Patent Description:
Wind power is considered one of the cleanest, most environmentally friendly energy sources presently available, and wind turbines have gained increased attention in this regard. A modern wind turbine typically includes a tower, a generator, a gearbox, a nacelle, and one or more rotor blades. The rotor blades capture kinetic energy of wind using known airfoil principles. For example, rotor blades typically have the cross-sectional profile of an airfoil such that, during operation, air flows over the blade producing a pressure difference between the sides. Consequently, a lift force, which is directed from a pressure side towards a suction side, acts on the blade. The lift force generates torque on the main rotor shaft, which is geared to a generator for producing electricity.

During operation, the various wind turbine components are subjected to a variety of loads. In particular, the rotor blades are subjected to both extreme and fatigue loading. Though the rotor blades are designed to withstand a certain amount of loading, wind conditions are not always predictable and can cause excessive blade damage. In worst case scenarios, blade damage can cause the entire wind turbine to be shut down such that appropriate repair measures can be scheduled. Such downtime causes a loss of annual energy production and is quite expensive.

Accordingly, it is advantageous to monitor blade health during operation of the wind turbine. Conventional systems employ sensors for monitoring certain characteristics of the blade and/or changes thereof overtime. For example, in certain instances, strain gauges may be employed for monitoring deflections in the rotor blades. In other examples, sensors may be mounted to the tower that monitor a distance of the blade tips of the rotor blades as the blades rotate about the hub. In such embodiments, if the distance changes overtime, blade damage may be inferred, and appropriate control action may be implemented.

Advances in monitoring blade health has been made in recent years but are not without issue. For example, infrared imaging has recently become more popular, yet the ability to analyze the large, cumbersome amounts of data associated with such imaging has proved to be too time-consuming and costly to be advantageous.

Accordingly, a system and method that addresses the aforementioned issues would be advantageous. Thus, the present disclosure is directed a system and method for monitoring wind turbine rotor blades using infrared imaging and machine learning techniques. A prior art example can be found in <CIT>.

In one aspect, the present disclosure is directed to a method for monitoring at least one rotor assembly of a wind turbine. For example, the rotor assembly may include any of a rotor having a rotatable hub with at least one rotor blade secured thereto. Thus, the method includes receiving, via an imaging analytics module of a controller, thermal imaging data of the rotor assembly. The thermal imaging data includes a plurality of image frames. The method also includes automatically identifying, via a first machine learning model of the imaging analytics module, a plurality of sections of the rotor assembly within the plurality of image frames until all sections of the rotor blade are identified. Further, the method includes selecting, via a function of the imaging analytics module, a subset of image frames from the plurality of image frames, the subset of image frames comprising a minimum number of the plurality of image frames required to represent all sections of the rotor blade. Moreover, the method includes generating, via a visualization module of the controller, an image of the rotor assembly using the subset of image frames.

In another aspect, the present disclosure is directed to a system for monitoring at least one rotor assembly of a wind turbine. The system includes a controller having an imaging analytics module configured to perform a plurality of operations and a visualization module. The plurality of operations includes, during a model-building time period, receiving input data comprising thermal imaging data of the rotor assembly, the thermal imaging data comprising a plurality of image frames, automatically identifying, via a machine learning model of the imaging analytics module, a plurality of sections of the rotor blade within the plurality of image frames until all sections of the rotor blade are identified, selecting, via a function of the imaging analytics module, a subset of image frames from the plurality of image frames, the subset of image frames comprising a minimum number of the plurality of image frames required to represent all sections of the rotor blade, automatically identifying, via the machine learning model, at least one anomaly and associated anomaly category information within the subset of image frames, generating output data comprising pixel information that corresponds to the at least one anomaly and the anomaly category information, and during a model-implementing time period, training the machine learning model over time using the input data and the output data. Further, the visualization module is configured to generate an image of the rotor assembly using the trained machine learning model. It should be understood that the system may include any one or more of the additional features described herein.

Infrared (IR) imaging provides the ability to detect subsurface defects that cannot be seen with traditional imaging methods. As such, the present disclosure is directed to an imaging system that collects IR images of wind turbine blades. The imaging system can either be operated manually or automated using a sensor-based controller. Further, the imaging system of the preset disclosure provides deep-learning based automation tools for performing analysis of the collected IR wind turbine blade data. After collecting the IR data, the imaging system can automatically identify various components and can proceed to automatically identify blade sections within the images. After performing this analysis, the IR frames can be sub-sampled using a metric function so that a minimum set of critical frames is included for final analysis, thereby reducing the burden of the analysis. More particularly, the imaging system may use a deep-learning based system to automatically recognize defects or anomalies from the IR images.

Accordingly, the present disclosure provides many advantages over prior art systems. For example, IR videos present a host of challenges as compared to traditional video analysis, due to the lack of contrast and increased noise causing difficulty in identifying unique features between images. Thus, an advantage of using a deep-learning based approach according to the present disclosure to automatically analyze IR images is that the underlying models can improve over time given more data. Further, the system of the present disclosure is configured to automatically compress large blade inspection video files into selected key image frames, thereby reducing computational cost for analyzing the data for defect or human visual inspection. Thus, the system of the present disclosure is configured to reduce storage requirements for the data and can easily generate reports for users.

Referring now to the drawings, <FIG> illustrates a perspective view of one embodiment of a wind turbine <NUM> according to the present disclosure. As shown, the wind turbines <NUM> includes a tower <NUM> extending from a support surface, a nacelle <NUM> mounted atop the tower <NUM>, and a rotor <NUM> coupled to the nacelle <NUM>. The rotor includes a rotatable hub <NUM> having a plurality of rotor blades <NUM> mounted thereon, which is, in turn, connected to a main rotor shaft that is coupled to the generator housed within the nacelle <NUM> (not shown). Thus, the generator produces electrical power from the rotational energy generated by the rotor <NUM>. It should be appreciated that the wind turbine <NUM> of <FIG> is provided for illustrative purposes only. Thus, one of ordinary skill in the art should understand that the invention is not limited to any particular type of wind turbine configuration.

Referring now to <FIG>, the wind turbine <NUM> may be part of a wind farm <NUM>. More particularly, <FIG>, illustrates a schematic diagram of one embodiment of a wind farm <NUM> containing a plurality of wind turbines <NUM> according to aspects of the present disclosure. The wind turbines <NUM> may be arranged in any suitable fashion. By way of example, the wind turbines <NUM> may be arranged in an array of rows and columns, in a single row, or in a random arrangement. Further, <FIG> illustrates an example layout of one embodiment of the wind farm <NUM>. Typically, wind turbine arrangement in a wind farm is determined based on numerous optimization algorithms such that AEP is maximized for corresponding site wind climate. It should be understood that any wind turbine arrangement may be implemented, such as on uneven land, without departing from the scope of the present disclosure.

As shown generally in the figures, each wind turbine <NUM> of the wind farm <NUM> may also include a turbine controller <NUM> communicatively coupled to a farm controller <NUM>. Moreover, in one embodiment, the farm controller <NUM> may be coupled to the turbine controllers <NUM> through a network <NUM> to facilitate communication between the various wind farm components. The wind turbines <NUM> may also include one or more sensors <NUM>, <NUM>, <NUM> configured to monitor various operating, wind, and/or loading conditions of the wind turbine <NUM>. For instance, the one or more sensors may include blade sensors for monitoring the rotor blades <NUM>; generator sensors for monitoring generator loads, torque, speed, acceleration and/or the power output of the generator; wind sensors <NUM> for monitoring the one or more wind conditions; and/or shaft sensors for measuring loads of the rotor shaft and/or the rotational speed of the rotor shaft. Additionally, the wind turbine <NUM> may include one or more tower sensors for measuring the loads transmitted through the tower <NUM> and/or the acceleration of the tower <NUM>. In various embodiments, the sensors may be any one of or combination of the following: accelerometers, pressure sensors, angle of attack sensors, vibration sensors, Miniature Inertial Measurement Units (MIMUs), camera systems, fiber optic systems, anemometers, wind vanes, Sonic Detection and Ranging (SODAR) sensors, infra lasers, Light Detecting and Ranging (LIDAR) sensors, radiometers, pitot tubes, rawinsondes, other optical sensors, and/or any other suitable sensors.

Referring now to <FIG>, there is illustrated a block diagram of one embodiment of suitable components that may be included within the farm controller <NUM>, the turbine controller(s) <NUM>, and/or other suitable controller according to the present disclosure. As shown, the controller(s) <NUM>, <NUM> may include one or more processor(s) <NUM> and associated memory device(s) <NUM> configured to perform a variety of computer-implemented functions (e.g., performing the methods, steps, calculations and the like and storing relevant data as disclosed herein). Additionally, the controller(s) <NUM>, <NUM> may also include a communications module <NUM> to facilitate communications between the controller(s) <NUM>, <NUM> and the various components of the wind turbine <NUM>. Further, the communications module <NUM> may include a sensor interface <NUM> (e.g., one or more analog-to-digital converters) to permit signals transmitted from one or more sensors <NUM>, <NUM>, <NUM> (such as the sensors described herein) to be converted into signals that can be understood and processed by the processors <NUM>. It should be appreciated that the sensors <NUM>, <NUM>, <NUM> may be communicatively coupled to the communications module <NUM> using any suitable means. For example, as shown, the sensors <NUM>, <NUM>, <NUM> are coupled to the sensor interface <NUM> via a wired connection. However, in other embodiments, the sensors <NUM>, <NUM>, <NUM> may be coupled to the sensor interface <NUM> via a wireless connection, such as by using any suitable wireless communications protocol known in the art.

As used herein, the term "processor" refers not only to integrated circuits referred to in the art as being included in a computer, but also refers to a controller, a microcontroller, a microcomputer, a programmable logic controller (PLC), an application specific integrated circuit, and other programmable circuits. Additionally, the memory device(s) <NUM> may generally include memory element(s) including, but not limited to, computer readable medium (e.g., random access memory (RAM)), computer readable non-volatile medium (e.g., a flash memory), a floppy disk, a compact disc-read only memory (CD-ROM), a magneto-optical disk (MOD), a digital versatile disc (DVD) and/or other suitable memory elements. Such memory device(s) <NUM> may generally be configured to store suitable computer-readable instructions that, when implemented by the processor(s) <NUM>, configure the controller(s) <NUM>, <NUM> to perform various functions as described herein.

Moreover, the network <NUM> that couples the farm controller <NUM>, the turbine controllers <NUM>, and/or the wind sensors <NUM> in the wind farm <NUM> may include any known communication network such as a wired or wireless network, optical networks, and the like. In addition, the network <NUM> may be connected in any known topology, such as a ring, a bus, or hub, and may have any known contention resolution protocol without departing from the art. Thus, the network <NUM> is configured to provide data communication between the turbine controller(s) <NUM> and the farm controller <NUM> in near real time.

Referring now to <FIG> and <FIG>, embodiments of a system <NUM> and method <NUM> for monitoring a rotor assembly of a wind turbine, such as one of the rotor <NUM>, the hub <NUM>, or one or more of the rotor blades <NUM> of the wind turbine <NUM>, are illustrated. More specifically, <FIG>, illustrates a schematic diagram of a system <NUM> for monitoring a rotor assembly of a wind turbine according to the present disclosure, whereas <FIG> illustrates a flow diagram of a method <NUM> for monitoring a rotor assembly of a wind turbine according to the present disclosure. In general, the system <NUM> includes a controller <NUM>, such as the farm controller <NUM>, the turbine controller(s) <NUM>, and/or other suitable controller according to the present disclosure. Further, as shown, the controller <NUM> may include an imaging analytics module <NUM> and a visualization module <NUM>, the functions of which are described in more detail below.

In general, as shown in <FIG>, the method <NUM> is described herein as implemented for monitoring the rotor assembly of the wind turbine <NUM> described above. However, it should be appreciated that the disclosed method <NUM> may be used to monitor any other rotor assembly or component having any suitable configuration. In addition, although <FIG> depicts steps performed in a particular order for purposes of illustration and discussion, the methods described herein are not limited to any particular order or arrangement. One skilled in the art, using the disclosures provided herein, will appreciate that various steps of the methods can be omitted, rearranged, combined and/or adapted in various ways.

As shown at (<NUM>), the method <NUM> includes receiving, via the imaging analytics module <NUM>, input data <NUM> containing thermal imaging data of any of the rotor assembly of the wind turbine <NUM> or rotor assembly of another wind turbine. For example, as shown in <FIG>, the input data/thermal imaging data <NUM> may include infrared imaging data having a plurality of image frames or videos. Thus, in certain embodiments, the method <NUM> may also include colleting the infrared imaging data by scanning the rotor assembly, e.g. via one or more infrared imaging devices <NUM> (such as an infrared camera). In particular embodiments, the data <NUM> may be collected using techniques described in <CIT> entitled "System and Method for Ground-Based Inspection of Wind Turbine Blades". Thus, the thermal imaging data may include one or more scans of the suction and pressure sides of the rotor assembly so as to cover the entirety of the blade(s) <NUM> from the blade root to the blade tip. Furthermore, the data <NUM> may be optionally stored in video format (e.g. within a memory store <NUM>) and then further processed by the imaging analytics module <NUM> to reduce the volume of data as described below.

More particularly and referring back to <FIG>, as shown at (<NUM>), the method <NUM> includes automatically identifying a plurality of sections of the rotor blade(s) <NUM> within the plurality of image frames until all sections of the rotor blade(s) <NUM> are identified. For example, in an embodiment, the plurality of sections of the rotor blade(s) <NUM> may include, for example, a blade root section, an intermediate blade section, or a blade tip section. In addition, as shown in <FIG>, the various sections may be automatically identified via a first machine learning model <NUM> of the imaging analytics module <NUM>. In such embodiments, the first machine learning model <NUM> may include a deep learning neural network. Thus, the first machine learning model <NUM> is configured to receive the video frames and automatically identify different components in the image frames. In various embodiments, the first machine learning model <NUM> can be learned in a supervised, semi-supervised or in an unsupervised fashion. Moreover, there may be several architectural variants of the deep learning neural network, any of which can be applied for the classification task. Once the algorithm identifies different components of the blade(s) <NUM> in the video frames, the method <NUM> may include aggregating image frames of the plurality of image frames from common sections of the plurality of sections until each of the plurality of sections is represented. For example, in one embodiment, if the infrared image frames contain scans of three rotor blades, after the analytics is done, the imaging analytics module <NUM> may have three sets of image frames containing all of the images belonging to all three blades, which can be aggregated based on the various sections of the rotor blade. The aggregation of the frames depends on the scanning protocol followed by operator to scan the wind turbine <NUM>.

In additional embodiments, the system <NUM> may also be able to automatically identify one or more components of the rotor blade(s) <NUM> within the plurality of image frames. Such components may include, for example, a blade root, a blade tip, a lug nut, a leading edge, a trailing edge, a pressure side, a suction side, or a maximum chord. For example, as shown in <FIG>, the imaging analytics module <NUM> may include a second machine learning model <NUM> for automatically identifying certain components of the rotor blade(s) <NUM>. In particular embodiments, for example, the second machine learning model <NUM> may automatically identify a blade axis of the rotor blade(s) <NUM> using the one or more components. More specifically, in an embodiment, the second machine learning model <NUM> may be configured to automatically identify the blade axis of the rotor blade(s) <NUM> using the one or more components by detecting a position of the lug nut or any other axis indicator (model may be adjusted to learn other nacelle and/or hub features) within the plurality of image frames and automatically identifying the blade axis of the rotor blade(s) <NUM> based on the position of the lug nut. Since the blade axis is defined by the position of the lug nut (or any other discernible features) on the wind turbine which defines the turbine axis relative to it, the second machine learning model <NUM> can determine which blade axis corresponds to the collected image frames.

It should be further understood that the various machine learning models described here may include one or more machine learning algorithms and may be part of a single model or multiple models. Moreover, the machine learning models described herein may include any suitable algorithm and/or statistical model (in addition to deep learning neural network), such as for example, stepwise linear regression. Generally, stepwise linear regression adds or removes features one at a time in an attempt to get the best regression model without over fitting. Further, stepwise regression typically has two variants, including forward and backward regression, both of which are within the scope of the invention. For example, forward stepwise regression is a step-by-step process of building a model by successive addition of predictor variables. At each step, models with and without a potential predictor variable are compared, and the larger model is accepted only if it leads to a significantly better fit to the data. Alternatively, backward stepwise regression starts with a model with all predictors and removes terms that are not statistically significant in terms of modeling a response variable.

Another statistical method that may be used to generate the machine learning models described herein may be an absolute shrinkage and selection operator (LASSO) algorithm. Generally, a LASSO algorithm minimizes the residual sum of squares subject to a constraint that the sum of the absolute value of the coefficients is smaller than a constant. Still another statistical algorithm that may be used to generate the model(s) is a M5 Prime (M5P) algorithm, which is a tree-based regression algorithm that is effective in many domains. For example, whereas stepwise linear regression produces a single global linear model for the data, tree based regression algorithms perform logical tests on features to form a tree structure. Generally, the M5P algorithm utilizes a linear regression model at each node of the tree, providing more specialized models. A machine learning model that necessarily includes direction may also be used along with the mean of the power ensemble group to determine entitlement (i.e., expectation of power). This can be considered an improvement over previous methods that filter data to specific direction sectors (which then form separate models for each sector). Other machine learning methods that may be used to generate the model(s) may also include Gaussian Process Models, Random Forest Models, Support Vector Machines, and/or a micro-service, which is discussed in more detail herein.

Referring still to <FIG>, after the model(s) <NUM>, <NUM> automatically identify the components and/or sections of the rotor blade(s) <NUM>, as shown at (<NUM>), the method <NUM> includes selecting, via a function <NUM> of the imaging analytics module <NUM>, a subset of image frames from the plurality of image frames. For example, as shown in <FIG>, the output of the first and/or second machine learning models <NUM>, <NUM> can be sent to a function module <NUM> containing the function <NUM> that determines the subset of data. Accordingly, the subset of image frames includes a minimum number of the plurality of image frames required to represent all sections of the rotor blade(s) <NUM>, e.g. without duplicate image frames or with a reduced amount of duplicate images. In such embodiments, the function <NUM> of the imaging analytics module <NUM> (i.e. used to select the subset of image frames) may include, for example, a metric function or an image stitching function. In such embodiments, the metric function may include a normalized cross correlation, a sum of an absolute difference, optical flow, a learned distance metric function, or any other suitable function. Further, in an embodiment, as shown in <FIG>, the method <NUM> may include training the function <NUM> via a machine learning algorithm <NUM>.

Thus, referring back to <FIG>, as shown at (<NUM>), the method <NUM> includes generating, via the visualization module <NUM>, an image <NUM> of the rotor assembly using the subset of image frames. It should be understood that the visualization module described herein may include a physical display monitor, a web application, or any suitable display means. Accordingly, as shown in <FIG>, the output data <NUM> of the visualization module <NUM> may include the image of the rotor assembly(s) <NUM>, which is described in more detail below.

More particularly, <FIG> illustrates a process flow diagram of one embodiment of the image stitching function <NUM> that can be used to generate the entire computer-generated image <NUM> of the rotor assembly in one image is illustrated. Further, the image stitching function <NUM> can be used to determine the overlap between image frames and subsequently to decide whether a given image frame is a keyframe or not is illustrated. As shown, the image stitching function <NUM> is configured to learn the mapping of pixels from one image to another, thereby giving the information about the transformation the image undergoes during scanning. These can be used to decide whether sufficient movement is made by the operator to call out new image as the keyframe or discard it. More particularly, as shown in <FIG>, a plurality of image frames are collected for various sections of the rotor blade(s) <NUM> (e.g. frame nx, frame nx-<NUM>, and so on). As shown at <NUM> and <NUM>, the plurality of image frames can then be preprocessed and registered. For example, during registration, the image frames can be stitched together using an enhanced correlation-based registration approach. More particularly, as shown at <NUM>, each image frame can be registered to the frame before it and then added cumulatively to all frames that were previously registered. Once this process is complete for all of the image frames (as shown at <NUM>), the entire rotor blade can be visible in one stitched image (<NUM>). Since the defects are detected on individual frames, and the transformation matrix is known between each frame, the defect location on each frame is also known globally with respect to the entire wind turbine blade, which is further explained herein.

In addition, the method <NUM> may also include training the machine learning models <NUM>, <NUM> described herein over time using the input data and/or output data <NUM>, <NUM> of the imaging analytics module <NUM>. For example, in general, there may be two parts of training, referred to herein as Phase I and Phase II. During Phase I, an initial set of training data is used to learn or develop and learn the machine learning models <NUM>, <NUM>. During Phase II, once the models <NUM>, <NUM> start producing the output data, which can be visualized by an inspector and edited for mistakes, the annotated or edited data/images can be input into the machine learning models <NUM>, <NUM> to continuously improve the models <NUM>, <NUM> over time. More specifically, during Phase II, a human annotator can the annotate the output data <NUM> that can be input back into the imaging analytics module <NUM>. As used herein, annotation in machine learning generally refers to a process of labelling data in a manner that can be recognized by machines or computers. Furthermore, such annotation can be completed manually by humans as human annotators generally better interpret subjectivity, intent, and ambiguity within the data. Thus, machines can learn from the annotated data by recognizing the human annotations over time. In some cases, annotation can be learned by artificial intelligence and/or other algorithms, such as semi-supervised learning or clustering, as well as any other suitable accurate labeling process. Accordingly, as shown in <FIG>, the annotated output data <NUM> can then be fed into the model(s) <NUM>, <NUM> for training and/or correcting. In other cases, unsupervised learning methods, such as gaussian mixture model, sparse reconstruction or Neural network based autoencoders/generative adversarial network (GANs), may also be employed where algorithms learning a normal distribution of the data and can be used to flag the ones which are anomalous. It should also be understood that the machine learning models <NUM>, <NUM> described herein may be trained via Phase I or Phase II only, rather than both.

In other words, the imaging analytics module <NUM> may include a supervised machine learning algorithm that can apply what has been learned in the past to new data using labeled data. Starting from the model build, the learning algorithm produces an inferred function to make predictions about the output values. As such, the imaging analytics module <NUM> is able to provide targets for any new input after sufficient training. The learning algorithm can also compare its output with the correct, intended output and find errors in order to modify the model accordingly.

Referring back to <FIG>, as shown at (<NUM>), the method <NUM> may include monitoring the computer-generated image <NUM> of the rotor assembly for anomalies on the rotor assembly. Such monitoring may be further understood with reference to <FIG> and <FIG> described herein. Thus, as shown at (<NUM>), the method <NUM> may include implementing a control action when at least one anomaly is detected. In one embodiment, for example, the control action may include generating an alarm. It should be understood that the control action as described herein may further encompass any suitable command or constraint by the controller <NUM>. For example, in several embodiments, the control action may include temporarily de-rating or up-rating the wind turbine <NUM>.

Up-rating or de-rating the wind turbine <NUM> may include speed up-rating or de-rating, torque up-rating or de-rating or a combination of both. Further, as mentioned, the wind turbine <NUM> may be uprated or de-rated by pitching one or more of the rotor blades <NUM> about its pitch axis. The wind turbine <NUM> may also be temporarily up-rated or de-rated by yawing the nacelle <NUM> to change the angle of the nacelle <NUM> relative to the direction of the wind. In further embodiments, the controller <NUM> may be configured to actuate one or more mechanical brake(s) in order to reduce the rotational speed of the rotor blades <NUM>. In still further embodiments, the controller <NUM> may be configured to perform any appropriate control action known in the art. Further, the controller <NUM> may implement a combination of two or more control actions.

Referring now to <FIG> and <FIG>, further embodiments of a system <NUM> and a method <NUM> for monitoring a rotor assembly of a wind turbine, such as one of the rotor <NUM>, the hub <NUM>, or one or more of the rotor blades <NUM> of the wind turbine <NUM>, are illustrated. More specifically, <FIG> illustrates a schematic diagram of a system <NUM> for monitoring a rotor assembly of a wind turbine according to the present disclosure, whereas <FIG> illustrates a flow diagram of a method <NUM> for monitoring a rotor assembly of a wind turbine according to the present disclosure. In general, the system <NUM> may include any of the components illustrated in <FIG>. Thus, as shown, the system <NUM> may include, at least, a controller <NUM>, such as the farm controller <NUM>, the turbine controller(s) <NUM>, and/or other suitable controller according to the present disclosure. Further, as shown, the controller <NUM> may include an imaging analytics module <NUM> and a visualization module <NUM>, the functions of which are described in more detail below. Remaining components of the system <NUM> are further explained below, with discussion of the method <NUM>.

As shown at (<NUM>), the method <NUM> includes, during a model-building time period, receiving, via the imaging analytics module <NUM>, the input data <NUM> described herein. As shown at (<NUM>), the method <NUM> includes automatically identifying, via a machine learning model <NUM> of the imaging analytics module <NUM>, at least one anomaly and associated anomaly category information using the input data <NUM>. In an embodiment, for example, the machine learning model <NUM> may include a deep learning neural network. Thus, in certain embodiments, the deep learning neural network may include a Convolution Neural Network (CNN) having an encoder-decoder architecture configured to implement semantic segmentation. Accordingly, the encoder-decoder architecture may be utilized for a semantic segmentation task. More particularly, the encoder network structure can learn the appropriate representation required to solve the given task while the decoder structure can combine the lower-level and higher-level representations to make a prediction. In on example, the prediction can be the probability map of each pixel belonging to a defect category. In another example, the first encoder-decoder structure can predict defect versus non-defect for each pixel and those pixels which were recognized as a defect can pass through another network for further classification into defect categories.

As shown at (<NUM>), the method <NUM> includes generating, via the imaging analytics module <NUM>, output data <NUM> comprising pixel information that corresponds to the anomaly(ies) and the associated anomaly category information. For example, the output data <NUM> may include the pixels that are associated with the anomaly(ies), including size, shape, concentrations, etc. Further, the category information may include, for example, a type or severity of the anomaly(ies). Moreover, in additional embodiments, the method <NUM> may include combining pixels of adjacent regions belonging to a common anomaly category into a single anomaly region. Thus, in such embodiments, the method <NUM> may include fitting at least one boundary (such as one or more polygons) to the single anomaly region using at least one of connected components or convex hull fitting and displaying the boundary via the visualization module <NUM>. Example boundaries <NUM> fitted to a plurality of defect regions are further illustrated in <FIG>.

As shown at (<NUM>), during a model-implementing time period, the method <NUM> includes training the machine learning model <NUM> over time using the input data <NUM> and output data <NUM>. Such training may include, for example, annotating the pixel information that corresponds to the anomaly and the associated anomaly category information over time, which may be completed by an expert operator. As such, in an embodiment, the operators can mark the pixels that belong to the defects/anomalies and their corresponding categories. Thus, the machine learning model <NUM> can be trained to predict defect pixel locations and the corresponding categories as accurately as possible to humans.

Further, as shown, the annotated output data <NUM> can then be stored in memory store <NUM>. In particular embodiments, the imaging analytics module <NUM> may thus be configured to generate a probability map <NUM> for the pixel information (e.g. a probability map for each pixel belonging to a defect category). As used herein, the probability map <NUM> generally includes probabilities that each pixel actually corresponds to a particular anomaly and the associated anomaly category information. Accordingly, a higher probability indicates higher chance of that a pixel belongs to that defect category. Thus, in certain embodiments, the method <NUM> may include marking the pixel information <NUM> with a confidence level above a certain threshold based on the probability map <NUM>, thereby creating a confidence map <NUM> of the anomaly(ies) and the associated anomaly category information. As such, during deployment of the model <NUM>, each pixel is associated with the highest probability category predicted for it by the network. Accordingly, the network can be either be directly trained to predict the defect pixel location and its category, or first trained to predict whether a pixel belongs to a defect versus a non-defect category and then each defect pixel classified to its corresponding defect category.

Referring to <FIG>, as shown at (<NUM>), the method <NUM> includes generating, via the visualization module <NUM>, an image of the rotor assembly, e.g. using the trained machine learning model <NUM>. For example, in several embodiments, the image of the rotor assembly may be generating using an image stitching function <NUM> to stitch together the plurality of image frames. For example, an example computer-generated image <NUM> of the rotor assembly is provided in <FIG>. Accordingly, in an embodiment, the method <NUM> may include monitoring the rotor assembly for anomalies using the computer-generated image <NUM> of the rotor assembly. As such, the computer-generated image of the rotor assembly is configured to display an anomaly location and the associated anomaly category information overlaid as layer on the image of the rotor assembly and implementing a control action when at least one anomaly is detected.

More particularly, as shown, the computer-generated image <NUM> of the rotor assembly may be displayed on an interactive user interface <NUM> that allows a user to interact with the image, such as via touch-screen technology having one or more selectable buttons <NUM> or a user device, such as a mouse or keyboard, that allows a user to select various options. For example, in an embodiment, the method <NUM> may include, in response to receiving a selection of the boundary <NUM>, displaying the associated anomaly category information and the pixel information via the visualization module <NUM>. Further, in an embodiment, the method <NUM> may include receiving at least command via the visualization module <NUM>. For example, such commands may include removing or modifying the boundary or changing the associated anomaly category information.

Claim 1:
A method for monitoring at least one rotor assembly of a wind turbine, the rotor assembly comprising at least one of a rotatable hub and at least one rotor blade, the method comprising:
Receiving (<NUM>), via an imaging analytics module of a controller, thermal imaging data of the rotor assembly, the thermal imaging data comprising a plurality of image frames;
automatically identifying (<NUM>), via a first machine learning model of the imaging analytics module, a plurality of sections of the rotor assembly within the plurality of image frames until all sections of the rotor blade are identified;
selecting (<NUM>), via a function of the imaging analytics module, a subset of image frames from the plurality of image frames, the subset of image frames comprising a minimum number of the plurality of image frames required to represent all sections of the rotor blade;
generating (<NUM>), via a visualization module of the controller, an image of the rotor assembly using the subset of image frames.