A method for intelligent detection of PDC bit damage

Abstract

This invention belongs to the field of drill bit damage detection technology, and specifically to a PDC drill bit damage intelligent detection method. It includes the following steps: S1: Data construction and preprocessing; S2: Comprehensive data augmentation; S3: Feature extraction and fusion; S4: Model training and optimization; S5: Model application and inference. This invention effectively solves the problem of missed detections caused by extreme sample imbalance. The loss curves of the model training and validation sets are highly consistent, and the convergence state is good, proving that it has strong generalization ability. It can effectively cope with the complex and variable imaging environment of real well sites, fully meeting the needs of real-time on-site monitoring. It provides an efficient, objective, and repeatable technical means for optimizing drilling parameters, predicting the remaining life of drill bits, and avoiding major safety accidents, and has significant industrial application potential in the field of intelligent drilling.

Description

Technical Field

[0001] This invention relates to the field of drill bit damage detection technology, specifically to a PDC drill bit damage intelligent detection method. Background Technology

[0002] Drill bit damage assessment is a core research component in drilling engineering for oil, gas, and geothermal resource development. As a key component for rock breaking, the performance of PDC (Polarized Die) drill bits directly impacts drilling efficiency, cost control, and operational safety. With exploration extending to deeper and more complex formations, accurate analysis of drill bit condition is crucial for optimizing drilling parameters, predicting remaining life, and preventing major safety accidents. Currently, the mainstream method for drill bit damage assessment still relies on manual inspection by field personnel. This method typically follows the International Association of Drilling Contractors (IADC) coding system, where professionals subjectively assess the wear and fracture patterns of the cutting teeth based on experience. However, this traditional method has significant limitations: First, the assessment results are highly dependent on the inspector's expertise, leading to strong subjectivity and often differing ratings for the same drill bit among different personnel; second, manual inspection is inefficient, and the current IADC standards are outdated in addressing new drill bits and complex damage patterns, failing to meet the refined and intelligent application requirements of modern intelligent drilling systems.

[0003] To overcome the drawbacks of manual assessment, intelligent monitoring technology has been gradually introduced into the drilling field. Existing technological approaches can be mainly summarized into the following two categories:

[0004] (1) Methods based on physical signal analysis: Early explorations mainly involved inferring the wear state of the drill bit indirectly by monitoring signals such as vibration, sound waves, and motor current during the drilling process in real time. For example, some technical solutions identify drill bit eddy current phenomena by analyzing vibration spectrum characteristics. However, such methods are highly susceptible to strong interference from complex downhole environmental noise and formation changes, and have stringent requirements for the specifications of the acquisition equipment. The signal processing process is also complex, resulting in poor robustness and universality in practical applications.

[0005] (2) Deep learning-based image detection methods: With the development of computer vision, models represented by convolutional neural networks, such as the YOLO series, are directly applied to damage detection of drill bit images. For example, some studies have used YOLOv3 or YOLOv7 algorithms to achieve effective inference of cutting tooth damage, while others have used multi-stage decoupling strategies combined with YOLOv10 for wear level classification. However, these general object detection models face specific bottlenecks when applied to drill bit damage detection tasks: 1) Difficulty in multi-scale feature extraction: Drill bit damage morphology spans a huge range, from micron-level thermal damage network cracks to decimeter-level large-area matrix damage. Existing general architectures are difficult to balance sensitivity to minute damage and understanding of global morphology. 2) Low contrast and environmental interference: The on-site shooting environment is harsh, with uneven lighting, oil stains, and limited shooting angles, which often leads to extremely low distinction between key features, such as thermal damage and initial abrasion, and insufficient robustness of the model in complex backgrounds. 3) Extremely imbalanced sample: In real-world scenarios, there are very few rare samples such as thermal damage. Traditional models, lacking specific design, generally have low accuracy in recognizing such difficult samples, which can easily lead to serious missed detections.

[0006] In summary, existing technologies, whether physical signal analysis or general-purpose deep learning models, cannot perfectly and accurately represent the complex multi-scale damage characteristics of drill bits, and their performance is limited when dealing with environmental interference and extreme data distributions in real-world industrial scenarios. Therefore, developing a deep learning method with feature extraction capabilities, high multi-scale feature localization accuracy, and strong environmental adaptability is an urgent need to promote the intelligent development of deep drilling. Summary of the Invention

[0007] The purpose of this section is to outline some aspects of the embodiments of the present invention and to briefly describe some preferred embodiments. Simplifications or omissions may be made in this section, as well as in the abstract and title of this application, to avoid obscuring the purpose of these documents; however, such simplifications or omissions should not be construed as limiting the scope of the invention.

[0008] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution: a PDC drill bit damage intelligent detection method, comprising the following steps:

[0009] S1: Data Construction and Preprocessing: The acquired PDC drill bit images are filtered, classified and labeled, and resolution normalization is performed.

[0010] S2: Comprehensive Data Augmentation: Performs Mosaic enhancement, Mixup enhancement, geometric transformation, and optical distortion operations on the preprocessed image;

[0011] S3: Feature Extraction and Fusion: The image is input into a dynamic adaptive extraction backbone network to extract multi-dimensional features. Then, a bidirectional path fusion neck network is used to perform top-down and bottom-up bidirectional path feature flow and deep fusion. The dynamic adaptive extraction backbone network adopts a hybrid architecture design to extract multi-scale features from tiny thermal damage cracks to large-area mechanical fractures. The bidirectional path fusion neck network is based on a bidirectional path fusion module as the neck network. By constructing a top-down and bottom-up bidirectional feature flow mechanism, it deeply fuses multi-scale features from different levels of the backbone network.

[0012] S4: Model Training and Optimization: The RT-DETR detection head is integrated, and a function that combines localization, classification, and regression losses is used to guide parameter optimization. The validation set metrics are used to determine whether the model meets engineering standards. The RT-DETR detection head achieves high-precision damage localization and classification by decoupling the prediction task, and damage prediction is directly achieved using the Transformer decoder architecture.

[0013] S5: Model Application and Inference: After normalizing the image to be detected, forward inference is performed to output visual detection results in real time, including damage category name, confidence level and prediction box position.

[0014] As a preferred embodiment of the PDC drill bit damage intelligent detection method described in this invention, the core components of the dynamically adaptive extraction backbone network include: a partial channel attention convolution module, an efficient multi-scale attention module, and global modeling and multi-scale enhancement.

[0015] As a preferred embodiment of the PDC drill bit damage intelligent detection method described in this invention, the partial channel attention convolution module includes:

[0016] Channel segmentation and dynamic weighting: For the input feature map, it is first segmented along the channel dimension into the part to be convolved and the invariant identity part. Then, a lightweight channel attention function is used to generate dynamic channel weights for the convolved part.

[0017]

[0018] in, For dynamic channel weights, It is the Sigmoid activation function. Implemented by global average pooling and two 1×1 convolutional layers. The part of the input feature map that has been convolved;

[0019] Feature enhancement and fusion: The generated weights are applied to The weighted feature map is obtained. ,

[0020]

[0021] in, This is the feature map after channel attention weighting. The part of the input feature map that has undergone convolution processing. For channel-by-channel multiplication operators, Dynamic channel weights;

[0022] Subsequently Perform 3×3 convolution feature extraction, concatenate it with the invariant input feature map along the channel dimension, and then perform cross-channel information fusion through 1×1 convolution to obtain the output:

[0023]

[0024] in, This is the final output feature map of the partial channel attention convolutional module. This is the feature map after channel attention weighting. For input feature maps that remain unchanged, For spatial feature extraction operators, For splicing operations, This is a cross-channel information fusion operator.

[0025] As a preferred embodiment of the PDC drill bit damage intelligent detection method described in this invention, the efficient multi-scale attention module is used to introduce a cross-spatial learning mechanism. The efficient multi-scale attention module performs 1D global average pooling along the height and width dimensions in two parallel branches to encode global spatial information. By modeling the interaction relationship between the height and width dimensions, it generates corresponding attention weight vectors. and Ultimately, this achieves the calibration of the characteristic response:

[0026]

[0027] in, This is the final output feature map of the efficient multi-scale attention module. These are the input features after grouping. and These are the height and width attention maps obtained through cross-space learning, respectively.

[0028] As a preferred embodiment of the PDC drill bit damage intelligent detection method described in this invention, the global modeling and multi-scale enhancement utilize a C2f attention module to model global contextual relationships at a deep network layer, and establish long-distance dependencies through a self-attention mechanism to help the model distinguish between large-area damage and damage occurring in specific locations. The fast spatial pooling pyramid module connected at the network end captures multi-scale contextual information through a series of max pooling layers, enhancing the model's robustness to damage targets of different sizes.

[0029] As a preferred embodiment of the PDC drill bit damage intelligent detection method described in this invention, the bidirectional path fusion neck network includes:

[0030] 1) Input feature preprocessing: The bidirectional path fusion neck network receives three feature layers S2, S3 and S4 with different resolutions from the backbone network output. First, the number of channels of each input layer is uniformly projected into fixed-dimensional feature maps P2, P3 and P4 through a 1×1 convolutional layer.

[0031] 2) Two-way fusion path design: This module contains two complementary information flow paths: Top-down semantic transmission path: High-level feature P4 contains rich semantic information. After its spatial size is expanded through upsampling, it enters the feature fusion module and combines with the mid-level feature P3 to generate the intermediate feature F3. Similarly, F3 continues to be transmitted down and fused with P2, so that the shallow features obtain the global semantic background required to identify the damage category; Bottom-up localization enhancement path: Based on semantic transmission, the low-level spatial feature F2, which contains strong localization information, is transmitted back through downsampling and fused with F3 to generate O3, and finally converges to the top level to generate O4.

[0032] 3) Node fusion logic: Each fusion node in the bidirectional path fusion neck network is the core of achieving deep integration. It receives high-level features and low-level features, processes them through specific fusion operators, and achieves deep interaction between feature dimensions and spatial information through parallel convolution and feature concatenation.

[0033] 4) Multi-scale output convergence: The output features of each level after bidirectional enhancement are globally integrated through a splicing operation to generate a high-quality feature map with both high-resolution spatial details and rich semantic expression. This map is then fed into the subsequent RT-DETR detection head to perform end-to-end prediction.

[0034] As a preferred embodiment of the PDC drill bit damage intelligent detection method described in this invention, the RT-DETR detection head integrates an IoU-aware query selection mechanism and a Transformer decoder. The IoU-aware query selection mechanism dynamically selects the initial query point most likely to contain the damage target based on the multi-scale encoding features output by the neck network. After the query point enters the decoder, the Transformer decoder continuously extracts damage details from the feature map through a multi-layer self-attention and cross-attention mechanism, and iteratively optimizes the category and location of the damage area.

[0035] In a preferred embodiment of the PDC drill bit damage intelligent detection method described in this invention, in step S4, a joint loss function is used for guided training:

[0036]

[0037] in, For the total joint loss, , , This is the weighting balance coefficient. Locate the loss for IoU. For classifying losses, The bounding box coordinate regression loss is used, and the classification loss is evaluated using the cross-entropy loss function for the seven damage categories. The calculation formula is as follows:

[0038]

[0039] in, This represents the number of samples in the current training batch. The total number of damage categories as defined. For the sample In category The real labels on Predict samples for the model Category The probability of bounding box coordinate regression loss is used to constrain the coordinates of the center point and the width and height of the bounding box using the L1 norm, ensuring that the predicted box still has good fit in challenging contexts.

[0040] Compared with the prior art, the beneficial effects of the present invention are: (1) significantly improving detection accuracy and class balance: the model proposed in this invention is effective on drill bit damage datasets. Reaching 0.649, With an accuracy of 0.463, this model surpasses mainstream target detection models such as RT-DETR, YOLOv8, YOLOv10, and YOLOv12 across all performance metrics. Particularly when dealing with rare and extremely difficult-to-detect micro-damage categories such as thermal damage, the model's average accuracy is significantly improved compared to the baseline model, effectively solving the problem of missed detections caused by extreme sample imbalance.

[0041] (2) Excellent environmental robustness and generalization ability: Through visualization comparison verification under different light intensities, shooting angles and complex backgrounds, especially under extreme conditions such as dim and uneven lighting, this invention is the only model that can accurately detect low-contrast features and accurately locate them. The loss curves of the model training and validation sets are highly consistent and converge well, proving that it has strong generalization ability and can effectively cope with the complex and ever-changing imaging environment of real well sites.

[0042] (3) The feature representation has engineering logic rationality: The dynamic adaptive extraction backbone network and bidirectional path fusion neck network fusion architecture designed in this invention realize the collaborative extraction of local and global features, which can accurately capture features with geological engineering significance. Ablation experiments have shown that each innovative module makes a significant contribution to performance improvement, and the prediction box is highly consistent with the actual boundary of the damage area, proving that the detection logic of the model is consistent with the identification criteria of drilling experts, thus enhancing the credibility of the detection results.

[0043] (4) Achieving an efficient and real-time intelligent evaluation process: The intelligent detection system constructed in this invention transforms the traditional evaluation process, which relies on human experience, is highly subjective, and inefficient, into a standardized intelligent process. The model inference speed reaches 87.43 FPS, which fully meets the needs of real-time on-site monitoring. It provides an efficient, objective, and repeatable technical means for optimizing drilling parameters, predicting the remaining life of the drill bit, and avoiding major safety accidents, and has significant industrial application potential in the field of intelligent drilling. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0045] Figure 1 This is a flowchart of an intelligent detection method for PDC drill bit damage according to the present invention;

[0046] Figure 2 This is a flowchart of the dynamic adaptive extraction backbone network of the PDC drill bit damage intelligent detection method of the present invention;

[0047] Figure 3 This is a flowchart of the bidirectional path fusion neck network of the PDC drill bit damage intelligent detection method of the present invention;

[0048] Figure 4 This is a schematic diagram of the detection results of the PDC drill bit damage intelligent detection method of the present invention. The left figure shows the detection results of thermal damage and eddy current damage under extremely dim lighting conditions, while the right figure shows the comprehensive detection results of corrosion and gauge-keeping damage under normal working conditions. Detailed Implementation

[0049] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0050] Secondly, the present invention is described in detail with reference to the schematic diagrams. When detailing the embodiments of the present invention, for ease of explanation, the cross-sectional views illustrating the device structure may be partially enlarged, not according to the usual scale. Furthermore, the schematic diagrams are merely examples and should not limit the scope of protection of the present invention. In addition, actual fabrication should include three-dimensional spatial dimensions of length, width, and depth.

[0051] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0052] This invention proposes an intelligent detection method for PDC drill bit damage. The core idea is to construct a deep learning architecture, utilize a dynamically adaptive extraction backbone network optimized for drill bit damage to extract local texture and global context features, and combine it with a bidirectional path fusion neck network to achieve high-precision fusion of multi-scale features. Finally, through real-time detection head and joint loss function, robust detection of multi-type and cross-scale drill bit damage under complex working conditions is achieved.

[0053] System overall process: The system of the present invention consists of a data construction and preprocessing module, a data augmentation module, a feature extraction and fusion module, a model training and evaluation module, and a model application module.

[0054] Specifically, such as Figure 1 The aforementioned intelligent detection method for PDC drill bit damage includes the following steps:

[0055] S1: Data Construction and Preprocessing: The acquired PDC drill bit images are filtered, classified and labeled, and resolution normalization is performed.

[0056] S2: Comprehensive Data Augmentation: Perform operations such as Mosaic enhancement, Mixup enhancement, geometric transformation and optical distortion on the preprocessed image to improve the model's perception of rare damage and environmental robustness;

[0057] S3: Feature Extraction and Fusion: The image input is dynamically and adaptively extracted by the backbone network to extract multi-dimensional features, and then the bidirectional path fusion neck network is used to perform bidirectional path feature flow and deep fusion from top to bottom and from bottom to top.

[0058] S4: Model Training and Optimization: Integrating the RT-DETR detector head, using a function that combines localization, classification, and regression losses to guide parameter optimization, and using validation set metrics to determine whether the model meets engineering standards;

[0059] S5: Model Application and Inference: After normalizing the image to be detected, forward inference is performed to output visual detection results in real time, including damage category name, confidence level and prediction box position.

[0060] Detailed explanation of each core module:

[0061] (1) Dynamic adaptive extraction of backbone network (e.g.) Figure 2 (as shown)

[0062] The dynamically adaptive backbone network employs a hybrid architecture design, aiming to efficiently extract multi-scale features ranging from minute thermal damage cracks to large-area mechanical fractures. Its core components include:

[0063] 1) Partial Channel Attention Convolution Module: To address the computational redundancy and insufficient feature representation capability of traditional convolution when extracting drill bit damage features, this invention introduces partial channel attention convolution.

[0064] ① Channel Segmentation and Dynamic Weighting: For the input feature map, it is first segmented along the channel dimension into the convolutional part and the invariant identity part. This invention generates dynamic channel weights for the convolutional part using a lightweight channel attention function:

[0065]

[0066] in, For dynamic channel weights, It is the Sigmoid activation function. Implemented by global average pooling and two 1×1 convolutional layers. This refers to the part of the input feature map that has undergone convolution processing.

[0067] ② Feature enhancement and fusion: The generated weights are applied to The weighted feature map is obtained. :

[0068]

[0069] in, This is the feature map after channel attention weighting. The part of the input feature map that has undergone convolution processing. For channel-by-channel multiplication operators, For dynamic channel weights.

[0070] Subsequently Perform 3×3 convolution feature extraction, concatenate it with the invariant input feature map along the channel dimension, and then perform cross-channel information fusion through 1×1 convolution to obtain the output:

[0071]

[0072] in, This is the final output feature map of the partial channel attention convolutional module. This is the feature map after channel attention weighting. For input feature maps that remain unchanged, For spatial feature extraction operators, For splicing operations, This is a cross-channel information fusion operator.

[0073] 2) Highly efficient multi-scale attention module:

[0074] To capture long-range dependent features such as directional spiral scratches, a dynamically adaptive backbone network integrates an efficient multi-scale attention module to introduce a cross-spatial learning mechanism. The efficient multi-scale attention module performs 1D global average pooling along the height and width dimensions in two parallel branches to encode global spatial information. By modeling the interaction between the height and width dimensions, corresponding attention weight vectors are generated. and Ultimately, this achieves the calibration of the characteristic response:

[0075]

[0076] in, This is the final output feature map of the efficient multi-scale attention module. These are the input features after grouping. and These are the height and width attention maps obtained through cross-spatial learning. This mechanism enables the model to accurately track and identify crack paths and irregular damage morphologies.

[0077] 3) Global modeling and multi-scale augmentation:

[0078] At the network depth, this invention utilizes a C2f attention module to model global contextual relationships and establishes long-distance dependencies through a self-attention mechanism, helping the model distinguish between large-area damage and damage occurring in specific locations. The fast spatial pooling pyramid module at the network's end captures multi-scale contextual information through concatenated max-pooling layers, significantly enhancing the model's robustness to damage targets of different sizes.

[0079] (2) Bidirectional path fusion neck network (e.g.) Figure 3 (as shown)

[0080] This invention designs a bidirectional path fusion module as the neck network. The core idea of ​​this module is to construct a top-down and bottom-up bidirectional feature flow mechanism to deeply fuse multi-scale features from different levels of the backbone network, aiming to solve the problems of low edge localization accuracy and disconnect between semantic information and spatial details in traditional detection networks when dealing with drill bit damage.

[0081] Input feature preprocessing: The bidirectional path fusion neck network receives three feature layers S2, S3, and S4 with different resolutions from the backbone network output. To ensure that the features at different levels can be mathematically processed, the number of channels in each input layer is first projected into fixed-dimensional feature maps P2, P3, and P4 through 1×1 convolutional layers, laying the foundation for subsequent bidirectional path fusion.

[0082] The bidirectional fusion path design includes two complementary information flow paths: ① Top-down semantic transmission path: High-level feature P4, containing rich semantic information, is expanded in spatial size through upsampling and then combined with mid-level feature P3 in the feature fusion module to generate intermediate feature F3. Similarly, F3 continues to be transmitted down and fused with P2, enabling shallow features to obtain the global semantic background required for identifying damage categories. ② Bottom-up localization enhancement path: Based on semantic transmission, low-level spatial feature F2, containing strong localization information, is retransmitted through downsampling and fused with F3 to generate O3, which is then finally converged at the top level to generate O4. This mechanism back-complements the deep semantic features with precise spatial details, ensuring the model's accurate localization of damage of various sizes.

[0083] 3) Node Fusion Logic: Each fusion node in the bidirectional path fusion neck network is the core of achieving deep integration. This module receives high-level and low-level features and processes them through specific fusion operators. This structure achieves deep interaction between feature dimensions and spatial information through parallel convolution and feature concatenation, ensuring that fine damage occurring on the material bonding surface, such as interface wear, can obtain extremely accurate boundary delineation. 4) Multi-Scale Output Convergence: Finally, the output features of each level after bidirectional enhancement are globally integrated through concatenation operations to generate a high-quality feature map with both high-resolution spatial details and rich semantic expression. This map is then fed into the subsequent RT-DETR detection head to perform end-to-end prediction.

[0084] (3) Detection head:

[0085] This invention integrates a detection head based on the RT-DETR architecture in the model prediction output stage, achieving high-precision damage localization and classification by decoupling the prediction task. This detection head abandons the complex post-processing steps in traditional target detection, directly implementing damage prediction using a Transformer decoder architecture.

[0086] The predictive head architecture employs an IoU-aware query selection mechanism: unlike traditional models that use fixed queries, this mechanism dynamically selects the initial query points most likely containing the damage target based on the multi-scale encoded features output by the neck network. This approach significantly reduces the computational load on subsequent decoders and is crucial for enabling real-time system operation.

[0087] Transformer Decoder: After the query point enters the decoder, it continuously extracts damage details from the feature map through a multi-layer self-attention and cross-attention mechanism, and iteratively optimizes the category and location of the damage region.

[0088] 3) Detection Process: Since the prediction result set output by the decoder is highly sparsity and non-redundant, this invention completely removes the non-maximum suppression post-processing step required in traditional object detection. This not only simplifies the inference process and improves processing speed, but also effectively avoids the problem of missed detection of small targets due to improper manual setting of the IoU threshold.

[0089] (4) Model training and optimization:

[0090] To achieve highly sensitive detection of complex and minute damage, this invention employs a joint loss function for guided training:

[0091]

[0092] in, For the total joint loss, , , This is the weighting balance coefficient. Locate the loss for IoU. For classifying losses, The bounding box coordinate regression loss is used. The classification loss is evaluated using the cross-entropy loss function for the seven damage categories, calculated as follows:

[0093]

[0094] in, This represents the number of samples in the current training batch. The total number of damage categories as defined. For the sample In category The real label on it. Predict samples for the model Category The probability of the bounding box coordinate regression loss is calculated. The L1 norm is used to constrain the coordinates of the center point and the width and height of the bounding box, ensuring that the predicted box still has good fit even in challenging contexts.

[0095] Example:

[0096] Comprehensive Implementation Example of PDC Drill Bit Damage Real-time Detection System Based on End-to-End Architecture:

[0097] Composition: This embodiment provides a workflow for intelligent damage detection of PDC drill bits. First, post-operation images of the PDC drill bit are acquired at the drilling site and normalized to a resolution of 640×640 pixels. Then, the images are input into a dynamically adaptive extraction backbone network, which uses a partial channel attention convolution module for dynamic weighting and combines it with an efficient multi-scale attention module to capture long-distance spatial dependencies, thereby extracting multi-scale features ranging from micro-cracks to large-area fractures. Next, the extracted features enter a bidirectional path fusion neck network, where deep fusion of high and low-level features is achieved through top-down semantic transfer and bottom-up spatial detail supplementation. Finally, the high-quality fused feature map is fed into the RT-DETR detection head, which uses an IoU-aware query selection mechanism and a Transformer decoder to directly output results including category, confidence level, and predicted bounding box position.

[0098] Effects: After implementing this example, the system can simultaneously and accurately identify seven typical types of damage, including corrosion, diameter maintenance damage, and thermal damage. Testing showed that the model achieved a mAP0.5 of 0.649 and a combined mAP0.5-0.95 of 0.463, with an inference speed of 87.43 FPS, balancing accuracy and speed, fully meeting the needs of real-time assessment in industrial settings. Figure 4As shown in the right figure, in a visual perception scenario under normal working conditions, this system can accurately identify the type of corrosion and gauge-keeping damage on the drill bit surface and fit the bounding box with high precision, fully demonstrating the excellent performance of the model in multi-scale, multi-category composite damage detection.

[0099] Examples of highly robust testing under complex and extremely harsh lighting conditions:

[0100] Composition: In actual well sites or future downhole in-situ visual perception scenarios, illumination is typically extremely weak, uneven, and accompanied by oil contamination, resulting in a very low image signal-to-noise ratio. This embodiment incorporates optical distortion during the data preprocessing stage to simulate harsh working conditions. In the deep network for feature extraction, the self-attention mechanism of the C2f attention module is used to establish a global contextual perspective, preventing the model from being misled by local reflections or single rust spots. Simultaneously, a reverse fusion mechanism combining the low-level spatial features in the bidirectional path fusion neck network ensures that extremely high-precision damage delimitation can still be provided even in complex backgrounds.

[0101] Results: In real-world testing under extremely low light, uneven illumination, and complex viewing angles, general-purpose target detection models (such as the entire YOLO series) lost their ability to detect minute thermal damage and generated numerous false bounding boxes. However, the system using the solution in this embodiment was able to clearly and accurately locate damage boundaries even with extremely low signal-to-noise ratios, successfully detecting thermal damage features missed by all comparison models. This demonstrates excellent anti-interference robustness and all-weather surface monitoring capabilities, and also provides sufficient technical validation for future deployment in low-power, low-light downhole intelligent sensing tools. Figure 4 As shown in the left figure, under extreme testing conditions of extremely low light and uneven illumination, the detection system of this invention can not only accurately identify structurally obvious eddy current damage and impact damage on the cutting tooth surface, but also successfully locate minute thermal damage that is easily missed due to background interference. This detection result intuitively demonstrates that the system has anti-interference robustness under low signal-to-noise ratio and complex background conditions, fully meeting the needs of all-weather monitoring at the well site.

[0102] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A smart detection method for PDC drill bit damage, characterized in that, Includes the following steps: S1: Data Construction and Preprocessing: The acquired PDC drill bit images are filtered, classified and labeled, and resolution normalization is performed. S2: Comprehensive Data Augmentation: Performs Mosaic enhancement, Mixup enhancement, geometric transformation, and optical distortion operations on the preprocessed image; S3: Feature Extraction and Fusion: The image is input into a dynamic adaptive extraction backbone network to extract multi-dimensional features. Then, a bidirectional path fusion neck network is used to perform top-down and bottom-up bidirectional path feature flow and deep fusion. The dynamic adaptive extraction backbone network adopts a hybrid architecture design to extract multi-scale features from tiny thermal damage cracks to large-area mechanical fractures. The bidirectional path fusion neck network is based on a bidirectional path fusion module as the neck network. By constructing a top-down and bottom-up bidirectional feature flow mechanism, it deeply fuses multi-scale features from different levels of the backbone network. S4: Model Training and Optimization: The RT-DETR detection head is integrated, and a function that combines localization, classification, and regression losses is used to guide parameter optimization. The validation set metrics are used to determine whether the model meets engineering standards. The RT-DETR detection head achieves high-precision damage localization and classification by decoupling the prediction task, and damage prediction is directly achieved using the Transformer decoder architecture. S5: Model Application and Inference: After normalizing the image to be detected, forward inference is performed to output visual detection results in real time, including damage category name, confidence level and prediction box position.

2. The intelligent detection method for PDC drill bit damage according to claim 1, characterized in that, The core components of the dynamically adaptive extraction backbone network include: a partial channel attention convolution module, an efficient multi-scale attention module, and global modeling and multi-scale enhancement.

3. The intelligent detection method for PDC drill bit damage according to claim 2, characterized in that, The partial channel attention convolution module includes: Channel segmentation and dynamic weighting: For the input feature map, it is first segmented along the channel dimension into the part to be convolved and the invariant identity part. Then, a lightweight channel attention function is used to generate dynamic channel weights for the convolved part. in, For dynamic channel weights, It is the Sigmoid activation function. Implemented by global average pooling and two 1×1 convolutional layers. The part of the input feature map that has been convolved; Feature enhancement and fusion: The generated weights are applied to The weighted feature map is obtained. , in, This is the feature map after channel attention weighting. The part of the input feature map that has undergone convolution processing. For channel-by-channel multiplication operators, Dynamic channel weights; Subsequently Perform 3×3 convolution feature extraction, concatenate it with the invariant input feature map along the channel dimension, and then perform cross-channel information fusion through 1×1 convolution to obtain the output: in, This is the final output feature map of the partial channel attention convolution module. This is the feature map after channel attention weighting. For input feature maps that remain unchanged, For spatial feature extraction operators, For splicing operations, This is a cross-channel information fusion operator.

4. The intelligent detection method for PDC drill bit damage according to claim 2, characterized in that, The efficient multi-scale attention module is used to introduce a cross-spatial learning mechanism. The efficient multi-scale attention performs 1D global average pooling along the height and width dimensions in two parallel branches to encode global spatial information. By modeling the interaction between the height and width dimensions, it generates corresponding attention weight vectors. and Ultimately, this achieves the calibration of the characteristic response: in, This is the final output feature map of the efficient multi-scale attention module. These are the input features after grouping. and These are the height and width attention maps obtained through cross-space learning, respectively.

5. The intelligent detection method for PDC drill bit damage according to claim 2, characterized in that, The global modeling and multi-scale enhancement, at a deep network layer, utilizes a C2f attention module to model global contextual relationships and establishes long-distance dependencies through a self-attention mechanism, helping the model distinguish between large-area damage and damage occurring in specific locations. The fast spatial pooling pyramid module connected at the network end captures multi-scale contextual information through a series of max pooling layers, enhancing the model's robustness to damage targets of different sizes.

6. The intelligent detection method for PDC drill bit damage according to claim 1, characterized in that, The bidirectional path fusion neck network includes: 1) Input feature preprocessing: The bidirectional path fusion neck network receives three feature layers S2, S3 and S4 with different resolutions from the backbone network output. First, the number of channels of each input layer is uniformly projected into fixed-dimensional feature maps P2, P3 and P4 through a 1×1 convolutional layer. 2) Two-way fusion path design: This module contains two complementary information flow paths: Top-down semantic transmission path: High-level feature P4 contains rich semantic information. After its spatial size is expanded through upsampling, it enters the feature fusion module and combines with the mid-level feature P3 to generate the intermediate feature F3. Similarly, F3 continues to be transmitted down and fused with P2, so that the shallow features obtain the global semantic background required to identify the damage category; Bottom-up localization enhancement path: Based on semantic transmission, the low-level spatial feature F2, which contains strong localization information, is transmitted back through downsampling and fused with F3 to generate O3, and finally converges to the top level to generate O4. 3) Node fusion logic: Each fusion node in the bidirectional path fusion neck network is the core of achieving deep integration. It receives high-level features and low-level features, processes them through specific fusion operators, and achieves deep interaction between feature dimensions and spatial information through parallel convolution and feature concatenation. 4) Multi-scale output convergence: The output features of each level after bidirectional enhancement are globally integrated through a splicing operation to generate a high-quality feature map with both high-resolution spatial details and rich semantic expression. This map is then fed into the subsequent RT-DETR detection head to perform end-to-end prediction.

7. The intelligent detection method for PDC drill bit damage according to claim 1, characterized in that, The RT-DETR detection head integrates an IoU-aware query selection mechanism and a Transformer decoder. The IoU-aware query selection mechanism dynamically selects the initial query point most likely to contain the damage target based on the multi-scale encoding features output by the neck network. After the query point enters the decoder, the Transformer decoder continuously extracts damage details from the feature map through a multi-layer self-attention and cross-attention mechanism, and iteratively optimizes the category and location of the damage region.

8. The intelligent detection method for PDC drill bit damage according to claim 1, characterized in that, In step S4, a joint loss function is used to guide training: in, For the total joint loss, , , This is the weighting balance coefficient. Locate the loss for IoU. For classifying losses, The bounding box coordinate regression loss is used, and the classification loss is evaluated using the cross-entropy loss function for the seven damage categories. The calculation formula is as follows: in, This represents the number of samples in the current training batch. The total number of damage categories as defined. For the sample In category The real labels on Predict samples for the model Category The probability of bounding box coordinate regression loss is used to constrain the coordinates of the center point and the width and height of the bounding box using the L1 norm, ensuring that the predicted box still has good fit in challenging contexts.

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