Turbine Blade Inspection Checklist for AI Borescope Assessment

By Johnson on July 31, 2026

turbine-blade-inspection-checklist-ai-borescope-assessment

A turbine blade borescope inspection is only as good as the consistency behind it — the entry angle at each stage, the image capture sequence across every blade row, the criteria used to classify a mark as cosmetic versus a defect requiring action, and the discipline to compare every finding against a real baseline rather than memory. Skip a step or apply inconsistent judgment and a developing crack, coating spallation, or foreign object damage site can pass two or three inspection cycles before anyone flags it as a trend. This interactive checklist lays out the sequence an AI-assisted borescope inspection should follow from entry through final classification — check off each item as your team completes it — and where iFactory's turbine inspection tools fit into each stage.

iFactory Checklist — Turbine Inspection

The Complete AI Borescope Turbine Blade Inspection Checklist

Entry angles, image capture sequence, defect classification criteria, and severity thresholds — a checkable, step-by-step procedure for running a consistent, AI-verified turbine blade inspection every cycle.
20 Items
across 6 checkable stages, from prep to fleet review
Every Row
Blade coverage
Standardized
Entry angles
4-Tier
Severity scale
Baseline
Comparison built in

Why a Checklist Matters More Than Experience Alone

Experienced inspection technicians develop strong instincts, but instinct is exactly the thing that varies from one person to the next and from one shift to another. A structured checklist does not replace technician skill — it removes the variability that skill alone cannot control, ensuring the same access points, the same image angles, and the same classification thresholds apply whether the inspection is being run by a twenty-year veteran or a technician six months into the role. That consistency is what makes cycle-over-cycle trend detection possible at all.

Tick each box below as your crew completes that step. Boxes are grouped by stage so a partially finished outage inspection always shows exactly where the walkdown left off.

Stage 1 — Pre-Inspection Preparation

Stage 2 — Entry Angle and Capture Sequence

Consistency at this stage is what makes AI-based comparison possible in the first place — a model comparing this cycle's image against last cycle's baseline needs the blade photographed from a matching angle and distance, or the comparison introduces noise that masks real degradation.

Stage 3 — AI-Assisted Defect Classification

Severity Thresholds by Defect Category

Defect Category Low Severity Medium Severity High Severity Typical Action at High
Crack Indication Not detected Sub-threshold length, monitor Exceeds length or depth threshold Remove from service
Coating Spallation Isolated, small area Multiple sites, moderate area Substrate exposed over large area Schedule recoat or replace
Leading Edge Erosion Cosmetic only Measurable profile change Aerodynamic profile compromised Engineering evaluation
Foreign Object Damage Surface mark, no deformation Minor deformation, no crack Deformation with crack indication Immediate escalation

Want this checklist built into your own inspection workflow with automated severity scoring? Book a walkthrough to see it running on sample blade images.

Stage 4 — Engineering Review and Sign-Off

Stage 5 — Recordkeeping for the Next Cycle

Stage 6 — Fleet-Level Pattern Review

The final stage extends beyond a single unit's inspection cycle — reviewing whether a specific defect pattern is appearing consistently across multiple units of the same turbine model, which often points to a design, coating, or operating condition issue rather than an isolated maintenance concern for one unit.

Common Mistakes That Break Baseline Comparison

Every stage of this checklist ultimately supports one goal: making sure the image captured this cycle can be compared against the baseline image with confidence. That goal is easier to state than to consistently execute across dozens of blades and multiple stages during a time-pressured outage window, and a handful of recurring mistakes are responsible for most of the comparison failures plants run into. These are flagged separately below since they are pitfalls to watch for, not steps to check off.

A
Approximating entry angle from memory instead of the documented standard for that specific port, which shifts perspective enough to introduce false deviation.
B
Skipping platform and root fillet images when time is tight, leaving a known high-risk area with no comparison data for that cycle.
C
Inconsistent lighting output between cycles, either from equipment drift or a different borescope unit being used without recalibration.
D
Mislabeling blade number or stage during a long walkdown, which causes the model to compare an image against the wrong baseline entirely.

Why the Fleet-Level Review Stage Often Gets Skipped — and Shouldn't

Stage six is the step most inspection programs treat as optional, largely because it requires pulling data across multiple units rather than focusing on the one unit currently in the outage bay. That is precisely why it matters. A single high-severity crack indication on one blade might reasonably be treated as an isolated maintenance item. The same crack indication appearing at the same stage position on three sister units within the fleet points toward something systemic — a coating batch issue, a design margin problem at that specific stage, or an operating condition common across those units that is accelerating a shared failure mode. Catching that pattern early, rather than treating each unit's finding in isolation, is often the difference between a scoped repair and a fleet-wide engineering investigation launched only after a unit actually fails.

Building this review into the standard checklist rather than leaving it as an ad hoc exercise means it happens every cycle rather than only when someone happens to notice a pattern informally. Plants that formalize this step typically assign it to the reliability engineering lead rather than the outage inspection technician, since it requires access to fleet-wide records that go beyond any single unit's outage file.

Frequently Asked Questions

Do we need to change our borescope hardware to use this checklist?
In most cases no — the checklist and the AI classification layer are designed to work with the digital borescope systems already standard in turbine outage inspection, since the value comes from standardizing the procedure and adding automated comparison rather than changing image capture hardware. What matters most is capture consistency: matching angle, distance, and lighting to the baseline set, which is a procedural discipline more than an equipment requirement. Our team reviews your current borescope setup during onboarding to confirm image quality is sufficient for reliable comparison. Reach out to our team to confirm compatibility with your equipment.
How is severity actually scored by the model?
Severity scoring combines two signals — the degree of deviation from the matched baseline image and the classification of the defect type against a trained reference library covering common turbine blade degradation modes like cracking, spallation, erosion, and foreign object damage. Each category has its own threshold logic, since a small crack indication and a similarly sized coating spallation site carry very different operational risk and require different severity curves. The thresholds shown in this checklist reflect common defaults, and are tuned to your specific turbine model and operating history during deployment. Book a walkthrough to see the scoring logic applied to sample images.
What happens if we don't have a clean baseline image set from prior cycles?
A missing or inconsistent baseline is common for plants running their first AI-assisted inspection cycle, and it does not block getting started. The first cycle run under the standardized checklist becomes the baseline going forward, with severity scoring in that initial cycle relying more on absolute defect classification against the trained reference library than on direct image comparison. From the second cycle onward, full baseline comparison becomes available, and inspection quality improves further with each subsequent cycle. Talk to our team about getting your first standardized cycle scheduled.
Can this checklist be adapted for steam turbines as well as gas turbines?
Yes, the six-stage structure of the checklist applies to both, though the specific defect categories and severity thresholds differ — steam turbine blades are more commonly assessed for erosion, deposit buildup, and stress corrosion cracking, while gas turbine hot-section blades are assessed more heavily for thermal-mechanical fatigue, coating spallation, and creep. The entry angle and capture sequence stages are adapted to each unit's specific borescope port configuration during onboarding. Book a demo to see the checklist configured for your specific turbine type.
How much time does the AI classification step add to an outage inspection?
The classification step runs in the background while the physical borescope walkdown continues, since images can be uploaded and processed as they are captured rather than requiring the walkdown to pause. In practice this means the AI-assisted classification adds negligible time to the outage window itself, while the engineering review stage that follows is typically faster than a fully manual review because flagged findings are pre-sorted by severity rather than requiring the engineer to review every single image from scratch. Contact our team for a time-on-outage estimate specific to your inspection scope.
Standardize Every Cycle.

Run This Checklist With Automated Severity Scoring Built In

See how the full six-stage, 20-item checklist runs on your own turbine blade images, with baseline comparison and severity scoring handled automatically at every step.

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