑vendor integration without consistent time indices, metadata, and cost‑efficient high‑frequency storage.
How it connects: Works with every major vendor. Streams data out via API and MQTT in real time. Designed for AI workloads to minimize compute/storage costs and maximize query performance.
Outcome: Vendor changes become routine, not a multi‑month risk. Your datalake outlives any single monitoring vendor.
2) AI Monitoring: Per‑component models that learn your fleet
What it does: Detects deviations months before alarms by building turbine‑ and component‑specific models (generator bearings, main bearing, gearbox stages, pitch/yaw systems, power curve changes).
Signal quality: Combines SCADA, CMS, blade monitoring inputs (via partners like Weidmüller), and environmental data to isolate true anomalies from weather or curtailment effects.
Workflow: Each alert includes a probable root cause and an action recommendation. Operators give feedback; the models learn across the portfolio.
Governance: Transparent event lifecycles, reproducible accuracy metrics, audit trails—so engineering and insurance can trust the calls.
3) Assistant: The wind industry’s AI co‑pilot
What it does: You upload oil reports, service inspections, OEM manuals—Assistant answers questions in natural language and ties them to live SCADA and Turbit events.
Example: “Show turbines with pitch imbalance likely causing >0.4% AEP loss, summarize last service notes, and draft a work order.” Assistant generates a ranked list and a ready‑to‑send plan.
Result: Busywork compresses. Engineering time goes back to engineering.
4) Turbit Blue: Closing the loop with insurers
What it does: Converts your risk infrastructure into better insurance conditions. Aligns claims management with detected events. Minimizes admin overhead.
Why it matters: If your models reduce risk but your policy doesn’t recognize it, you’re leaving money on the table.
Note on hardware: Turbit is software‑only. For rotor blade monitoring hardware, Turbit partners with Weidmüller, ensuring deep integration without locking you into any single hardware stack.
The In‑House Cost Curve vs. a Proven IT Infrastructure Stack from a Third Party
If you’re weighing in‑house vs. external solutions, list the true costs of “build”:
Team and time
Data platform: 2–3 data engineers for ingestion, storage, schema management, time alignment, security, and APIs.
ML/MLOps: 1–2 ML engineers for model training, drift monitoring, continuous improvement, experiment tracking, and deployment pipelines.
Domain expertise: Reliability engineering to convert anomalies into actionable work orders across varying turbine types and sites.
Ops and Security: 24/7 availability, on‑call for data outages, backup/restore, upgrades, KPI reporting.
Complexity you must own
Multi‑vendor integrations, IEC 61400‑25/OPC UA edge cases, high‑frequency CMS processing and compression.
Model generalization across sites with different terrain, curtailments, and grid codes.
Alarm governance and auditability to satisfy insurers and internal risk.
Change management as vendors/hardware evolve.
TCO reality
Year 1: It looks comparable to a license. Year 2–3: Maintenance, scaling, and staff turnover make it more expensive—and riskier—because accuracy and trust degrade without continuous learning and feedback loops.
External solutions win when:
You need consistent accuracy metrics across a diversified portfolio.
You want to de‑risk vendor changes and avoid re‑platforming.
You need insurer trust and better terms, not just “a model.”
Outcome Examples You Can Measure
Early failure detection: A generator DE bearing deviation was detected ~4 months before threshold alarms, scheduled replacement during low‑wind season. Avoided 12 days of downtime and ~€180k combined cost.
AEP recovery: Pitch drift on 18 turbines was identified and corrected, recovering ~0.4% AEP at portfolio level. For a fleet producing ~1,050 GWh/year, that equated to ~4.2 GWh—roughly €200k/year at €48/MWh.
OPEX unlocked: By combining Datahub + AI Monitoring + Assistant, the team reduced avoidable maintenance spend and unplanned interventions. Across portfolios, Turbit users typically unlock 30% or more OPEX savings potential while increasing failure coverage—because insurers trust the risk reduction and align terms.
Noise reduction: Compared to raw thresholding, nuisance alarms dropped >60%. Engineers received fewer, higher‑quality alerts with root cause suggestions.
Decision latency: Time from “we think something’s off” to “approved work order” fell from weeks to hours. Assistant handled summaries, comparisons, and documentation using the team’s own PDFs and system data.
Insurance: With Turbit Blue, claim management aligned with detected events. Premium conditions improved due to demonstrable, repeatable risk mitigation.
The emotional shift mattered too. The Friday 5 p.m. “we may have a failure brewing” calls turned into scheduled work windows. Lena’s team controlled the narrative again.
The Bigger Picture: Infrastructure for a Low‑Risk, Low‑Cost Renewable Future
The industry is aging—literally. Turbines are older, spare parts scarcer, and electricity prices more extreme. Liability caps are a signal: more risk is moving to owners. The way out isn’t just “more data” or “a model.” It’s a risk infrastructure that:
Normalizes heterogeneous data across vendors into an AI‑ready datalake.
Delivers high‑fidelity, per‑component predictive maintenance with clear accuracy and governance.
Automates the busywork so the human experts focus on decisions.
Converts operational excellence into better insurance outcomes.
That’s what Turbit was built to do—so operators can keep wind reliable, profitable, and almost risk free.
FAQ: Build vs. Buy Predictive Maintenance and Data Infrastructure for Wind
Q: Why can’t we just use SCADA thresholds and OEM alarms? A: Thresholds miss slow‑developing issues and environmental effects. AI models trained per turbine and component detect subtle deviations months earlier by learning each site’s normal behavior—improving wind O&M decision support beyond fixed limits.
Q: What accuracy should I expect from AI Monitoring? A: Expect months of lead time for many failure modes (bearings, gearbox stages, pitch/yaw anomalies), with far fewer nuisance alarms than thresholding. Accuracy is reported transparently per alert type and improves with your team’s feedback.
Q: How fast can we integrate our data into a datalake? A: With Turbit Datahub, most portfolios onboard core SCADA within weeks, with CMS, met mast, oil reports, and service PDFs following shortly. The platform is built for high‑time and high‑frequency data and streams to your systems via API/MQTT.
Q: We fear vendor lock‑in. How does Turbit handle integration of different systems? A: Turbit is vendor‑agnostic. Datahub integrates with every major renewable monitoring vendor and exposes your data back out in real time. If you change CMS or SCADA vendors, your datalake and AI stack stay intact.
Q: Is Turbit a hardware provider? A: No. Turbit is software‑only. For rotor blade monitoring hardware, Turbit partners with hardware providers like Weidmüller and integrates their data into the AI stack.
Q: Can we justify external costs vs. building in‑house? A: When you include staffing (data engineering, ML, MLOps, reliability), integration complexity, model governance, and insurer requirements, the total cost of ownership for in‑house typically exceeds a proven platform within 12–24 months—while delivering lower, less auditable accuracy.
Q: How do insurers factor into this? A: Turbit is the only AI‑driven predictive maintenance platform trusted globally by insurers to reduce renewable asset risks proactively. With Turbit Blue, that trust translates into aligned claim processes and improved insurance conditions.
Q: What about security and OT constraints? A: Deployments follow strict IT/OT security practices, support on‑prem and cloud‑hybrid patterns, and use controlled connectors (e.g., OPC UA, IEC 61400‑25). Audit trails and role‑based access are standard according to ISO27001.
Q: Can the Assistant really work with our PDFs and service notes? A: Yes. Upload oil lab reports, inspection forms, and OEM manuals. The Assistant answers questions, cross‑references with live data, and drafts work orders. It’s the wind‑specific, RAG‑enabled co‑pilot for your team.
Q: Where do I learn more about each component? A: Datahub · AI Monitoring · Blade Monitoring · Assistant · Turbit Blue
Conclusion
Lena didn’t choose a tool—she chose an operating model. If you’re at the build vs. buy crossroads, it’s worth a conversation. Start with Datahub, prove value with AI Monitoring, scale your team with Assistant, and let Turbit Blue turn it into financial stability. Talk to Turbit and see how a trusted risk infrastructure can reduce OPEX, increase AEP, and improve insurance terms across your fleet.
