Engine bonding interface defect detection method based on weak grating array technology

Abstract

The invention discloses an engine bonding interface defect detection method based on a weak grating array technology, and the specific detection process is as follows: designing a weak grating array optical fiber sensing network suitable for an engine interface according to the geometrical characteristics of a solid rocket engine; manufacturing a solid engine equivalent physical model, establishing a finite element simulation model, performing solid rocket engine interface defect semi-physical simulation, and constructing an engine interface defect knowledge base; the method comprises the following steps: establishing an interface defect intelligent mapping model, preprocessing measured data, obtaining data features, identifying and classifying the data features according to the interface defect intelligent mapping model, and confirming interface defect conditions. According to the defect detection method, a weak reflection grating sensing network is implanted into a contact interface of an engine shell and a heat insulation layer, a continuous physical interface is discretized into a plurality of independent monitoring areas, and accurate judgment of defects is achieved by deeply analyzing actually measured data collected by the weak reflection grating array optical fiber sensing network.

Description

Technical Field

[0001] This invention relates to the field of solid rocket motor testing technology, and in particular to a method for detecting defects at the engine bonding interface based on weak grating array technology. Background Technology

[0002] The combustion chamber is a key power unit for spacecraft, and its structural integrity is directly related to flight safety. The combustion chamber of the engine is usually made of multiple layers of materials, including shell, insulation, lining, and propellant, bonded together. During production and use, defects such as debonding, creep, and cracks may occur at the interfaces due to poor manufacturing processes, aging, or overload. These defects can change the combustion surface pattern of the engine, affect engine performance, and even cause catastrophic accidents.

[0003] Currently, the detection of interface defects in solid rocket motors mainly employs ultrasonic testing, X-ray testing, and traditional fiber optic sensing technologies. Ultrasonic testing requires coupling and has poor adaptability to complex results, while X-ray testing presents safety protection issues and is difficult to monitor in real time. Traditional fiber optic grating sensors have high reflectivity, large crosstalk, and limited spatial resolution, making it difficult to achieve distributed and accurate measurements.

[0004] As an emerging distributed optical fiber sensing technology, weak grating array technology has a reflectivity as low as 1%, and a single optical fiber can reuse thousands of sensing units. It has advantages such as high precision, resistance to electromagnetic interference, and distributed measurement, providing a brand-new solution for dynamic monitoring of engine interface defects.

[0005] However, existing research focuses on detection methods, with limited studies on the precise correlation between strain data acquired by fiber optic sensors and engine interface defects. This invention constructs a strain knowledge base using semi-physical methods and combines it with artificial intelligence algorithms to provide technical support for engine interface defect identification. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes a method for detecting defects at the engine bonding interface based on weak grating array technology. This method embeds a weak reflection grating sensor network into the contact interface between the engine casing and the insulation layer, discretizing the continuous physical interface into multiple independent monitoring areas. Using hardware-in-the-loop simulation technology, it systematically simulates the responses of various defects such as interface debonding, weak bonding, and cracks under different operating conditions. Accurate defect identification is achieved through in-depth analysis of the measured data collected by the weak grating array fiber optic sensor network.

[0007] A method for detecting defects at the bonding interface of an engine based on weak grating array technology, the specific detection process is as follows:

[0008] S1. Based on the geometric characteristics of solid rocket motors, a weak grating array fiber optic sensing network suitable for the engine interface is designed.

[0009] S2, create an equivalent physical model of the solid rocket engine, establish a finite element simulation model, perform semi-physical simulation of interface defects in the solid rocket engine, and build a knowledge base for engine interface defects;

[0010] S3. Establish an intelligent mapping model for interface defects, preprocess the measured data and obtain data features, identify and classify the data features according to the intelligent mapping model for interface defects, and confirm the interface defect situation.

[0011] As a preferred embodiment of the above technical solution, in step S1, a weak grating array fiber optic sensor network is deployed based on optical frequency domain reflection demodulation technology. Specifically, 4n weak grating array fiber optic sensors are deployed along the engine busbar axis or the fiber winding direction of the housing at the contact interface between the solid engine casing and the insulation layer. The three-dimensional spatial position of the measuring point on each sensor at the engine interface is recorded as follows: Adjacent sensors jointly define a monitoring area. , where i = 1, 2, ..., 4n.

[0012] As a preferred embodiment of the above technical solution, the continuous physical interface of the engine is discretized into multiple independent monitoring areas by using a weak grating array fiber optic sensor network.

[0013] As a preferred embodiment of the above technical solution, the specific process of step S2 is as follows:

[0014] S21. Based on the engine manufacturing process, a weak grating array fiber optic sensor is embedded into the engine interface. From M operating conditions, m representative operating conditions are selected for physical construction, and model strain data under various defect conditions are obtained. ;

[0015] S22, Use ABAQUS to build finite element models for m different working conditions and obtain the strain data of the corresponding simulation models. ;

[0016] S23, with Based on, through Verify the finite element simulation model;

[0017] S24. Obtain the strain curve data set under M working conditions based on the verification results. and strain field data sets for each region ;

[0018] S25, Extract Corresponding strain characteristics And strain characteristics extracted after correlation calculation with adjacent sensors under the same working conditions. , ,right , , and After normalization, the strain curve data set is obtained. With strain field data set , and Together they constitute the interface defect strain knowledge base Δ;

[0019] in, ; .

[0020] As a preferred embodiment of the above technical solution, four types of key variables, including defect shape, defect size, defect location, and engine attitude, are introduced to verify the simulation model using physical samples. Specifically, for each type of variable, the following settings are made: , , , A typical state constitutes A comprehensive simulation is conducted, from which m representative key conditions are selected for physical fabrication. Based on the experimental data of these m sets of physical conditions, the corresponding finite element simulation models are verified and corrected. The verified high-confidence simulation models are then used to deduce and obtain response data for all M experimental conditions.

[0021] As a preferred embodiment of the above technical solution, the size and location of the defect are calculated in a dimensionless manner using the formula a / A, where a is a defect-related parameter and A is a parameter related to the discretized monitoring area.

[0022] As a preferred embodiment of the above technical solution, the interface defect strain characteristics include the strain data characteristics of a single optical cable and the correlation characteristics between the strain data of two adjacent optical cables.

[0023] As a preferred embodiment of the above technical solution, the specific process of step S3 is as follows:

[0024] S31, Establish an intelligent mapping model for initiating interface defects based on the interface strain knowledge base;

[0025] S32, Acquire physical engine sensor test data Engine attitude information, measuring point location information The data is segmented and located based on the measurement point location information and engine structure.

[0026] S33, to Preprocessing, including outlier removal, noise reduction, and normalization, is performed to extract strain features. Strain characteristics extracted after correlation calculation of adjacent sensors , ;

[0027] S34, , , As input, the intelligent mapping model for defects at the engine interface outputs possible defect results. With corresponding weight coefficients ;

[0028] S35, according to formula Predicted values ​​of strain field for each region were obtained. Image recognition algorithms predict values Perform image recognition to obtain the location and size of defects.

[0029] As a preferred embodiment of the above technical solution, the extracted strain data features include the strain data features of a single optical cable and the correlation features between the strain data of two adjacent optical cables.

[0030] The beneficial effects of this invention are as follows:

[0031] 1. This invention utilizes weak reflection gratings and optical frequency domain reflection technology to embed a weak reflection grating sensor network into the contact interface between the engine casing and the insulation layer, discretizing the continuous physical interface into multiple independent monitoring areas. Using hardware-in-the-loop simulation technology, it systematically simulates the responses of various defects such as interface debonding, weak adhesion, and cracks under different operating conditions, acquiring and recording the fiber optic cable strain data of each area and its strain correlation characteristics with adjacent fiber optic cables. Ultimately, it constructs an interface defect knowledge base containing both "local defect strain characteristics" and "overall strain distribution." This effectively solves key technical challenges such as the difficulty in directly observing interface defects in solid rocket motors, the complexity of failure modes, and the limited monitoring range of traditional point sensors, providing a feasible technical approach for achieving distributed, high-precision sensing of interface states.

[0032] 2. This invention achieves accurate defect identification through in-depth analysis of measured data collected by a weak grating array fiber optic sensor network. Specifically, this process includes two levels: first, in-depth mining of local features such as strain response amplitude and spectrum of a single optical cable; second, comprehensive analysis of the correlation characteristics of strain data from adjacent optical cables in the time and frequency domains. Based on this, an interface defect knowledge base is established, and artificial intelligence algorithms are used to intelligently learn and classify data features that integrate local and correlated characteristics, thereby enabling the judgment and evaluation of defect type, location, and size, providing technical support for intelligent diagnosis and early warning of solid rocket motor interface health status. Attached Figure Description

[0033] Figure 1 This is a flowchart of the defect identification algorithm of the present invention.

[0034] Figure 2 This is a schematic diagram of the engine interface sensor network.

[0035] Figure 3 This is a schematic diagram of the engine interface sensing unit. Detailed Implementation

[0036] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0037] like Figure 1 The specific detection process of the engine bonding interface defect detection method based on weak grating array technology is as follows:

[0038] S1: Based on the geometric characteristics of solid rocket motors, a weak reflection grating array fiber optic sensing network suitable for the engine interface was designed, and OFDR technology was used to demodulate the sensing optical cable. The weak reflection grating sensors cover various types with measurement point spacing ranging from 1cm to 5m, including those with identical wavelengths, wavelength division multiplexing (WDM) types, and reflection-enhanced types, etc., for practical engineering applications. Figure 2 As shown, taking n=1, four weak-reflection grating array fiber optic sensors, numbered L01, L02, L03, and L04, are arranged along the 001 busbar direction at the solid engine casing / insulation layer interface, forming a weak-reflection grating sensing network. The three-dimensional spatial position of the measuring point on each sensor at the engine interface is recorded as follows: (i=1,2,..4). Adjacent sensors jointly define a monitoring area. The continuous interface of the engine is discretized into four monitoring units.

[0039] S2: Set the impact of four key variables on defect characterization: defect shape, defect size, defect location, and engine attitude, and perform dimensionless characterization of defect size and location. For example... Figure 3 As shown, the defect size 002 is determined by formula Dimensionless transformation These refer to the areas of defect 002 and region 001, respectively. Dimensionless location is achieved through... , , calculate, The distance between defect 002 and optical cable L01. The distance between defect area 002 and optical cable L02. This refers to the distance from the defect to the starting point of region 001 along the engine busbar direction. Settings are set for each type of variable. , , , A typical state constitutes A comprehensive simulation of various operating conditions was conducted. From these, m of the most representative key operating conditions were selected for physical fabrication. Subsequently, the corresponding finite element simulation models were verified and corrected based on the experimental data from these m sets of physical models. The verified high-confidence simulation models were then used to deduce and obtain response data for all M experimental conditions. Strain data of the two optical cables corresponding to each region and the correlation characteristics of their strain data were extracted to obtain the strain field of each region, thus constructing a solid-state engine interface defect knowledge base.

[0040] Specifically, the following steps are included:

[0041] S21: Based on the engine manufacturing process, a weak grating array fiber optic sensor is embedded into the engine interface. From M types of operating conditions, m representative operating conditions are selected for physical construction, and model strain data under m defect conditions is obtained. ;

[0042] S22: Use ABAQUS to build finite element models for m different working conditions and obtain strain data for the corresponding simulation models. ;

[0043] S23: with Based on, through Verify the finite element simulation model;

[0044] S24: Obtain the strain curve data set under M working conditions based on the verification results. and strain field data sets for each region ;

[0045] S25: Extract Corresponding strain characteristics And strain characteristics extracted after correlation calculation with adjacent sensors under the same working conditions. , ,right , , ,and After normalization, the strain curve data set is obtained. With strain field data set , and Together they constitute the interface defect strain knowledge base Δ.

[0046] S3: Based on the interface defect strain knowledge base Δ, establish an intelligent mapping model for interface defects. Acquire strain data from the sensing optical cable. Engine current attitude information, overall measurement point position information Strain data Preprocessing and feature extraction are performed, and the extracted features are used as input. An intelligent interface defect judgment model is used to classify defect situations, obtaining possible interface defect scenarios. The predicted strain field values ​​for each region are obtained by linearly accumulating these scenarios. The predicted strain field values ​​for each region are then identified to determine and evaluate the defect type, location, and size. Specifically, the steps include:

[0047] S31: Establish an intelligent mapping model for initiating interface defects based on the interface strain knowledge base;

[0048] S32: Obtain test data for physical engine sensors Engine attitude information, measuring point location information The data is segmented and located based on the measurement point location information and engine structure.

[0049] S33: Yes Preprocessing steps such as outlier removal, noise reduction, and normalization are performed to extract strain features. Strain characteristics extracted after correlation calculation of adjacent sensors , ;

[0050] S34: Will , , As input, the possible results are output using an AI model of initiating interface defects. With weighting coefficients ;

[0051] S35: According to formula Predicted values ​​of strain field for each region were obtained. For the predicted value Image recognition is used to obtain the location and size of defects.

[0052] Specifically, the data processing algorithms used can remove outliers and filter noise from the acquired raw sensor signals. The specific implementation methods include one-dimensional convolutional neural networks, wavelet transform, and statistical anomaly detection algorithms (such as the 3σ criterion and DBSCAN density clustering).

[0053] The artificial intelligence algorithms used are data classification algorithms for defect pattern classification, including convolutional neural networks (CNN), recurrent neural networks (RNN) and their variant Long Short-Term Memory networks (LSTM), as well as traditional machine learning algorithms such as support vector machines (SVM) and random forests. Intelligent classification of interface states is achieved through feature learning and classification decisions.

[0054] The image recognition algorithm used is a three-dimensional image recognition algorithm for analyzing the three-dimensional deformation features of the interface, including but not limited to point cloud data processing algorithms, three-dimensional convolutional neural networks (3D-CNN), multi-view three-dimensional reconstruction algorithms, and three-dimensional feature extraction techniques (such as normal vector estimation and key point detection) to achieve interface defect localization and recognition.

[0055] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for detecting defects at the bonding interface of an engine based on weak grating array technology, characterized in that: The specific testing process is as follows: S1. Based on the geometric characteristics of solid rocket motors, a weak grating array fiber optic sensing network suitable for the engine interface is designed. S2, create an equivalent physical model of the solid rocket engine, establish a finite element simulation model, perform semi-physical simulation of interface defects in the solid rocket engine, and build a knowledge base for engine interface defects; S3. Establish an intelligent mapping model for interface defects, preprocess the measured data and obtain data features, identify and classify the data features according to the intelligent mapping model for interface defects, and confirm the interface defect situation.

2. The method for detecting defects at the engine bonding interface based on weak grating array technology according to claim 1, characterized in that: In step S1, a weak grating array fiber optic sensor network is deployed based on optical frequency domain reflection demodulation technology. Specifically, 4n weak grating array fiber optic sensors are deployed along the engine bus axis or the fiber winding direction of the casing at the contact interface between the solid engine casing and the insulation layer. The three-dimensional spatial position of the measuring point on each sensor at the engine interface is recorded as follows: Adjacent sensors jointly define a monitoring area. , where i = 1, 2, ..., 4n.

3. The method for detecting defects at the engine bonding interface based on weak grating array technology according to claim 2, characterized in that: The continuous physical interface of the engine is discretized into multiple independent monitoring areas by using a weak grating array fiber optic sensor network.

4. The method for detecting defects at the engine bonding interface based on weak grating array technology according to claim 2, characterized in that: The specific process of step S2 is as follows: S21. Based on the engine manufacturing process, a weak grating array fiber optic sensor is embedded into the engine interface. From M operating conditions, m representative operating conditions are selected for physical construction, and model strain data under various defect conditions are obtained. ; S22, Use ABAQUS to build finite element models for m different working conditions and obtain the strain data of the corresponding simulation models. ; S23, with Based on, through Verify the finite element simulation model; S24. Obtain the strain curve data set under M working conditions based on the verification results. and strain field data sets for each region ; S25, Extract Corresponding strain characteristics And strain characteristics extracted after correlation calculation with adjacent sensors under the same working conditions. , ,right , , and After normalization, the strain curve data set is obtained. With strain field data set , and Together they constitute the interface defect strain knowledge base Δ; in, ; .

5. The method for detecting defects at the engine bonding interface based on weak grating array technology according to claim 4, characterized in that: Four key variables, including defect shape, defect size, defect location, and engine attitude, are introduced to validate the simulation model using physical samples. Specifically, for each type of variable, the following settings are implemented: , , , A typical state constitutes A comprehensive simulation is conducted, from which m representative key conditions are selected for physical fabrication. Based on the experimental data of these m sets of physical conditions, the corresponding finite element simulation models are verified and corrected. The verified high-confidence simulation models are then used to deduce and obtain response data for all M experimental conditions.

6. The method for detecting defects at the engine bonding interface based on weak grating array technology according to claim 4, characterized in that: The size and location of the defect are calculated dimensionlessly using the formula a / A, where a is a defect-related parameter and A is a parameter related to the discretized monitoring area.

7. The method for detecting defects at the engine bonding interface based on weak grating array technology according to claim 4, characterized in that: The strain characteristics of interface defects include the strain data characteristics of a single optical cable and the correlation characteristics between the strain data of two adjacent optical cables.

8. The method for detecting defects at the engine bonding interface based on weak grating array technology according to claim 4, characterized in that: The specific process of step S3 is as follows: S31, Establish an intelligent mapping model for initiating interface defects based on the interface strain knowledge base; S32, Acquire physical engine sensor test data Engine attitude information, measuring point location information The data is segmented and located based on the measurement point location information and engine structure. S33, to Preprocessing, including outlier removal, noise reduction, and normalization, is performed to extract strain features. Strain characteristics extracted after correlation calculation of adjacent sensors , ; S34, , , As input, the intelligent mapping model for defects at the engine interface outputs possible defect results. With corresponding weight coefficients ; S35, according to formula Predicted values ​​of strain field for each region were obtained. Image recognition algorithms predict values Perform image recognition to obtain the location and size of defects.

9. The method for detecting defects at the engine bonding interface based on weak grating array technology according to claim 8, characterized in that: The extracted strain data features include strain data features of a single optical cable and correlation features between strain data of two adjacent optical cables.

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