Image-Based Rotational Tracking and Integrity Assessment of Wind Turbine Blade Attachment Region

Abstract

Abstract A system for visually monitoring a wind turbine blade attachment region is disclosed. The system comprises at least one imaging sensor and a processor configured to extract visual reference features from captured image data and register the data to a spatial reference frame. Based on this spatial reference frame, the system identifies at least one unique component within the attachment region, such as pitch bearings and fasteners, to determine their precise location, orientation, and condition. The processor is configured to detect mechanical degradation, including positional displacements such as fastener rotation indicative of structural loosening, and surface anomalies such as cracking or lubricant leakage. The system may be further configured for independent blade pitch angle calculation, calibration of inertial measurement units using the visually derived reference frame, and temporal comparison of registered image data. Condition data and fault alerts may be output to an external interface for remote monitoring. Abstract 20 26 20 20 12 17 M ar 2 02 6 A b s t r a c t 2 0 2 6 2 0 2 0 1 2 1 7 M a r 2 0 2 6 A b s t r a c t 2 0 2 6 2 0 2 0 1 2 1 7 M a r 2 0 2 6

Description

TECHNICAL FIELD The disclosure relates generally to structural health and condition monitoring of wind turbines. Specifically, the disclosure relates to a system for visually monitoring a wind turbine blade attachment region by extracting visual reference features and registering image data to a spatial reference frame. This spatially registered data provides in-situ optical inspection and condition monitoring of components disposed within the blade attachment region, facilitating the early detection of mechanical degradation, including positional displacements and surface anomalies, to support predictive maintenance strategies. BACKGROUND OF THE INVENTION

[0002] Wind turbine blade attachment regions are subject to high-amplitude cyclic mechanical loads and harsh environmental conditions. Components within these regions, including pitch bearings, attachment fasteners, pitch drive mechanisms, seal interfaces, and structural blade elements, undergo gradual degradation. Such degradation includes positional displacements, such as rotational displacements of attachment fasteners, axial shifts, gap separations, or pitch misalignments, and surface anomalies, such as spalling, material fretting, structural cracking, seal degradation, or lubricant leakage. Over time, these modes can lead to costly downtime or catastrophic failure.

[0003] Current inspection methods are largely manual, requiring physical access to the blade attachment region. Such procedures are time-consuming, hazardous, and often reactive rather than preventive. Sensor-based approaches that do exist tend to treat orientation sensing, fastener integrity, and component monitoring as disconnected tasks. Many existing systems capture raw images without maintaining a persistent spatial reference frame. Without this spatial context, reliably aligning images temporally to accurately detect subtle changes, such as positional displacements, becomes difficult.

[0004] Data access and retrofit remain barriers to deployment. Prior systems commonly rely on turbine control data via the turbine’s Supervisory Control and Data Acquisition (SCADA) 2026202012   17 Mar 2026 system to obtain blade pitch angle or hub state, which can have access restrictions, demand custom integration, and may be unavailable or inconsistent across mixed fleets. Furthermore, a wind turbine's internal pitch encoders and mechanical control systems can drift or fall out of calibration over time. Relying solely on internal SCADA data without an independent, visually derived reference metric can lead to undetected pitch misalignments. Alternatives that instrument the structure, for example strain-gauged bolts, load-sensing washers, or torque sensors, add wiring, telemetry and calibration burdens, introduce environmental ingress and mechanical failure points, and scale poorly across large bolt-circles. 0005] Conventional monitoring systems often lack a steady, repeatable view of the same parts. Small changes in the field of view of an imaging sensor or distance between inspections can make round bolt-circles look oval and bolt heads appear to turn when they have not. Furthermore, while actively adjusting the field of view, such as via panning or zooming, is often necessary to inspect fine component details, conventional motorised cameras lose their absolute spatial context when moved. Without a persistent coordinate system, a zoomed-in sensor cannot reliably index or confirm exactly which unique component it is evaluating. Because mounts are frequently ad hoc, simple physical references are seldom used, and routine sensor calibration is uncommon, images from different dates cannot be aligned to a common reference with confidence. Lens effects and minor shifts from vibration or temperature add further drift. As a result, apparent changes in the image data may be artefacts rather than real movement, undermining cross-session comparison, trending, and fault localisation.

[0006] Optical reliability under operational conditions is a recurring weakness. Fields of view are often occluded or narrowed by rotating structures, service hardware and the geometric boundaries of the blade attachment region. Imaging lenses are further susceptible to contamination from grease, dust, and moisture; illumination varies with ambient light leaks, specular reflections, and LED flicker. Rotation and vibration introduce motion blur, while CMOS rolling-shutter readout distorts fast-moving edges. Together these effects degrade metrology and repeatability, particularly for detecting small bolt-angle changes or subtle axial shifts.

[0007] Existing image-based inspections frequently lack frame-level association between imagery and the blade’s absolute pitch or rotational state at the moment of capture. Where orientation 2026202012   17 Mar 2026 is logged at all, it is often captured separately (e.g. via controller / SCADA) at different sampling rates and with unsynchronised clocks, leading to time value mismatches and potentially uncalibrated readings. Furthermore, prior systems struggle to capture reliable multi-dimensional or depth-aware measurements, severely limiting the ability to assess axial displacement, flange geometry, or local surface shape for wear analysis across maintenance intervals. 0008] Deliberately applied or tracked reference features on the blade attachment region, pitch bearing, retaining fasteners, and adjacent rotating hardware are rarely utilised effectively in prior deployments. In the absence of such persistent, high-contrast references, systems rely on natural texture or ad-hoc paint marks that vary with illumination, contamination, and viewpoint. This undermines robust rotational tracking, prevents the reliable mapping of sequential frames into an expanded spatial reference frame, and limits automated detection of small condition changes across maintenance intervals.

[0009] Where multiple sensing modalities are present (e.g. white light, IR, thermal), prior art typically lacks a defined coordinate frame tied to the rotating components. Without a stable set of physical references in view, cross-session image data and multi-modal datasets cannot be consistently co-registered, reducing sensitivity, increasing false positives, and complicating the localisation and identification of emerging faults at the pitch bearing, bladeroot interface, and bolt-circles.

[0010] Systems that estimate pitch or rotational pose using only inertial measurement units (IMUs) or accelerometers accumulate integration errors from gyro bias, scale-factor and temperature effects, gravity misalignment, and mounting creep. After power cycling or abnormal temperature changes, alignment can shift, necessitating periodic calibration to an external reference that many installations do not provide. As a result, pose estimates degrade between maintenance intervals and cannot guarantee repeatable spatial context for condition monitoring.

[0011] Prior approaches to fastener condition tracking often rely on instrumented hardware (e.g. strain-gauged bolts, load-sensing washers, torque sensors) or simple paint marks for manual condition monitoring. Instrumented approaches add mass, wiring, and calibration overhead and create additional failure or ingress points; they also scale poorly to large bolt circles. By 2026202012   17 Mar 2026 contrast, paint-mark checks are qualitative and operator-dependent and are not repeatable enough for reliable automated, fleet-wide trending. 0012] At fleet scale, time synchronisation is often inadequate. Imaging sensors are frequently not time-aligned: their clocks drift, their sampling / exposure times differ, and network delays further offset their timestamps. If timestamps are not frame-accurate and synchronised, operators cannot confidently match events to the correct visual frame or blade position. This weakens event reconstruction and reduces the accuracy of anomaly localisation. 0013] Accordingly, there is a need for a retrofit-capable monitoring system that provides a perimage rotational reference, by extracting visual reference features, enabling consistent registration of image data to a spatial reference frame across sessions without reliance on proprietary SCADA or control signals. Such a system should co-register multi-modal data to a common coordinate frame tied to the rotating hardware; support robust metrology despite occlusion, contamination, variable illumination, motion blur and rolling-shutter effects; and mitigate geometric errors from parallax, changing standoff and lens distortion. It should enable non-invasive monitoring of unique components of the blade attachment region, including detection of positional displacements and surface anomalies, while providing a stable, visually derived reference for IMU calibration to address drift. Together, these requirements define the need for an integrated, spatially aware monitoring system suitable for continuous operation in the wind turbine environment. SUMMARY OF THE INVENTION

[0014] The present disclosure provides an integrated system for visually monitoring a wind turbine blade attachment region. In various embodiments, the system comprises at least one imaging sensor positioned such that at least a portion of the blade attachment region is within its field of view, alongside a communicatively coupled processor. The processor is configured to process image data obtained from the imaging sensor to extract one or more visual reference features, such as a fiducial marker, a structural geometry of a blade attachment component, or a surface texture of a blade attachment component. The processor matches these extracted visual reference features to known locations to register the image data to a spatial reference frame, which may comprise a multi-dimensional coordinate system. In some embodiments, 2026202012   17 Mar 2026 the processor is further configured to determine a pitch angle of the blade relative to a reference, based on the extracted visual reference features. The processor may further output this determined pitch angle as an independent reference metric to calibrate or validate a pitch control system of the wind turbine. Based on this spatial reference frame, the processor identifies at least one unique component within the image data, such as a pitch bearing, an attachment bolt, a pitch drive mechanism, a seal interface, a lightning protection system, or a structural blade element, and determines its location and orientation. Furthermore, the processor is configured to determine a condition of the unique component based on the image data. Determining this condition may comprise detecting a positional displacement, such as a rotational displacement of an attachment fastener indicative of structural loosening, an axial shift of a component, a gap separation at a structural interface, or a pitch misalignment; or detecting a surface anomaly, such as surface spalling, material fretting, structural cracking, seal degradation, lubricant leakage, composite delamination, surface corrosion, or debris contamination.

[0016] To facilitate image capture, the system may comprise an active illumination source configured to selectively illuminate the field of view of the imaging sensor. Additionally, the system may incorporate at least one inertial measurement unit communicatively coupled to the processor. To counteract sensor drift, the processor may be configured to utilise the determined spatial reference frame as a calibration reference to correct the inertial measurement unit. Following calibration, the processor may communicate with the inertial measurement unit to determine the spatial orientation of subsequent images due to movement of the turbine’s attachment region and / or the imaging sensor.

[0017] For advanced spatial mapping, the at least one imaging sensor may comprise a motorised camera, with pan, tilt, and or zoom functionality. In this embodiment, the processor is configured to actively adjust a field of view of the camera to capture image data of the desired unique components. This captured image data is subsequently registered to the spatial reference frame using the indexed coordinates of the components. The system may also include at least one additional imaging sensor having a partially overlapping field of view, allowing the processor to fuse image data from both sensors to construct an expanded spatial reference frame. 2026202012   17 Mar 2026 To track component health across multiple inspections, the processor may be configured to execute a temporal comparison between newly acquired data from the imaging sensor and stored reference data to monitor a status of the blade attachment region over time. Should this analysis reveal an issue, the processor may transmit an alert signal indicative of a fault condition. Finally, to support fleet-level or off-site management, the processor may be configured to output at least one of the determined locations, the determined orientation, or the determined condition of the unique component to an external platform, allowing for remote condition monitoring. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 illustrates a partial internal view of a wind turbine equipped with a system for visually monitoring a blade attachment region in accordance with one embodiment of the present disclosure.

[0020] Figure 2 illustrates a camera setup monitoring the pitch bearing and fiducial markers for pitch angle detection.

[0021] Figure 3 is a schematic diagram illustrating an image registration process for mapping raw image data to the spatial reference frame.

[0022] Figure 4 is a schematic diagram illustrating a rotational displacement of a unique component—a bolt head—as detected by the system across a temporal sequence of image data.

[0023] Figure 5 is a schematic diagram illustrating an expanded spatial reference frame of the blade attachment region generated by registering image data from a plurality of imaging sensors.

[0024] Figure 6 is a flowchart outlining the data processing steps of the monitoring system.

[0025] Figure 7 is an example user interface image of multiple data streams overlayed over the background geometry. 2026202012   17 Mar 2026 DETAILED DESCRIPTION OF EMBODIMENTS 0026] The present disclosure provides an integrated system for visually monitoring a wind turbine blade attachment region. The system comprises at least one imaging sensor positioned to capture at least a portion of the blade attachment region, and a processor, communicatively coupled to the at least one imaging sensor, configured to process the captured image data. Specifically, the processor extracts one or more visual reference features from the image data and registers the image data to a spatial reference frame based on those features. Based on this spatial reference frame, the processor identifies at least one unique component within the image data and determines its location and orientation. This unique component may comprise pitch bearings, bearing races, attachment fasteners, a pitch drive mechanism, a seal interface, a lightning protection system, or a structural blade element.

[0027] In various embodiments, the system may be further configured to use this spatially registered data to monitor for faults or calculate a pitch angle of the blade relative to a reference. By leveraging computer vision to extract visual reference features, such as fiducial markers, inherent component geometry and surface features, the blade pitch angle may be obtained independent of the turbine’s control system. The image data may be analysed by the processor to determine a condition of the unique components within the blade attachment region. Determining this condition may comprise detecting a positional displacement, such as a rotational displacement of a unique attachment bolt indicative of structural loosening, or detecting a surface anomaly, such as surface spalling, delamination or lubricant leakage. The following description illustrates embodiments of the apparatus and method with reference to drawing numbers.

[0028] Figure 1 illustrates a wind turbine incorporating the visual monitoring system 100. In this embodiment, at least one imaging sensor 101 is located within the wind turbine blade 103 and / or hub 104 and is positioned such that at least a portion of the blade attachment region is within its field of view. In the embodiment depicted, the system comprises two imaging sensors 101 positioned on either side of the pitch bearing 102. Alternatively, the system may comprise a single imaging sensor on one side of the pitch bearing. In a further embodiment, the imaging sensor is mounted externally to capture the blade attachment region. This configuration is particularly suited for turbine designs where attachment components, such as bolts, are exposed on the exterior of the blade. 2026202012   17 Mar 2026 0029] Figure 2 depicts a cutaway of the imaging sensor’s field of view and monitoring of the blade attachment region 200. In this embodiment, the imaging sensor 101 is positioned such that its field of view 203 captures the pitch bearing 102 and attachment bolts 201. The processor processes image data obtained from the imaging sensor to extract one or more visual reference features from the blade attachment region. These visual reference features may comprise a fiducial marker 202, the structural geometry of a component within the blade attachment region, or a specific surface texture of a blade attachment component 204. The processor registers the image data to a spatial reference frame, which may comprise a multidimensional coordinate system corresponding to the blade attachment region, by matching the extracted visual reference features to known locations within the spatial reference frame. Based on this spatial reference frame, the processor identifies at least one unique component within the image data and determines its location and orientation.

[0030] Figure 3 provides a detailed illustration of this registration process 300. The system acquires a raw image data frame 301 from the imaging sensor, which encompasses unique components, such as attachment bolts 201. Due to the camera angle, this raw frame typically exhibits perspective skew and may be obscured by components within the blade attachment region 303. To correct this, the processor extracts at least one visual reference feature 304 and mathematically maps the raw pixel coordinates onto the spatial reference frame. This transformation results in a registered image data frame 302 wherein perspective distortion is mitigated, and the unique components are aligned to the shared spatial reference frame. This normalisation substantially improves accuracy, ensuring that components are indexed at their respective locations to enable accurate temporal comparisons, regardless of the imaging sensor's specific viewpoint or standoff distance at the moment of capture.

[0031] In some embodiments, the processor is configured to determine a pitch angle of the blade relative to a reference based on the extracted visual reference features. The processor is further configured to determine rotational metrics, including revolution count, direction, and motion. Because this optical measurement provides a reliable data stream that operates independently of SCADA or control signals, the determined pitch angle can be output as an independent reference metric to calibrate or validate a pitch control system of a turbine, which may drift over time. 2026202012   17 Mar 2026 Imaging sensors may be fixed or motorised. In some embodiments, the at least one imaging sensor comprises a motorised pan-tilt-zoom camera, which may implement any combination of pan, tilt, and zoom (e.g. PT, PZ, TZ, full PTZ, or electronic PTZ (ePTZ) using high-resolution sensors). In this embodiment, the processor is configured to actively adjust a field of view of the camera such that it encompasses at least one unique component based on its indexed coordinates within the spatial reference frame. The PTZ / focus state is recorded, and captures are reprojected to the spatial reference frame, preserving measurement validity during close-up acquisitions of blade attachment region components. Autofocus, motorised zoom and optional stabilisation / gimbals mitigate blur in some embodiments. PT / PTZ inspections may be continuous, scheduled (e.g. periodic bolt-circle passes), or event-triggered by detected anomalies. The system may further comprise at least one additional imaging sensor having a field of view that partially overlaps with the field of view of the at least one imaging sensor. In this embodiment, the processor is configured to fuse the image data from both imaging sensors to construct an expanded or shared spatial reference frame. In a further embodiment, this combined reference frame may be uploaded to a remote condition monitoring platform to facilitate the display of a digital twin for monitoring of the blade attachment region.

[0033] In some embodiments, the system further comprises at least one inertial measurement unit (IMU) communicatively coupled to the processor to capture the dynamic movement and spatial orientation of the monitored region and components. In one embodiment, to counteract the inherent drift of IMUs caused by temperature fluctuations, dynamic loading, or power cycles, the processor may calibrate the IMU. Specifically, the processor derives a reference metric directly from the extracted visual reference features and utilises this to calibrate the inertial data, mitigating accumulated drift. Once calibrated, the IMU may continuously feed accurate inertial data back to the processor in another embodiment. This synergistic feedback loop enables the system to enter an inertial holdover state during a temporary loss of visual references. For example, if the processor actively adjusts the field of view to zoom in on a specific component, the narrow view may no longer contain sufficient visual reference features. In this state, the processor seamlessly relies on the newly calibrated IMU data to propagate orientation and maintain the spatial reference frame. Upon reacquisition of the visual reference features, the spatial estimate is re-aligned, and any newly accumulated drift is removed. Inertial measurements are time-aligned with the registered imagery to correlate with visual imagery. 2026202012   17 Mar 2026 0034] To physically support and protect the optical components within the harsh wind turbine environment, the at least one imaging sensor may be secured using specialised mounts and housings. Such mounts and housings may be engineered for vibration, temperature variation and contamination inherent to the install location, and may incorporate protective windows, shrouds and cleaning features such as a wiper. 0035] In some embodiments, the system comprises an illumination module configured to selectively illuminate a field of view of the at least one imaging sensor to facilitate image capture. This illumination may include white-light sources, synchronised strobes to mitigate motion blur, and polarising elements configured to suppress specular reflections from metallic or reflective surfaces. In another embodiment, near-infrared (NIR) illuminators may provide interior lighting in internal blade and hub cavities to improve contrast in low light. Narrowband spectral filters may additionally be utilised to reject ambient and stray daylight. The imaging sensor may also be configured to maximise image quality in the wind turbine blade attachment region. In some embodiments, this comprises a global-shutter sensor to further suppress motion artefacts. In other embodiments, the system may utilise alternative imaging modalities, such as infrared, thermal, or motion-magnified imaging. In embodiments comprising multiple imaging sensors across multiple modalities, such as thermal and white light, imaging sensors may be co-boresighted to better align data to the shared spatial reference frame.

[0036] All such modalities are time-synchronised and spatially aligned to the spatial reference frame for direct data comparison and fault analysis. The at least one imaging sensor and processor may be provided as a modular assembly suitable for retrofit or for embedded installation during manufacturing. In one embodiment, power may be sourced directly from the turbine. In other embodiments, energy harvesters—such as vibration, kinetic, thermal-gradient, or auxiliary solar harvesters—provide power with battery buffering. Hardware may additionally incorporate surge and lightning protection.

[0037] Figure 4 illustrates a detailed embodiment of the system’s capability to determine a condition of the at least one unique component of the blade attachment region based on the imaging data. Specifically, determining the condition may comprise detecting a positional displacement relative to the shared spatial reference frame. To illustrate this detection 2026202012   17 Mar 2026 process, the figure presents a sequence of top-down views of an attachment fastener, shown here as a bolt head 402 and washer 401 captured at different time intervals. As shown, each attachment fastener may include a visual fiducial mark 403, such as a line or dot, applied to its top surface. Alternatively, the processor may reference a natural surface feature or inherent geometry, such as the edges of the bolt head 402 to track the positional displacement in the absence of a fiducial marker. By executing a temporal comparison between newly acquired image data and stored reference data, such as comparing the angular position of the fiducial marker in successive images against a reference (e.g. t2, t3 against t1), the processor is able to detect a rotational displacement of an attachment fastener indicative of bolt loosening or vibrational drift. Arrows or angular indicators may be output to highlight the change in orientation over time. This non-contact optical method enables early detection of mechanical fastener degradation without the need for torque sensors or physical instrumentation.

[0038] The detected positional displacement is not limited to bolt rotation, but may also include an axial shift of a component, a gap separation at a structural interface, or a pitch misalignment. In addition to these spatial shifts, determining the condition may comprise detecting a surface anomaly on the unique components. These surface anomalies may include surface spalling, material fretting, structural cracking, seal degradation, lubricant leakage, composite delamination, surface corrosion, and debris contamination.

[0039] Figure 5 exemplifies one embodiment of generating an expanded spatial reference frame, wherein two images 501, 502 of the pitch bearing 102 with overlapping fields of view (503) are aligned and registered by matching portions of fiducial markers 202 from both images to produce a single, composite image 500 of the entire pitch bearing. To ensure these overlapping images align seamlessly and the resulting spatial reference frame is accurate, the system is configured to perform a multi-factor calibration of the imaging sensors, accounting for specific optics, mounting geometry, and capture timing. Following this calibration, the processor applies digital lens corrections and viewpoint constraints to the captured image data. These processing steps mitigate geometric errors—such as optical distortion caused by changes in camera perspective or variations in standoff distance—ensuring the physical dimensions of the components are correctly represented before registration. Integrated calibration fixtures may further support automated self-checks and recalibration routines during turbine maintenance windows. Furthermore, in embodiments where depth perception is advantageous—such as when constructing a three-dimensional expanded spatial reference 2026202012   17 Mar 2026 frame—the system may incorporate depth-sensing hardware, including stereoscopic imaging configurations or time-of-flight (ToF) sensing to provide precise three-dimensional measurements of axial displacement and map the component geometry, including bolt circles, bearing races, blade-root interfaces, and individual fastener features. Additionally, the processor may utilise photogrammetric methods to reconstruct the local surface topology of these components, establishing a baseline for precise cross-session comparisons over the operational lifespan of the turbine. 0040] Figure 6 illustrates a flowchart representing the data processing sequence executed by the monitoring system. The process begins with the at least one imaging sensor capturing image data of the blade attachment region and associated visual reference features 601. These images are then analysed by the processor to extract the visual reference features 602 and register the image data to the spatial reference frame 603. Optionally, the system may branch from this step to calculate the pitch angle of the blade 607, interface with one or more IMUs mounted on or near the bearing, using the spatially registered data to calibrate or contextualise inertial data 608, or to independently validate a pitch control system 609.

[0041] Returning to the primary image pipeline, based on the established spatial reference frame, the processor identifies at least one unique component, such as an attachment bolt, within the image data and determines its location and orientation 604. The processor then determines a condition of the unique component, such as detecting a positional displacement indicative of structural loosening or identifying a surface anomaly 605. In an exemplary embodiment, this comprises comparing sequential images of bolt heads to determine a positional displacement indicative of structural loosening. Finally, if the analysis identifies conditions exceeding predefined thresholds, such as seal leakage or bolt rotation, the processor generates an alert 606. Specifically, the processor is configured to output an alert signal indicative of a fault condition associated with the blade attachment region. Furthermore, the processor may be configured to output at least one of the determined location, orientation, or condition of the at least one unique component to an external interface 610. This flow depicts the integrated software pipeline and decision logic underlying the described embodiments.

[0042] In various embodiments, the processor executes analytics on the spatially registered image data to quantify these determined conditions. For example, to evaluate a positional displacement, the software routines may calculate the specific angle of a rotated fastener or 2026202012   17 Mar 2026 measure the physical dimensions of a structural gap (e.g., flange separation or shim movement) by tracking visual reference features in the registered view. As previously described, stereo imaging or time-of-flight depth sensing may be used to facilitate the construction of a shared spatial reference frame to confirm or refine these positional measurements volumetrically. In one embodiment, to track the health of the turbine components across multiple inspections, the processor is configured to execute a temporal comparison between newly acquired data from the at least one imaging sensor and stored reference data to monitor a status of the blade attachment region over time. For example, the stored reference data may comprise an initial set of image data previously registered to the spatial reference frame. By comparing the newly acquired data against this historical baseline, the processor can trend the progression of conditions over time, such as tracking a widening gap separation at a structural interface or a worsening surface anomaly, and trigger an alert if a developing deviation is detected.

[0044] In various embodiments, the acquisition of image data may be continuous, scheduled, or event-triggered (e.g., by vibration, temperature, or predefined detection thresholds). To support large-scale deployments, monitoring parameters may be configurable at the fleet, site, model and component levels. These parameters may also be individualised for a specific turbine according to its historical performance signature. To facilitate this higher-level management, the condition and spatial data transmitted to the external interface allows operators, in one embodiment, to track specific fault conditions, review alert criteria, and monitor component health across an entire wind farm from a centralised dashboard.

[0045] In various embodiments, the processor may be disposed locally or remotely relative to the imaging sensor. For example, processing operations may be executed by embedded hardware disposed directly inside a housing enclosing the imaging sensor, by a local computing device situated within the nacelle or hub, or the captured image data may be transmitted to a remote server or cloud computing environment for processing. Because wind turbines are often located in remote areas with poor network connectivity, the system may be configured to handle low-bandwidth environments. For instance, the system may immediately transmit small data packets (such as an alert signal) over a satellite or cellular link, while temporarily storing large image files locally on the turbine until a higher-bandwidth connection becomes available. To ensure security, all transmitted data may be encrypted, and system access 2026202012   17 Mar 2026 strictly controlled. Furthermore, the system may support secure over-the-air (OTA) updates, allowing operators to remotely upgrade the system's firmware or deploy newly trained anomaly-detection algorithms across an entire fleet of turbines without requiring physical maintenance visits. In some embodiments, the external interface comprises an operator dashboard that presents the spatially registered image data. To assist in condition monitoring, the user interface renders the image data with visual indicators and measurement guides derived from the tracked visual reference features. As shown in Figure 7, one embodiment of the interface displays a base view 700 of the internal blade geometry 701 and the monitored subject—here, the pitch bearing 102—within the shared spatial reference frame, along with the attachment bolts 201 and fiducial markers 202. Rather than presenting raw, disconnected camera feeds, the interface utilises the spatial reference frame to provide a unified perspective of the blade attachment region.

[0047] When the processor determines a condition or identifies that predefined thresholds have been exceeded, the system may be configured to render localised image frames 702 over specific regions of interest. These frames may highlight a detected positional displacement 703, such as the rotational shift of an attachment bolt, or a surface anomaly 704, such as a crack in the bearing race. As previously described, the processor is configured to output an alert signal indicative of a fault condition associated with the blade attachment region. Furthermore, the interface may associate metadata, such as timestamps and sensor configurations, with the spatially registered data.

Claims

1. A system for visually monitoring a wind turbine blade attachment region, comprising:a. At least one imaging sensor, wherein the imaging sensor is positioned such that at least a portion of a blade attachment region is within the field of view of the at least one imaging sensor;b. A processor, configured to process data obtained from the imaging sensor to:i. extract one or more visual reference features from the image data;ii. register the image data to a spatial reference frame based on the extracted visual reference features;iii. Identify at least one unique component within the image data and determine its location and orientation based on the spatial reference frame.

2. The system of claim 1, wherein the visual reference features comprise at least one of: a fiducial marker, a structural geometry of a blade attachment component or a surface texture of a blade attachment component.

3. The system of claim 1, wherein registering the image data to the spatial reference frame comprises matching the extracted visual reference features to known locations within the spatial reference frame.

4. The system of claim 1, wherein the processor is further configured to determine a pitch angle of the blade relative to a reference, based on the extracted visual reference features.

5. The system of claim 4, wherein the processor is further configured to output the determined pitch angle as an independent reference metric to calibrate or validate a pitch control system of the wind turbine.

6. The system of claim 1, wherein the spatial reference frame comprises a multi-dimensional coordinate system corresponding to the blade attachment region.2026202012   17 Mar 20267. The system of claim 1, wherein the at least one unique component within the imaging data comprises at least one of a pitch bearing, an attachment bolt, a pitch drive mechanism, a seal interface, a lightning protection system or a structural blade element.

8. The system of claim 1, wherein the processor is further configured to determine the condition of the at least one unique component of the blade attachment region based on the image data.

9. The system of claim 8, wherein determining the condition comprises detecting a positional displacement selected from the group consisting of: a rotational displacement of an attachment fastener, an axial shift of a component, a gap separation at a structural interface, and a pitch misalignment.

10. The system of claim 9, wherein the positional displacement comprises a rotational displacement of an attachment fastener indicative of structural loosening.

11. The system of claim 8, wherein determining the condition comprises detecting a surface anomaly selected from the group consisting of: surface spalling, material fretting, structural cracking, seal degradation, lubricant leakage, composite delamination, surface corrosion and debris contamination.

12. The system of claim 8, wherein the processor is further configured to output at least one of the determined location, the determined orientation, or the determined condition of the at least one unique component to an external interface.

13. The system of claim 1, further comprising an active illumination source configured to selectively illuminate a field of view of the at least one imaging sensor to facilitate image capture.

14. The system of claim 1, further comprising at least one inertial measurement unit communicatively coupled to the processor.2026202012   17 Mar 202615. The system of claim 12, wherein the processor is further configured to utilise the determined spatial reference frame as a calibration reference to correct inertial data from the at least one inertial measurement unit.

16. The system of claim 13, wherein the processor is configured to communicate with the at least one inertial measurement unit following calibration to determine the spatial orientation of subsequent images due to movement of the turbine’s attachment region and / or imaging sensor.

17. The system of claim 1, wherein the processor is configured to execute a temporal comparison between newly acquired data from the at least one imaging sensor and stored reference data to monitor a status of the blade attachment region over time.

18. The system of claim 1, wherein the processor is further configured to output an alert signal indicative of a fault condition associated with the blade attachment region.

19. The system of claim 1, further comprising at least one additional imaging sensor, wherein the processor is configured to register image data from both imaging sensors to a shared spatial reference frame.

20. The system of claim 1, wherein the processor is configured to actively adjust a field of view of the at least one imaging sensor via at least one of an electronic or mechanical pan, tilt, or zoom operation to encompass the at least one unique component based on its indexed coordinates within the spatial reference frame.

Images