Port device vibration signal processing method

Abstract

The present invention relates to the technical field of signal processing. Disclosed is a port device vibration signal processing method. A comprehensive function is constructed, and on the basis of the characteristics of a frequency scale, a second wavelet basis function and a threshold, the concentration of energy distribution, the computational complexity of second wavelet basis functions under each frequency scale, orthogonal values between the second wavelet basis functions under each frequency scale, a sparse value of second wavelet coefficients under each frequency scale and a signal-to-noise ratio of each time-domain signal obtained after threshold processing and reconstruction are taken into comprehensive consideration, so as to realize the selection of an optimal frequency scale, an optimal second wavelet basis function and an optimal threshold. This innovative method eliminates the limitation of a single or optimization algorithm, fixed parameter setting or a predefined rule in the prior art, thereby significantly improving the accuracy and robustness of low-frequency vibration signal processing.

Description

A method for processing vibration signals of port equipment Technical Field This invention relates to the field of signal processing technology, and in particular to a method for processing vibration signals of port equipment. Background Technology As a crucial hub in international and domestic logistics chains, the safety and reliability of port infrastructure are paramount. With the continuous expansion and increasing complexity of ports, the demand for health monitoring of port equipment (such as large cranes, wharf structures, and ships) is growing. Vibration signals are one of the important indicators for assessing the operational status and structural integrity of these devices. However, existing vibration sensors face numerous challenges in processing low-frequency vibration signals, especially in the complex multi-sea environment of ports. Therefore, developing a method that can effectively extend the measurement range of vibration sensors and accurately process low-frequency vibration signals is particularly urgent. Currently, commonly used magnetoelectric vibration sensors perform well in the high-frequency range, but their measurement capabilities in the low-frequency range are limited. Furthermore, port equipment typically involves large structures and heavy machinery with low vibration frequencies, especially during startup, shutdown, or load changes, resulting in significant low-frequency vibrations. The complex environment, influenced by factors such as temperature, humidity, salt spray, and wind and waves, further affects the acquisition and processing of low-frequency vibration signals. Existing technologies for processing low-frequency signals mostly involve removing background noise using filters to obtain a denoised signal, then applying wavelet functions to the denoised signal to obtain wavelet coefficients, followed by thresholding to obtain the processed time-domain signal. Finally, the signal-to-noise ratio of the time-domain signal is verified to ensure a significant improvement. If the verification fails, the frequency, function, and threshold must be reselected, and the signal processing repeated. Existing methods for selecting wavelet functions, frequency divisions, and thresholds rely on single or optimized algorithms, or fixed parameter settings or predefined rules. While these methods can achieve good results in certain specific scenarios, they largely depend on single or optimized algorithms, or on fixed parameter settings and predefined rules for selecting wavelet functions, frequency divisions, and thresholds. The limitation of this approach is that it cannot quickly complete signal processing in certain scenarios, requiring the resetting of frequency ranges, function selection, and thresholds. This results in an inability to fully adapt to different types of low-frequency vibration signals and complex noise environments, and in practical applications, it may not be able to quickly achieve optimal processing results, leading to low signal processing efficiency. Summary of the Invention To address the aforementioned technical problems, this invention provides a method for processing vibration signals from port equipment, comprising: Step 1: Acquire raw signals using sensors; Step 2: Preprocess the original signal; divide the preprocessed original signal into initial frequency scales to obtain sub-band signals of different frequencies; set several sampling points on the sub-band signals of different initial frequency scales, and perform a first wavelet transform on each sampling point using the first wavelet basis function to obtain the first wavelet coefficients of each sampling point under different initial frequency scales; calculate the energy distribution under each initial frequency scale based on the first wavelet coefficients of each sampling point under different initial frequency scales, and obtain the concentration based on the energy distribution under each initial frequency scale; Step 3: Remove background noise from the sub-band signal at each initial frequency scale using a filter to obtain a denoised signal, and record the filter length at each initial frequency scale; obtain the second wavelet basis function, select the second wavelet basis function to perform a second wavelet transform on each denoised signal at each initial frequency scale to obtain the second wavelet coefficients, calculate the orthogonality value of the current second wavelet basis function with other second wavelet basis functions at each initial frequency scale, and calculate the computational complexity of each second wavelet basis function at each initial frequency scale; obtain a threshold, and perform threshold processing on the second wavelet coefficients; input the thresholded second wavelet coefficients into the inverse wavelet transform to reconstruct the time-domain signal; calculate the signal-to-noise ratio based on the reconstructed time-domain signal and the original signal; obtain the sparsity value based on the second wavelet coefficients at each initial frequency scale; Step 4: Construct a comprehensive function using sparsity, signal-to-noise ratio, concentration, computational complexity, and orthogonality. Based on the comprehensive function, select an optimized frequency scale from the initial frequency scale, an optimized second wavelet basis function from the second wavelet basis function, and an optimized threshold from the threshold. Step 5: Determine the optimized sub-band signal based on the optimized frequency scale, denoise the optimized sub-band signal using a filter to obtain the optimized denoised signal, perform a second wavelet transform on the optimized denoised signal using the optimized second wavelet basis function to obtain the optimized second wavelet coefficients, and perform threshold processing on the second wavelet coefficients according to the optimized threshold to obtain the optimized time domain signal.

[0010] Furthermore, the process of selecting an optimized frequency scale from the initial frequency scale, an optimized second wavelet basis function from the second wavelet basis function, and an optimized threshold from the threshold function, based on the synthesis function, includes the following steps: Step 41: Calculate the concentration of sub-band signal energy distribution at each initial frequency scale; take the initial frequency scale with the highest concentration as the initial optimized frequency scale. Step 42: Preset candidate second wavelet basis functions and candidate thresholds; Step 43: Select candidate second wavelet basis functions and perform second wavelet transform on each denoised signal at each initial optimized frequency scale to obtain candidate second wavelet coefficients; Step 44: Calculate the orthogonality value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimized frequency scale; Step 45: Calculate the computational complexity of each candidate wavelet basis function at each initial optimized frequency scale; Step 46: Threshold each candidate second wavelet coefficient using a candidate threshold; input the thresholded candidate second wavelet coefficients into the inverse wavelet transform to reconstruct the candidate time-domain signal; calculate the candidate signal-to-noise ratio based on the reconstructed candidate time-domain signal and the original signal; obtain the candidate sparse value based on the candidate second wavelet coefficients at each initial optimized frequency scale. Step 47: Based on the orthogonality between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimization frequency scale, the computational complexity of each candidate second wavelet basis function at each initial optimization frequency scale, the candidate sparse value obtained from the candidate second wavelet coefficients at each initial optimization frequency scale, the candidate signal-to-noise ratio calculated between the reconstructed candidate time-domain signal and the original signal, and the concentration of sub-band signal energy distribution at each initial optimization frequency scale, calculate the comprehensive function value, take the candidate second wavelet basis function with the largest comprehensive function value as the optimized second wavelet basis function, and take the candidate threshold with the largest comprehensive function value as the optimization threshold. Step 48: Based on the optimized second wavelet basis function and the optimized threshold, recalculate the comprehensive function value at each initial frequency scale, and use the initial frequency scale with the maximum comprehensive function value as the optimized frequency scale. Furthermore, the process of calculating the orthogonality value of each candidate wavelet function at the initial optimization frequency scale is as follows: calculate the correlation between each candidate second wavelet basis function and other candidate second wavelet basis functions at the initial optimization frequency scale to obtain the correlation matrix; calculate the maximum and minimum singular values ​​of the correlation matrix, obtain the condition number of the correlation matrix based on the maximum and minimum singular values, and use the condition number as the orthogonality value; construct an evaluation threshold for the orthogonality value, and divide the orthogonality value into reward terms and penalty terms based on the evaluation threshold. Furthermore, the computational complexity of each candidate wavelet basis function at each initial optimized frequency scale is: ; In the formula The computational complexity of the g-th candidate second wavelet basis function under the initial optimized frequency scale c. The signal length representing the initial optimized frequency scale c N represents the filter length at the initial optimized frequency scale c, and N is the length of the original signal. Furthermore, the orthogonality value of each candidate wavelet function at the initial optimization frequency scale is: ; In the formula The orthogonality value of the correlation matrix A between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c is... This represents the maximum singular value of the correlation matrix between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c. This represents the relationship between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c. The minimum singular value of the correlation matrix, Z represents the total number of candidate second wavelet basis functions under the initial optimized frequency scale c. Furthermore, the synthesis function is: In the formula This represents a comprehensive consideration of the initial frequency scale d and the second wavelet basis function. Threshold Synthesis function The concentration of energy distribution at the i-th initial frequency scale d Represents the transformation function. The orthogonal value representing the j-th second wavelet basis function The computational complexity of the j-th second wavelet basis function This represents the signal-to-noise ratio (SNR) between the reconstructed time-domain signal after the k-th threshold processing and the original signal. This represents the sparse values ​​of the optimized second wavelet coefficients after processing with the k-th threshold. These represent adjustment coefficients, . Furthermore, the transformation function is: In the formula, The transformation value represents the orthogonality value of the j-th second wavelet basis function, z represents a positive number, and z represents the evaluation threshold. Representative award items, This represents a penalty. The embodiments of the present invention have the following technical effects: This invention constructs a comprehensive function that, based on the characteristics of frequency scale, second wavelet basis function, and threshold, comprehensively considers the concentration of energy distribution, the computational complexity of the second wavelet basis function at the frequency scale, the orthogonality between second wavelet basis functions at the frequency scale, the sparsity of second wavelet coefficients at the frequency scale, and the signal-to-noise ratio of the reconstructed time-domain signal after thresholding. This allows for the selection of optimized frequency scale, optimized second wavelet basis function, and optimized threshold. This innovative method overcomes the limitations of existing technologies that rely on single or optimized algorithms, fixed parameter settings, or predefined rules, significantly improving the accuracy and robustness of low-frequency vibration signal processing. First, through multi-dimensional comprehensive optimization, this invention can more comprehensively capture the multi-scale features of the signal, ensuring optimal denoising performance in different application scenarios. Second, the highly adaptive optimization method allows it to flexibly handle signals of different types and complexities, especially in complex environments such as port equipment, where it maintains stable denoising performance under the influence of various external factors such as temperature, humidity, salt spray, and wind and waves. Furthermore, the flexible selection of wavelet basis functions and dynamically adjusted frequency division enable this method to adapt to different types of low-frequency vibration signals, whether high-frequency, low-frequency, or non-stationary, finding the most suitable processing solution. Moreover, the search for the global optimum ensures optimal denoising throughout the entire signal processing process, rather than just local optimization. Finally, by introducing computational complexity as one of the optimization objectives, this invention can reasonably control the consumption of computational resources while maintaining denoising performance, making it suitable for real-time processing and large-scale data processing scenarios. In summary, this invention not only improves the processing accuracy and robustness of low-frequency vibration signals but also possesses stronger adaptability and flexibility, enabling efficient and stable signal denoising in complex environments, providing strong technical support for health monitoring and fault prediction of port equipment. This invention significantly improves the accuracy and robustness of low-frequency vibration signal processing through a systematic multi-stage optimization strategy. First, the invention uses the initial frequency scale with the highest concentration as the initial optimization frequency scale. This selection, based on the concentration of energy distribution, ensures that the initially chosen frequency scale best captures the main components of the signal, thus providing a solid foundation for subsequent optimization. Next, at the initial optimization frequency scale, the computational complexity, orthogonality, sparsity, and signal-to-noise ratio of the reconstructed time-domain signal after thresholding of the second wavelet basis function are calculated. By constructing a synthesis function and combining these multi-dimensional parameters, the second wavelet basis function corresponding to the maximum synthesis function value is selected as the optimized second wavelet basis function, and the threshold corresponding to the maximum synthesis function value is selected as the optimization threshold. This optimization method based on the synthesis function not only considers the denoising effect but also balances computational complexity and orthogonality, ensuring the efficiency and stability of the algorithm in different application scenarios. Subsequently, based on the optimized second wavelet basis function and the optimized threshold, the present invention recalculates the comprehensive function value at each initial frequency scale, and finally selects the initial frequency scale with the largest comprehensive function value as the optimized frequency scale. This process, through global optimization, ensures that the best denoising effect is obtained throughout the entire signal processing process, rather than just a local optimum. This method avoids the limitations of existing technologies that rely on single or fixed parameter settings, and can flexibly cope with different types and complexities of signals in complex environments. In particular, it can maintain stable denoising performance even when port equipment is affected by various external factors such as temperature, humidity, salt spray, wind, and waves. Attached Figure Description To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort. Figure 1 is a flowchart of a port equipment vibration signal processing method provided by an embodiment of the present invention. Detailed Implementation To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. Figure 1 is a flowchart of a vibration signal processing method for port equipment provided by an embodiment of the present invention. Referring to Figure 1, the method specifically includes: Step 1: Acquire raw signals using sensors. The sensor is preferably a magnetoelectric vibration sensor for low-frequency vibration measurement, such as the LDT05-06C magnetoelectric vibration velocity sensor. This sensor has the following characteristics: Frequency range: 0.5 Hz to 500 Hz, covering the low-frequency vibration range commonly found in port equipment. Sensitivity: 40 mV / (mm / s), ensuring sufficient output voltage even at low frequencies. Operating temperature range: -40 ℃ to +125 ℃, adaptable to temperature variations in port environments. Protection rating: IP67, with excellent waterproof and dustproof performance, suitable for the complex environment of ports. Anti-interference capability: Built-in electromagnetic shielding reduces the impact of external electromagnetic interference on the signal. Sensor installation location: Select key locations for sensor installation based on the specific structure of the port equipment. For example, install sensors on parts prone to low-frequency vibrations, such as the crane boom, base, and drive shaft. Ensure the sensor is in close contact with the object being measured to avoid signal distortion due to looseness. Step 2: Preprocess the original signal. Preprocessing includes: DC component removal: Use a high-pass filter (e.g., with a cutoff frequency of 0.1 Hz) to remove the DC component from the signal. Ensure the accuracy of subsequent processing. Normalization: Normalizes the signal amplitude to the interval [-1, 1] to facilitate subsequent wavelet transform and other signal processing operations. Remove high-frequency noise: Use a low-pass filter (e.g., with a cutoff frequency of 500 Hz) to remove high-frequency noise from the signal while retaining low-frequency vibration components. The preprocessed original signal is divided into initial frequency scales to obtain sub-band signals of different frequencies. Several sampling points are set for the sub-band signals of different initial frequency scales. The first wavelet transform is performed on each sampling point using the first wavelet basis function (Db4 wavelet) to obtain the first wavelet coefficients of each sampling point under different initial frequency scales. Based on the first wavelet coefficients of each sampling point under different initial frequency scales, the energy distribution under each initial frequency scale is calculated, and the concentration is obtained based on the energy distribution under each initial frequency scale. The initial frequency scale is ,... ,in Lowest frequency scale These are virtual high frequencies. Each frequency scale corresponds to a sub-band signal. , where a represents the frequency scale and b represents the translation parameter. First wavelet coefficients ; Let x represent the first wavelet coefficient at the q-th sampling point under the i-th initial frequency scale d, and let x represent the original signal. This represents the first wavelet basis function at the i-th initial frequency scale d. The energy distribution at each initial frequency scale is as follows: Let H represent the energy distribution at the i-th initial frequency scale d, where H is the total number of initial frequency scales. Concentration refers to the degree to which the energy distribution of a signal is concentrated at a certain scale. Generally, the higher the energy concentration, the more concentrated the main energy of the signal is on fewer wavelet coefficients, which is helpful for subsequent denoising and feature extraction. The concentration of energy distribution at the i-th initial frequency scale d The variance of the energy distribution at the i-th initial frequency scale d This represents the mean of the energy distribution at the i-th initial frequency scale d. The concentration derived from variance and mean can effectively reflect the distribution of signal energy, even in areas with relatively high noise levels. Even under large-scale conditions, it can accurately identify the main components of the signal. Compared to other complex energy distribution indices, variance-based energy concentration calculation is simple and easy to implement. It is applicable to multi-scale wavelet transforms, enabling the evaluation of signal energy concentration at different scales and helping to select the optimal optimization scale. Step 3: Remove background noise from the sub-band signal at each initial frequency scale using a filter to obtain a denoised signal, and record the filter length at each initial frequency scale; obtain the second wavelet basis function, select the second wavelet basis function to perform a second wavelet transform on each denoised signal at each initial frequency scale to obtain the second wavelet coefficients, calculate the orthogonality value of the current second wavelet basis function with other second wavelet basis functions at each initial frequency scale, and calculate the computational complexity of each second wavelet basis function at each initial frequency scale; obtain a threshold, and perform threshold processing on the second wavelet coefficients; input the thresholded second wavelet coefficients into the inverse wavelet transform to reconstruct the time-domain signal; calculate the signal-to-noise ratio based on the reconstructed time-domain signal and the original signal; obtain the sparsity value based on the second wavelet coefficients at each initial frequency scale. The calculation process for the orthogonality value and computational complexity is the same as that in step 47, and will not be described in detail in this embodiment. The signal-to-noise ratio is calculated using existing technology. The sparsity value is calculated by the ratio of the second wavelet coefficient at each initial frequency scale to the maximum second wavelet coefficient at each initial frequency scale. In this embodiment, the calculation of sparsity values ​​is mainly used to evaluate the sparsity of the second wavelet coefficients after thresholding. Higher sparsity indicates that after thresholding, the main energy of the signal is concentrated on a few non-zero coefficients, which helps remove noise and preserve key signal features. By introducing sparsity values ​​as part of the comprehensive optimization function, this invention can ensure better preservation of the sparse structure of the signal while maintaining denoising effectiveness, thereby improving the quality of the reconstructed time-domain signal. The calculation of sparsity values ​​further optimizes the processing flow of low-frequency vibration signals. Specifically, the introduction of sparsity values ​​enables the algorithm to better preserve the sparse structure of the signal, reduce the impact of noise on the signal, and ensure that the reconstructed time-domain signal has higher clarity and reliability. Combining energy concentration, computational complexity, orthogonality, and signal-to-noise ratio, this invention constructs a multi-dimensional comprehensive optimization function, achieving globally optimal frequency scale, second wavelet basis function, and threshold selection, significantly improving the accuracy and robustness of signal processing. Step 4: Construct a synthesis function by considering sparsity, signal-to-noise ratio, concentration, computational complexity, and orthogonality. Based on the synthesis function, select an optimized frequency scale from the initial frequency scale, an optimized second wavelet basis function from the second wavelet basis function, and an optimized threshold from the threshold. This embodiment incorporates several innovative improvements based on signal characteristics, significantly enhancing the accuracy, robustness, and computational efficiency of low-frequency vibration signal processing. First, regarding the selection of the frequency scale, this invention introduces the concentration of energy distribution as a key evaluation index. By calculating the energy concentration at each initial frequency scale, the scale with the highest concentration is selected as the initial optimized frequency scale. This improvement ensures that the selected scale can most effectively capture the main components of the signal, especially in the low-frequency vibration signals commonly found in port equipment, enabling more accurate identification of key vibration features. Second, regarding the selection of the second wavelet basis function, this invention not only considers its computational complexity at a specific frequency scale but also introduces orthogonality as an evaluation criterion. The introduction of computational complexity allows the algorithm to reasonably control computational resource consumption while ensuring denoising effectiveness, making it suitable for real-time processing and large-scale data processing scenarios; while the introduction of orthogonality ensures the independence between different wavelet basis functions, avoiding the introduction of redundant information and further improving the accuracy of signal processing. Third, for the selection of the threshold, this invention comprehensively considers two key parameters: sparsity and signal-to-noise ratio. The introduction of sparsity values ​​allows the algorithm to better preserve the sparse structure of the signal and reduce the impact of noise. The introduction of signal-to-noise ratio (SNR) ensures that the reconstructed time-domain signal after thresholding has higher clarity and reliability. Finally, by constructing a comprehensive function, the parameters of the above multiple dimensions (energy concentration, computational complexity, orthogonality, sparsity, and SNR) are organically combined to achieve multi-objective global optimization. This comprehensive optimization strategy not only overcomes the limitations of existing technologies that rely on single or fixed parameter settings, but also flexibly handles signals of different types and complexities in complex environments, ensuring optimal denoising effects in various application scenarios. In summary, this embodiment significantly improves the accuracy, robustness, and computational efficiency of low-frequency vibration signal processing through multi-dimensional comprehensive optimization, providing strong technical support for health monitoring and fault prediction of port equipment. The process of selecting the optimal frequency scale, the optimal second wavelet basis function, and the optimal threshold through the synthesis function is as follows: Step 4-1: Calculate the concentration of subband signal energy distribution at each initial frequency scale; take the initial frequency scale with the highest concentration as the initial optimized frequency scale. Step 4-2: Preset candidate second wavelet basis functions and candidate thresholds. Step 4-3: Select candidate second wavelet basis functions and perform second wavelet transform on each denoised signal at each initial optimized frequency scale to obtain candidate second wavelet coefficients. Step 4-4: Calculate the orthogonality value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimized frequency scale. Steps 4-5: Calculate the computational complexity of each candidate wavelet basis function at each initial optimized frequency scale. Steps 4-6: Threshold each candidate second wavelet coefficient using a candidate threshold; input the thresholded candidate second wavelet coefficients into the inverse wavelet transform to reconstruct the candidate time-domain signal; calculate the candidate signal-to-noise ratio based on the reconstructed candidate time-domain signal and the original signal; obtain the candidate sparse value based on the candidate second wavelet coefficients at each initial optimized frequency scale. Steps 4-7: Based on the orthogonality between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimization frequency scale, the computational complexity of each candidate second wavelet basis function at each initial optimization frequency scale, the candidate sparse values ​​obtained from the candidate second wavelet coefficients at each initial optimization frequency scale, the candidate signal-to-noise ratio calculated between the reconstructed candidate time-domain signal and the original signal, and the concentration of sub-band signal energy distribution at each initial optimization frequency scale, calculate the comprehensive function value, use the candidate second wavelet basis function with the largest comprehensive function value as the optimized second wavelet basis function, and use the candidate threshold with the largest comprehensive function value as the optimization threshold. Calculate the correlation between each candidate second wavelet basis function and other candidate second wavelet basis functions at the initial optimization frequency scale to obtain the correlation matrix; calculate the maximum and minimum singular values ​​of the correlation matrix, and obtain the condition number of the correlation matrix based on the maximum and minimum singular values, and use the condition number as the orthogonality value; construct an evaluation threshold for the orthogonality value, and divide the orthogonality value into reward terms and penalty terms according to the evaluation threshold. In the formula, The orthogonality value of the correlation matrix A between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c is... This represents the maximum singular value of the correlation matrix between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c. Z represents the minimum singular value of the correlation matrix between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c, and Z represents the total number of candidate second wavelet basis functions at the initial optimized frequency scale c. Obtain the signal length of the denoised signal at the initial optimized frequency scale and the filter length at the initial optimized frequency scale. Based on the signal length and filter length, calculate the computational complexity of each candidate second wavelet basis function. In the formula, Let represent the computational complexity of the g-th candidate second wavelet basis function under the initial optimized frequency scale c. The signal length representing the initial optimized frequency scale c N represents the filter length at the initial optimized frequency scale c, and N is the length of the original signal. This represents the candidate second wavelet basis function. In the formula This represents a comprehensive consideration of the initial frequency scale d and the second wavelet basis function. Threshold Synthesis function The concentration of energy distribution at the i-th initial frequency scale d Represents the transformation function. The orthogonal value representing the j-th second wavelet basis function The computational complexity of the j-th second wavelet basis function This represents the signal-to-noise ratio (SNR) between the reconstructed time-domain signal after the k-th threshold processing and the original signal. This represents the sparse values ​​of the optimized second wavelet coefficients after processing with the k-th threshold. These represent adjustment coefficients, . Furthermore, In the formula, The transformation value represents the orthogonality value of the j-th second wavelet basis function, z represents a positive number, z>2, and Y represents the evaluation threshold. The transformation value represents the orthogonality value of the j-th second wavelet basis function, z represents a positive number, z>2, and Y represents the evaluation threshold. Representative award items, This represents a penalty. In signal processing, orthogonality is a key metric that measures the degree of independence and non-interference among different wavelet basis functions. A higher orthogonality value indicates lower correlation between wavelet basis functions, which helps improve the accuracy and robustness of signal processing. When constructing the synthesis function, the orthogonality value is considered in two ways: a reward term and a penalty term. when The reward items are ,along with As the value increases, the reward term gradually decreases, but remains greater than 0. This indicates that wavelet basis functions with higher orthogonality receive higher rewards, thus gaining higher weights in the synthesis function. when The penalty item is ,along with As the value decreases, the penalty term gradually increases, but it remains less than 0. This indicates that wavelet basis functions with lower orthogonality will receive lower weights, or may even be excluded. Step 48: Based on the optimized second wavelet basis function and the optimized threshold, recalculate the comprehensive function value at each initial frequency scale, and use the initial frequency scale with the maximum comprehensive function value as the optimized frequency scale. The optimized second wavelet basis function better matches the time-frequency characteristics of the signal, while the optimized threshold preserves key signal features while removing noise. Based on this, the comprehensive function value at each initial frequency scale is recalculated. By incorporating parameters across multiple dimensions, including energy concentration, computational complexity, orthogonality, sparsity, and signal-to-noise ratio, the evaluation at each frequency scale is ensured to be more comprehensive and accurate. Finally, the initial frequency scale with the largest comprehensive function value is selected as the optimization frequency scale. This not only guarantees the global optimality of signal processing but also allows for flexible handling of signals of different types and complexities in complex environments, ensuring optimal denoising results across various application scenarios. Step 5: Determine the optimized sub-band signal based on the optimized frequency scale, denoise the optimized sub-band signal using a filter to obtain the optimized denoised signal, perform a second wavelet transform on the optimized denoised signal using the optimized second wavelet basis function to obtain the optimized second wavelet coefficients, and perform threshold processing on the second wavelet coefficients according to the optimized threshold to obtain the optimized time domain signal.

[0093] The processing steps in this step are the same as those in existing technologies, except that the optimization of frequency scale, noise reduction signal, and threshold are selected. This processing method significantly improves the efficiency and reliability of low-frequency vibration signal processing, completely eliminating the need for repeated verification after obtaining the time-domain signal in existing technologies. Traditional methods often rely on fixed parameter settings or empirical values ​​to determine the frequency scale, filter type, wavelet basis function, and threshold. This necessitates multiple trials and verifications when processing different types of signals to ensure the accuracy of the final result. This application, however, uses a global optimization strategy to dynamically adjust these key parameters during processing, ensuring that each step is based on the optimal solution. Specifically, optimizing the selection of the frequency scale allows for more accurate capture of the main components of the signal, avoiding information loss due to improper frequency selection; optimizing the denoising signal effectively removes noise through an adaptive filter while preserving the key features of the signal; and optimizing the threshold ensures that noise is effectively suppressed without over-smoothing the signal in the coefficient processing after the second wavelet transform. Therefore, the entire processing is not only more efficient but also yields a high-quality optimized time-domain signal in a single step, eliminating the need for repeated verification and significantly saving time and computational resources. It should be noted that the terminology used in this invention is for describing specific embodiments only and is not intended to limit the scope of this application. For example... As illustrated in this specification, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include the plural. The terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, 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 a process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, or apparatus that includes said element. It should also be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Unless otherwise expressly specified and limited, the terms "installed," "connected," "linked," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components. For those skilled in the art, the specific meaning of the above terms in the present invention can be understood according to the specific circumstances. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method of processing a vibration signal of a port facility, characterized by, include: Step 1: Acquire raw signals using sensors; Step 2: Preprocess the original signal; The preprocessed original signal is divided into initial frequency scales to obtain sub-band signals of different frequencies. Several sampling points are set on the sub-band signals of different initial frequency scales. The first wavelet transform is performed on each sampling point using the first wavelet basis function to obtain the first wavelet coefficient of each sampling point under different initial frequency scales. Based on the first wavelet coefficient of each sampling point under different initial frequency scales, the energy distribution under each initial frequency scale is calculated, and the concentration is obtained based on the energy distribution under each initial frequency scale. Step 3: Remove background noise from the sub-band signal at each initial frequency scale using a filter to obtain a denoised signal, and record the filter length at each initial frequency scale; obtain the second wavelet basis function, select the second wavelet basis function to perform the second wavelet transform on each denoised signal at each initial frequency scale to obtain the second wavelet coefficients, calculate the orthogonality value of the current second wavelet basis function with other second wavelet basis functions at each initial frequency scale, and calculate the computational complexity of each second wavelet basis function at each initial frequency scale; Obtain the threshold, and then perform threshold processing on the second wavelet coefficients using the threshold. The thresholded second wavelet coefficients are input into the inverse wavelet transform to reconstruct the time-domain signal; the signal-to-noise ratio is calculated based on the reconstructed time-domain signal and the original signal; and sparse values ​​are obtained based on the second wavelet coefficients at each initial frequency scale. The sparse value is obtained by calculating the ratio of the second wavelet coefficient at each initial frequency scale to the maximum second wavelet coefficient at that scale. Step 4: Construct a comprehensive function using sparsity, signal-to-noise ratio, concentration, computational complexity, and orthogonality. Based on the comprehensive function, select an optimized frequency scale from the initial frequency scale, an optimized second wavelet basis function from the second wavelet basis function, and an optimized threshold from the threshold. Step 5: Determine the optimized sub-band signal based on the optimized frequency scale, denoise the optimized sub-band signal using a filter to obtain the optimized denoised signal, perform a second wavelet transform on the optimized denoised signal using the optimized second wavelet basis function to obtain the optimized second wavelet coefficients, and perform threshold processing on the second wavelet coefficients according to the optimized threshold to obtain the optimized time domain signal.

2. The method of claim 1, wherein, The process of selecting an optimized frequency scale from the initial frequency scale, an optimized second wavelet basis function from the second wavelet basis function, and an optimized threshold from the threshold, based on the synthesis function, includes the following steps: Step 4-1: Calculate the concentration of sub-band signal energy distribution at each initial frequency scale; take the initial frequency scale with the highest concentration as the initial optimized frequency scale. Step 4-2: Preset candidate second wavelet basis functions and candidate thresholds; Step 4-3: Select candidate second wavelet basis functions and perform second wavelet transform on each denoised signal at each initial optimized frequency scale to obtain candidate second wavelet coefficients; Step 4-4: Calculate the orthogonality value between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimization frequency scale; Steps 4-5: Calculate the computational complexity of each candidate wavelet basis function at each initial optimized frequency scale; Steps 4-6: Threshold each candidate second wavelet coefficient using a candidate threshold; input the thresholded candidate second wavelet coefficients into the inverse wavelet transform to reconstruct the candidate time-domain signal; calculate the candidate signal-to-noise ratio based on the reconstructed candidate time-domain signal and the original signal; obtain the candidate sparse value based on the candidate second wavelet coefficients at each initial optimized frequency scale. Steps 4-7: Based on the orthogonality between the current candidate wavelet basis function and other candidate wavelet basis functions at each initial optimization frequency scale, the computational complexity of each candidate second wavelet basis function at each initial optimization frequency scale, the candidate second wavelet coefficients at each initial optimization frequency scale to obtain candidate sparsity values, the candidate signal-to-noise ratio of the reconstructed candidate time-domain signal and the original signal, and the concentration of sub-band signal energy distribution at each initial optimization frequency scale, calculate the comprehensive function value, take the candidate second wavelet basis function with the largest comprehensive function value as the optimized second wavelet basis function, and take the candidate threshold with the largest comprehensive function value as the optimization threshold. Steps 4-8: Based on the optimized second wavelet basis function and the optimized threshold, recalculate the comprehensive function value at each initial frequency scale, and use the initial frequency scale with the maximum comprehensive function value as the optimized frequency scale.

3. The method for processing vibration signals of a port equipment according to claim 2, characterized in that, The process of calculating the orthogonality value of each candidate wavelet function at the initial optimization frequency scale is as follows: calculate the correlation between each candidate second wavelet basis function and other candidate second wavelet basis functions at the initial optimization frequency scale to obtain the correlation matrix; Calculate the maximum and minimum singular values ​​of the correlation matrix, obtain the condition number of the correlation matrix based on the maximum and minimum singular values, and use the condition number as the orthogonality value; An evaluation threshold is constructed for the orthogonal values, and the orthogonal values ​​are divided into reward items and penalty items according to the evaluation threshold.

4. The method of claim 3, wherein, The computational complexity of each candidate second wavelet basis function at the initial optimized frequency scale includes: obtaining the signal length of the denoised signal at the initial optimized frequency scale and the filter length at the initial optimized frequency scale; and calculating the computational complexity of each candidate second wavelet basis function based on the signal length and the filter length.

5. The method of claim 4, wherein, The computational complexity of each candidate wavelet basis function at each initial optimization frequency scale is: ； In the formula Let represent the computational complexity of the g-th candidate second wavelet basis function under the initial optimized frequency scale c. The signal length representing the initial optimized frequency scale c N represents the filter length at the initial optimized frequency scale c, and N is the length of the original signal.

6. The port equipment vibration signal processing method according to claim 3, characterized in that, The orthogonality value of each candidate wavelet function at the initial optimization frequency scale is: ； In the formula The orthogonality value of the correlation matrix A between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c is... This represents the maximum singular value of the correlation matrix between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c. Z represents the minimum singular value of the correlation matrix between the h-th and o-th candidate second wavelet basis functions at the initial optimized frequency scale c, and Z represents the total number of candidate second wavelet basis functions at the initial optimized frequency scale c.

7. The port equipment vibration signal processing method according to claim 1, characterized in that, The synthesis function is: ； In the formula, This represents a comprehensive consideration of the initial frequency scale d and the second wavelet basis function. Threshold Synthesis function The concentration of energy distribution at the i-th initial frequency scale d Representative transformation function The orthogonal value representing the j-th second wavelet basis function The computational complexity of the j-th second wavelet basis function This represents the signal-to-noise ratio (SNR) between the reconstructed time-domain signal after the k-th threshold processing and the original signal. This represents the sparse values ​​of the optimized second wavelet coefficients after processing with the k-th threshold. They represent adjustment coefficients respectively. 。 8. A method for processing vibration signals of port equipment according to claim 7, characterized in that, The conversion function is: ； In the formula, The transformation value represents the orthogonality value of the j-th second wavelet basis function, z represents a positive number, and z represents the evaluation threshold. Representative award items, This represents a penalty.

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