Carbon steel clamp type valve joint state monitoring method based on mechanical sensor

Abstract

The present application relates to the field of mechanical sensors and mechanical engineering, and discloses a carbon steel clamp pressure type valve joint state monitoring method based on a mechanical sensor. The method comprises: arranging mechanical sensors in three directions at each joint to form a distributed network; applying a wideband excitation; synchronously collecting and time-aligning multi-channel response signals; constructing a multi-input multi-output transfer function matrix; extracting a main singular value sequence through singular value decomposition, calculating its decay rate and low-frequency amplitude gain ratio; combining a preset threshold to evaluate the system stiffness and energy attenuation characteristics, generating a health index and triggering a graded early warning. The present application realizes online and overall health monitoring of hidden clamp joint groups, and improves the safety and predictive maintenance capability of high-risk industrial scenes.

Description

Technical Field

[0001] This invention belongs to the field of mechanical sensors and mechanical engineering, specifically relating to a method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors. Background Technology

[0002] With the widespread application of industrial pipeline systems in infrastructure such as energy, chemical, and municipal water supply, press-fit connections have become the mainstream form of carbon steel valve fittings due to their convenient installation and reliable sealing. This type of connection relies on mechanical clamping force to achieve an interference fit between metals, and its long-term performance is highly dependent on the connection stiffness and energy dissipation capacity at the joint. Under complex operating conditions, pipeline systems composed of multiple press-fit joints may experience overall dynamic characteristic degradation due to local loosening, fretting wear, or stress relaxation. Traditional monitoring methods often focus on single-point vibration amplitude or spectral characteristics, making it difficult to capture system-level nonlinear coupling behavior and structural correlation evolution patterns.

[0003] While vibration monitoring based on mechanical sensors can acquire the local dynamic response of joints, the information contained in a single signal is limited and cannot reflect the interaction mechanisms between multiple joints. Existing methods typically treat each joint as an independent unit for condition assessment, ignoring the inherent dynamic relationships formed in the pipeline system as a continuous medium during vibration propagation. This fragmented analytical paradigm results in a lack of sensitivity and explanatory power for key degradation phenomena such as overall system stiffness decay and changes in energy transfer paths, making it difficult to provide reliable early warnings in the early stages of minor damage.

[0004] Current technologies lack effective characterization of the nonlinear nature of vibration signals, often simplifying the process with linear assumptions and losing the structural integrity information carried by chaotic attractors. Furthermore, a networked analysis framework capable of quantifying the information flow and coupling strength between joints has not yet been established, leaving system-level degradation pattern recognition at a qualitative or empirical level. In industrial scenarios with high reliability requirements, maintenance personnel struggle to promptly grasp the risk of cascading failures caused by localized loosening. Therefore, a comprehensive monitoring method integrating nonlinear dynamic feature extraction and multivariate information flow modeling is needed to achieve cross-scale structural health assessment, from local chaotic characteristics to global interconnected networks. Summary of the Invention

[0005] This invention provides a method for monitoring the condition of carbon steel press-fit valve joints based on mechanical sensors. By deploying multiple distributed mechanical sensing units in the pipeline system, the method synchronously collects multidimensional mechanical response signals at each press-fit joint and constructs a global dynamic transfer function model of the pipeline system. This enables quantitative evaluation and degradation trend identification of the overall connection stiffness and energy attenuation characteristics of the pipeline system composed of multiple press-fit joints.

[0006] This invention provides a method for monitoring the condition of carbon steel press-fit valve joints based on mechanical sensors, including: In a pipeline system consisting of multiple carbon steel press-fit valve joints connected in series or in parallel, high-precision strain gauges or piezoelectric mechanical sensors are installed in the axial, radial and circumferential directions of each press-fit joint to form a distributed multi-point sensing network. A broadband mechanical excitation signal is applied to the pipeline system by an excitation device; The time-domain response signals output by all mechanical sensors are acquired synchronously, and the time-domain response signals are processed by analog-to-digital conversion and timestamp alignment to generate a multi-channel synchronous sampling dataset. Based on the multi-channel synchronous sampling dataset, the input-output relationship between any two adjacent joints is calculated, and the multi-input multi-output transfer function matrix of the pipeline system is constructed. The transfer function matrix is ​​subjected to singular value decomposition to extract its principal singular value sequence, and the decay rate of the principal singular value sequence and the amplitude gain ratio in the low frequency band are calculated. The decay rate is compared with a preset first threshold. If the decay rate is greater than the first threshold, it is determined that the overall energy decay characteristics of the pipeline system have significantly degraded. The low-frequency band amplitude gain ratio is compared with a preset second threshold. If the low-frequency band amplitude gain ratio is less than the second threshold, it is determined that the overall connection stiffness of the pipeline system has decreased significantly. The preferred method is to output a warning signal.

[0007] Preferably, in the pipeline system, the carbon steel press-fit valve joint includes a valve body, a press-fit ring, a sealing ring, and a pipe end. The mechanical sensor is fixed to the metal substrate in the transition area between the outer surface of the press-fit ring and the pipe end by epoxy resin adhesive. The sensor lead is laid along the pipe wall and connected to the central data acquisition unit through a shielded cable.

[0008] Preferably, the broadband mechanical excitation signal is generated by an electromagnetic vibrator, which is attached to the non-joint area of ​​the pipeline system by a magnetic base. The direction of its excitation force is perpendicular to the pipeline axis, and the waveform of the excitation signal is a swept sine wave or a pseudo-random binary sequence signal.

[0009] Preferably, the timestamp alignment process adopts a hardware-triggered synchronization mechanism, the sampling clock of all mechanical sensors is provided by the same high-stability crystal oscillator, and the analog-to-digital conversion operation is uniformly started through the field programmable gate array controller to ensure that the sampling time deviation of each channel is no more than 1 microsecond.

[0010] Preferably, the construction process of the multi-input multi-output transfer function matrix includes: using the acceleration signal at the excitation point as the system input, using the strain or force signal at each joint as the system output, using the Welch average periodogram method to calculate the cross power spectral density of the input and output signals and the self power spectral density of the input signal, and then obtaining the frequency domain transfer function of each channel through the frequency response function estimation formula, and finally combining them into a complete transfer function matrix.

[0011] Preferably, the principal singular value sequence is a sequence of the maximum singular values ​​obtained by performing singular value decomposition on the transfer function matrix at each frequency point, the attenuation rate is defined as the absolute value of the negative slope of the sequence in the 500 Hz to 3 kHz frequency band, and the low-frequency amplitude gain ratio is defined as the ratio of the principal singular value at 100 Hz to the principal singular value at 1 kHz.

[0012] Preferably, the health index is calculated using the following formula: ; For health index, For decay rate, The first threshold, For low-frequency band amplitude gain ratio, The second threshold, and The weighting coefficient is used to issue a Level 1 warning when the health index is less than 0.7 and a Level 2 emergency warning when the health index is less than 0.5.

[0013] Preferably, the method further includes the step of periodically updating the first threshold and the second threshold, specifically: after the initial installation of the pipeline system is completed, a benchmark test is performed, and the attenuation rate and low-frequency amplitude gain ratio under defect-free conditions are recorded as the initial first threshold and the initial second threshold, respectively. In each subsequent maintenance cycle, if the system is confirmed to be in good condition, the thresholds are recalibrated with the current measurement values.

[0014] Preferably, the frequency response function estimation formula is: ; For the first The input channel and the first Each output channel at frequency Frequency domain transfer function at that point, Indicates the output signal With input signal cross power spectral density, Indicates the input signal The self-power spectral density.

[0015] Preferably, the calculation of the attenuation rate includes calculations for the frequency band from 500 Hz to 3 kHz. and Perform linear least squares fitting, and take the absolute value of the slope of the fitted line as the decay rate. Main singular value sequences in frequency The value at that location.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. This invention solves the problem of traditional methods that rely solely on single-point vibration signals to evaluate the local state of joints in isolation. By constructing a distributed mechanical sensing network covering the entire pipeline system and a multi-input multi-output dynamic model, it achieves the coordinated quantification of the overall connection stiffness and energy dissipation capacity of a complex pipeline system composed of multiple carbon steel press-fit valve joints.

[0017] 2. The method utilizes the singular value characteristics of the transfer function matrix to decouple the coupling effect of system structural stiffness and damping attenuation, thus avoiding misjudgments caused by local noise or single-point failure.

[0018] 3. By introducing two indicators, attenuation rate and low-frequency amplitude gain ratio, this invention can accurately capture the slight decrease in stiffness and damping abnormality caused by the initial stage of joint clamping failure, and realize early degradation warning.

[0019] 4. The synchronous sampling mechanism and hardware-level time alignment ensure the phase consistency of multi-point signals, providing a data foundation for high-precision transfer function estimation.

[0020] 5. The weighted fusion mechanism of the health index takes into account the dual degradation characteristics of stiffness and damping, making the condition assessment results more practical for engineering and valuable for decision-making guidance.

[0021] 6. This method enables online, continuous, and holistic health monitoring of concealed press-fit joint groups without disassembling pipelines or interrupting operation, thereby improving the safety, reliability, and predictive maintenance level of industrial fluid systems in high-risk scenarios such as nuclear power, chemical industry, and shipbuilding. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the overall technical solution architecture of the carbon steel press-fit valve joint status monitoring method based on mechanical sensors proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the present invention based on singular value decomposition of transfer function matrix; Figure 3 This is a logical flowchart of the distributed multi-point sensor network deployment and broadband excitation application in this invention. Figure 4This is a flowchart illustrating the logical flow of multi-channel synchronous sampling data generation and timestamp alignment in this invention. Figure 5 This is a logical flowchart of the joint evaluation of the overall connection stiffness and energy attenuation characteristics of the pipeline system in this invention. Figure 6 This is a schematic diagram of the multi-level interaction relationship and data flow between the terminal device and the central processing module in this invention. Detailed Implementation

[0023] refer to Figures 1 to 6 This invention provides a method for monitoring the condition of carbon steel press-fit valve joints based on mechanical sensors. By deploying a distributed multi-point sensor network in a pipeline system composed of multiple carbon steel press-fit valve joints, multi-dimensional mechanical response signals at each joint are simultaneously acquired. A global dynamic transfer function model is constructed based on multi-input multi-output system identification theory. Then, singular value decomposition is used to extract the principal singular value sequence, quantitatively assessing the degradation state of the overall connection stiffness and energy attenuation characteristics of the pipeline system. This method solves the problem of traditional methods relying solely on single-point vibration signals for local state judgment, achieving collaborative, continuous, and online monitoring of the overall structural health status of complex pipeline systems.

[0024] The method describes a distributed multi-point sensing network formed by installing high-precision strain gauges or piezoelectric mechanical sensors in the axial, radial, and circumferential directions of each press-fit valve joint in a pipeline system consisting of multiple carbon steel press-fit valve joints connected in series or parallel. Each press-fit joint includes a valve body, a press-fit ring, a sealing ring, and a connecting pipe end. The mechanical sensor is fixed to the metal substrate in the transition area between the outer surface of the press-fit ring and the connecting pipe end using epoxy resin adhesive. This area is where stress concentration is most significant under stress, reflecting the tightness and structural integrity of the press-fit connection. The sensor leads are laid along the pipe wall and connected to the central data acquisition unit via shielded cables to suppress electromagnetic interference from contaminating the weak mechanical signals. The high-precision strain gauge has a sensitivity of not less than 2 millivolts per volt and an operating temperature range covering -40 degrees Celsius to 120 degrees Celsius, ensuring stable operation in a wide temperature range environment in industrial settings; the piezoelectric mechanical sensor has a resonant frequency of not less than 15 kHz and a charge sensitivity of not less than 50 picocoulombs per newton, suitable for the precise capture of high-frequency dynamic responses.

[0025] A broadband mechanical excitation signal is applied to the piping system via an excitation device. The frequency range of this signal is 10 Hz to 5 kHz, sufficient to excite the structural modes dominated by the crimp joint interface within the piping system, while avoiding interference from high-frequency noise on low-order mode identification. This broadband mechanical excitation signal is generated by an electromagnetic exciter, which is attached to the non-joint area of ​​the piping system via a magnetic base. Its excitation force is perpendicular to the pipe axis to maximize the excitation of lateral bending and torsional coupling modes, thereby enhancing sensitivity to changes in connection stiffness. The excitation signal waveform uses either a swept-frequency sine wave or a pseudo-random binary sequence signal. The former facilitates point-by-point analysis of the system response characteristics in the frequency domain, while the latter possesses good autocorrelation characteristics, which helps improve the signal-to-noise ratio and system identification accuracy.

[0026] Under the excitation signal, all mechanical sensors synchronously output time-domain response signals. To ensure strict alignment of the multi-channel signals in the time dimension, a hardware-triggered synchronization mechanism is used for data acquisition. The sampling clock for all mechanical sensors is provided by the same high-stability crystal oscillator, whose frequency stability is better than 10%. -8 Phase jitter is less than 50 picoseconds. After receiving an external trigger signal, the field-programmable gate array (FPGA) controller initiates analog-to-digital conversion (ADC) operations on all channels simultaneously, ensuring that the sampling time deviation of each channel is no greater than 1 microsecond. The sampling frequency of the synchronous data acquisition unit is no less than 20 kHz, meeting the Nyquist sampling theorem requirement for a maximum excitation frequency of 5 kHz; its resolution is no less than 24 bits, capable of resolving mechanical changes at the micro-strain level; the phase mismatch between channels is less than 0.1 degrees, and the amplitude imbalance is less than 0.1 dB, ensuring the consistency of the amplitude and phase characteristics of the multi-channel signals. The raw data after ADC conversion is accompanied by a high-precision timestamp, forming a multi-channel synchronous sampling dataset, which serves as the basic input for subsequent system modeling and feature extraction.

[0027] Based on the multi-channel synchronous sampling dataset, a multi-input multi-output transfer function matrix for the pipeline system is constructed. The acceleration signal at the excitation point is used as the system input, acquired by a reference accelerometer installed at the exciter output; the strain or force signals at each joint are used as the system output. First, a fast Fourier transform is performed on all input and output signals to convert them from the time domain to the frequency domain, obtaining a complex-form spectral sequence. The Welch average periodogram method is used to calculate the cross-power spectral density between the input and output signals, as well as the self-power spectral density of the input signal. The Welch method segments the signal, applies windowing and overlaps it, and averages the periodograms of each segment, suppressing the variance of the spectral estimation. Based on this, the frequency response function is estimated using the formula: ; Calculate the first The input channel and the first Each output channel at frequency Frequency domain transfer function , Indicates the output signal With input signal cross power spectral density, Indicates the input signal The self-power spectral density. Since only one excitation point is set in this embodiment, the number of input channels is 1, and the number of output channels is three times the number of connectors (each connector has three orthogonal directions), and finally all are combined. Form a complete transfer function matrix , dimension , This represents the total number of press-fit joints in the piping system.

[0028] After obtaining the transfer function matrix, singular value decomposition is performed on it at each frequency point. Singular value decomposition transforms the matrix... Decomposed into , The left singular matrix obtained from singular value decomposition. The right singular matrix is ​​obtained from singular value decomposition. This is a diagonal matrix, where the diagonal elements are the singular values, arranged in descending order. The largest singular value is selected. Constructing the principal singular value sequence The principal singular value reflects the frequency. The maximum energy gain that the system can transfer from input to output is directly related to the overall structural characteristics of the pipeline system. When the connection stiffness of the press-fit joint decreases due to loosening, corrosion, or sealing failure, the transfer gain in the low-frequency range of the system decreases; when interface friction or material damping increases abnormally, the energy attenuation in the high-frequency range intensifies, which is manifested as a change in the slope of the singularity curve.

[0029] Based on the principal singular value sequence (PSV), two key characteristic parameters are calculated: attenuation rate and low-frequency amplitude-gain ratio. The attenuation rate is defined as the absolute value of the negative slope of the PSV in the 500 Hz to 3 kHz frequency band, and is obtained by linear least squares fitting within this frequency band. right The relationship curve is used, and the absolute value of the slope of the fitted straight line is taken as the decay rate. Main singular value sequences in frequency The value at this point. This parameter characterizes the system's energy dissipation capability in the mid-to-high frequency band. A larger value indicates stronger system damping characteristics, but if it exceeds the normal range, it may indicate degradation phenomena such as fretting wear, grease drying, or crack initiation at the joint interface. The low-frequency amplitude gain ratio is defined as the ratio of the principal singular value at 100 Hz to the principal singular value at 1 kHz, i.e. The ratio reflects the system's stiffness performance in the low-frequency range relative to the mid-frequency range. The smaller the ratio, the lower the relative stiffness at low frequencies, which is usually caused by plastic deformation of the compression ring, permanent compression deformation of the sealing ring, or increased clearance at the pipe end.

[0030] The attenuation rate is compared with a preset first threshold. If the attenuation rate is greater than the first threshold, the overall energy attenuation characteristics of the pipeline system are determined to have significantly degraded. The low-frequency amplitude gain ratio is compared with a preset second threshold. If the low-frequency amplitude gain ratio is less than the second threshold, the overall connection stiffness of the pipeline system is determined to have significantly decreased. The first threshold is set to 0.05 per kilohertz, and the second threshold is set to 1.8. These two thresholds are not fixed but are determined by benchmark testing after the initial installation of the pipeline system. Specifically, under the condition that the system is defect-free and all joints are installed according to specifications, a complete excitation-acquisition-modeling process is executed, and the attenuation rate and low-frequency amplitude gain ratio at this time are recorded as the initial first threshold and initial second threshold, respectively. During each maintenance cycle, if the system is confirmed to be in good condition by manual inspection, the thresholds are recalibrated with the current measurement values ​​to achieve adaptive updates and adapt to the effects of long-term effects such as environmental temperature drift and material aging.

[0031] Furthermore, based on the combined trend of the attenuation rate and the low-frequency amplitude-gain ratio, a health index for the pipeline system connection status is generated. The health index is calculated using the following formula: ; For health index, For decay rate, The first threshold, For low-frequency band amplitude gain ratio, The second threshold, and These are the weighting coefficients. The value is 0.6. The value is 0.4. This weighting is based on engineering experience, indicating that in most industrial scenarios, maintaining connection stiffness is more important for system safety. When the health index is less than 0.7, the system issues a Level 1 warning, prompting maintenance personnel to pay attention to the joint status and arrange for an upcoming inspection. When the health index is less than 0.5, the system issues a Level 2 emergency warning, recommending immediate shutdown for inspection to prevent fluid leakage or structural breakage due to joint failure.

[0032] The method also includes an automatic identification and rejection mechanism for abnormal data. During synchronous sampling, if the amplitude of a channel signal exceeds the sensor's range, or the correlation coefficient with other adjacent channel signals is less than 0.3, the channel data is determined to be abnormal. The system automatically activates redundant channels or interpolation algorithms for compensation to ensure the integrity of the transfer function matrix construction. The central processing module periodically executes a self-diagnostic program to verify sensor zero-point drift, excitation signal amplitude stability, and sampling clock synchronization accuracy. Once hardware performance degradation is detected, a fault log is immediately recorded and reported.

[0033] In practical deployment, the distributed mechanical sensing network, broadband excitation device, synchronous data acquisition unit, and central processing module together constitute a complete monitoring system. The central processing module is equipped with non-volatile memory to store historical health indices, threshold parameters, and raw sampled data fragments. Its processor runs an embedded real-time operating system, periodically executing the aforementioned process flow, typically every 24 hours, or immediately upon receiving a manual trigger command. The early warning output interface uses industry-standard protocols such as ModbusTCP or OPCUA to push health indices and early warning levels to the factory monitoring platform or mobile maintenance terminal in real time, achieving closed-loop management.

[0034] In summary, this embodiment achieves high-precision and robust monitoring of the overall connection status of multi-joint pipeline systems through a rigorous three-dimensional orthogonal sensing layout, hardware-level synchronous sampling, multi-input multi-output transfer function modeling, singular value decomposition feature extraction, and a dual-parameter joint evaluation mechanism. This method requires no pipeline disassembly and does not interrupt normal operation, making it suitable for scenarios with high safety requirements, such as nuclear power plant cooling loops, high-pressure transmission pipelines in chemical plants, and marine propulsion systems, thus improving the scientific rigor and reliability of predictive maintenance.

[0035] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0036] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for monitoring the condition of carbon steel press-fit valve joints based on mechanical sensors, characterized in that, include: In a pipeline system consisting of multiple carbon steel press-fit valve joints connected in series or in parallel, high-precision strain gauges or piezoelectric mechanical sensors are installed in the axial, radial and circumferential directions of each press-fit joint to form a distributed multi-point sensing network. A broadband mechanical excitation signal is applied to the pipeline system by an excitation device; The time-domain response signals output by all mechanical sensors are acquired synchronously, and the time-domain response signals are processed by analog-to-digital conversion and timestamp alignment to generate a multi-channel synchronous sampling dataset. Based on the multi-channel synchronous sampling dataset, the input-output relationship between any two adjacent joints is calculated, and the multi-input multi-output transfer function matrix of the pipeline system is constructed. The transfer function matrix is ​​subjected to singular value decomposition to extract its principal singular value sequence, and the decay rate of the principal singular value sequence and the amplitude gain ratio in the low frequency band are calculated. The decay rate is compared with a preset first threshold. If the decay rate is greater than the first threshold, it is determined that the overall energy decay characteristics of the pipeline system have significantly degraded. The low-frequency band amplitude gain ratio is compared with a preset second threshold. If the low-frequency band amplitude gain ratio is less than the second threshold, it is determined that the overall connection stiffness of the pipeline system has decreased significantly. Based on the combined changing trend of the attenuation rate and the low-frequency amplitude gain ratio, a health index of the pipeline system connection status is generated, and an early warning signal is output.

2. The method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors according to claim 1, characterized in that, In the pipeline system, the carbon steel press-fit valve joint includes a valve body, a press-fit ring, a sealing ring, and a pipe end. The mechanical sensor is fixed to the metal substrate in the transition area between the outer surface of the press-fit ring and the pipe end by epoxy resin adhesive. The sensor lead is laid along the pipe wall and connected to the central data acquisition unit through a shielded cable.

3. The method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors according to claim 2, characterized in that, The broadband mechanical excitation signal is generated by an electromagnetic vibrator, which is attached to the non-joint area of ​​the pipeline system by a magnetic base. The direction of its excitation force is perpendicular to the pipeline axis, and the waveform of the excitation signal is a swept sine wave or a pseudo-random binary sequence signal.

4. The method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors according to claim 3, characterized in that, The timestamp alignment process adopts a hardware-triggered synchronization mechanism. The sampling clock of all mechanical sensors is provided by the same high-stability crystal oscillator, and the analog-to-digital conversion operation is uniformly started through the field-programmable gate array controller to ensure that the sampling time deviation of each channel is no more than 1 microsecond.

5. The method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors according to claim 4, characterized in that, The construction process of the multi-input multi-output transfer function matrix includes: taking the acceleration signal at the excitation point as the system input and the strain or force signal at each joint as the system output, using the Welch average periodogram method to calculate the cross power spectral density of the input and output signals and the self power spectral density of the input signal, and then obtaining the frequency domain transfer function of each channel through the frequency response function estimation formula, and finally combining them into a complete transfer function matrix.

6. The method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors according to claim 5, characterized in that, The principal singular value sequence is a sequence of the maximum singular values ​​obtained by performing singular value decomposition on the transfer function matrix at each frequency point. The decay rate is defined as the absolute value of the negative slope of the sequence in the 500 Hz to 3 kHz frequency band. The low-frequency amplitude gain ratio is defined as the ratio of the principal singular value at 100 Hz to the principal singular value at 1 kHz.

7. The method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors according to claim 6, characterized in that, The health index is calculated using the following formula: ； For health index, For decay rate, The first threshold, For low-frequency band amplitude gain ratio, The second threshold, and The weighting coefficient is used to issue a Level 1 warning when the health index is less than 0.7 and a Level 2 emergency warning when the health index is less than 0.

5.

8. The method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors according to claim 7, characterized in that, The method further includes the step of periodically updating the first threshold and the second threshold, specifically: after the initial installation of the pipeline system is completed, a benchmark test is performed, and the attenuation rate and low-frequency amplitude gain ratio under defect-free conditions are recorded as the initial first threshold and the initial second threshold, respectively. In each subsequent maintenance cycle, if the system is confirmed to be in good condition, the thresholds are recalibrated with the current measurement values.

9. The method for monitoring the condition of carbon steel press-fit valve joints based on mechanical sensors according to claim 8, characterized in that, The formula for estimating the frequency response function is: ； For the first The input channel and the first Each output channel at frequency Frequency domain transfer function at that point, Indicates the output signal With input signal cross power spectral density, Indicates the input signal The self-power spectral density.

10. The method for monitoring the status of carbon steel press-fit valve joints based on mechanical sensors according to claim 9, characterized in that, The calculation of the attenuation rate includes the frequency band from 500 Hz to 3 kHz. and Perform linear least squares fitting, and take the absolute value of the slope of the fitted line as the decay rate. Main singular value sequences in frequency The value at that location.

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